From a0cc189c1312b3a7e1f3ae4596b137e9a3768374 Mon Sep 17 00:00:00 2001 From: Sara JC Gosline Date: Wed, 11 Feb 2026 09:43:59 -0800 Subject: [PATCH 1/3] test for gh pages --- README.md | 5 +- docs/404.html | 114 ++ docs/LICENSE-text.html | 116 ++ docs/articles/examples.html | 518 ++++++++ .../examples_files/figure-html/figure_2-1.png | Bin 0 -> 40093 bytes .../examples_files/figure-html/figure_3-1.png | Bin 0 -> 23985 bytes .../examples_files/figure-html/figure_3-2.png | Bin 0 -> 22159 bytes docs/articles/index.html | 90 ++ docs/articles/leapR.html | 869 +++++++++++++ .../leapR_files/figure-html/corr_plot-1.png | Bin 0 -> 57942 bytes .../figure-html/fishers_plot-1.png | Bin 0 -> 54834 bytes .../leapR_files/figure-html/ks_plot-1.png | Bin 0 -> 64650 bytes .../leapR_files/figure-html/phos_plot-1.png | Bin 0 -> 56209 bytes .../leapR_files/figure-html/phos_plot-2.png | Bin 0 -> 57091 bytes .../articles/leapR_files/figure-html/zt-1.png | Bin 0 -> 61272 bytes .../pagedtable-1.1/css/pagedtable.css | 142 ++ .../pagedtable-1.1/js/pagedtable.js | 1151 +++++++++++++++++ docs/authors.html | 123 ++ docs/bootstrap-toc.css | 60 + docs/bootstrap-toc.js | 159 +++ docs/docsearch.css | 148 +++ docs/docsearch.js | 85 ++ docs/index.html | 214 +++ docs/link.svg | 12 + docs/pkgdown.css | 384 ++++++ docs/pkgdown.js | 108 ++ docs/pkgdown.yml | 8 + docs/reference/calcTTest.html | 148 +++ docs/reference/cluster_enrichment.html | 166 +++ docs/reference/combine_omics.html | 156 +++ .../correlation_comparison_enrichment.html | 136 ++ docs/reference/correlation_enrichment.html | 119 ++ docs/reference/enrichment_in_abundance.html | 160 +++ docs/reference/enrichment_in_groups.html | 160 +++ .../enrichment_in_relationships.html | 129 ++ docs/reference/get_pathway_information.html | 128 ++ docs/reference/index.html | 155 +++ docs/reference/kinasesubstrates.html | 113 ++ docs/reference/krbpaths.html | 113 ++ docs/reference/leapR-package.html | 97 ++ docs/reference/leapR.html | 340 +++++ docs/reference/longlist.html | 97 ++ docs/reference/ncipid.html | 113 ++ docs/reference/plot_leapr_bar.html | 159 +++ docs/reference/read_gene_sets.html | 134 ++ docs/reference/shortlist.html | 98 ++ vignettes/order-enrichment.Rmd | 126 ++ 47 files changed, 7149 insertions(+), 4 deletions(-) create mode 100644 docs/404.html create mode 100644 docs/LICENSE-text.html create mode 100644 docs/articles/examples.html create mode 100644 docs/articles/examples_files/figure-html/figure_2-1.png create mode 100644 docs/articles/examples_files/figure-html/figure_3-1.png create mode 100644 docs/articles/examples_files/figure-html/figure_3-2.png create mode 100644 docs/articles/index.html create mode 100644 docs/articles/leapR.html create mode 100644 docs/articles/leapR_files/figure-html/corr_plot-1.png create mode 100644 docs/articles/leapR_files/figure-html/fishers_plot-1.png create mode 100644 docs/articles/leapR_files/figure-html/ks_plot-1.png create mode 100644 docs/articles/leapR_files/figure-html/phos_plot-1.png create mode 100644 docs/articles/leapR_files/figure-html/phos_plot-2.png create mode 100644 docs/articles/leapR_files/figure-html/zt-1.png create mode 100644 docs/articles/leapR_files/pagedtable-1.1/css/pagedtable.css create mode 100644 docs/articles/leapR_files/pagedtable-1.1/js/pagedtable.js create mode 100644 docs/authors.html create mode 100644 docs/bootstrap-toc.css create mode 100644 docs/bootstrap-toc.js create mode 100644 docs/docsearch.css create mode 100644 docs/docsearch.js create mode 100644 docs/index.html create mode 100644 docs/link.svg create mode 100644 docs/pkgdown.css create mode 100644 docs/pkgdown.js create mode 100644 docs/pkgdown.yml create mode 100644 docs/reference/calcTTest.html create mode 100644 docs/reference/cluster_enrichment.html create mode 100644 docs/reference/combine_omics.html create mode 100644 docs/reference/correlation_comparison_enrichment.html create mode 100644 docs/reference/correlation_enrichment.html create mode 100644 docs/reference/enrichment_in_abundance.html create mode 100644 docs/reference/enrichment_in_groups.html create mode 100644 docs/reference/enrichment_in_relationships.html create mode 100644 docs/reference/get_pathway_information.html create mode 100644 docs/reference/index.html create mode 100644 docs/reference/kinasesubstrates.html create mode 100644 docs/reference/krbpaths.html create mode 100644 docs/reference/leapR-package.html create mode 100644 docs/reference/leapR.html create mode 100644 docs/reference/longlist.html create mode 100644 docs/reference/ncipid.html create mode 100644 docs/reference/plot_leapr_bar.html create mode 100644 docs/reference/read_gene_sets.html create mode 100644 docs/reference/shortlist.html create mode 100644 vignettes/order-enrichment.Rmd diff --git a/README.md b/README.md index 75b98b4..c2973f6 100644 --- a/README.md +++ b/README.md @@ -5,14 +5,11 @@ Layered Enrichment Analysis of Pathways in R (leapR) a tool that carries out sta ## Install leapR is available through Bioconductor repository here -```{r install} - +``` if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(version = "3.21") - BiocManager::install('BiocStyle') - BiocManager::install('leapR') ``` diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 0000000..52be5b5 --- /dev/null +++ b/docs/404.html @@ -0,0 +1,114 @@ + + + + + + + +Page not found (404) • leapR + + + + + + + + + + + +
+
+ + + + +
+
+
+

Page not found (404)

+
+ +Content not found. Please use links in the navbar. + +
+ + + +
+ + + +
+

+

Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

+
+ +
+

+

Site built with pkgdown 2.2.0.

+
+ +
+
+ + + + + + + + diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html new file mode 100644 index 0000000..7599ea4 --- /dev/null +++ b/docs/LICENSE-text.html @@ -0,0 +1,116 @@ + +License • leapR + + +
+
+ + + +
+
+
+

License

+
+ +
Copyright 2025 Battelle Memorial Institute
+
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+
+1. Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+
+2. Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
+ +
+ + + +
+ + + +
+

Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

+
+ +
+

Site built with pkgdown 2.2.0.

+
+ +
+ + + + + + + + diff --git a/docs/articles/examples.html b/docs/articles/examples.html new file mode 100644 index 0000000..54af499 --- /dev/null +++ b/docs/articles/examples.html @@ -0,0 +1,518 @@ + + + + + + + +leapR Paper Examples • leapR + + + + + + + + + + + +
+
+ + + + +
+
+
+

leapR Paper Examples

+ + + + + +
+ + + +
+

Load libraries needed +

+
+# load the core libraries
+library(leapR)
+library(gplots)
+library(rmarkdown)
+# plotting helpers used in this vignette
+library(ggplot2)
+library(dplyr)
+library(tibble)
+library(stringr)
+
+
+

Example data +

+

A sample data set is included that is from the CPTAC study of 169 +ovarian tumors. We include the dataset as a object, containing three +assays (transcriptomics, global proteomics, and phosphoproteomics) to +enable interoperability with other tools, and store example file as +rda on Figshare +as example.

+

This data can be loaded as follows:

+
+url <- "https://api.figshare.com/v2/file/download/56536217"
+pdata <- download.file(url, method = "libcurl", destfile = "protData.rda")
+#  as.matrix()
+load("protData.rda")
+
+p <- file.remove("protData.rda")
+
+url <- "https://api.figshare.com/v2/file/download/56536214"
+tdata <- download.file(url, method = "libcurl", destfile = "transData.rda")
+load("transData.rda")
+p <- file.remove("transData.rda")
+
+url <- "https://api.figshare.com/v2/file/download/56536211"
+phdata <- download.file(url, method = "libcurl", destfile = "phosData.rda")
+load("phosData.rda")
+p <- file.remove("phosData.rda")
+

We also have local data we can load

+
+
+data(shortlist)
+data(longlist)
+data(ncipid)
+data(kinasesubstrates)
+## columns that we want to use for results
+
+cols_to_display <- c("ingroup_n", "outgroup_n", "background_n", 
+                     "pvalue", "BH_pvalue")
+
+
+

Figure 2 +

+

We will compare the ability of transcriptomics, proteomics, and +phosphoproteomics to inform about differences between short and long +surviving patient groups. In addition to other methods, we also employ a +calcTTest function that takes two sets of samples from the +SummarizedExperiment object and computes the t-test between +them. The results are then stored in the rowData of the +same object, so that they can be used for enrichment later on.

+

This spans the multiple enrichment methods in leapR and also includes +multi-omics.

+

The resulting heatmap is presented as Figure 2 in the paper.

+
+# load the single omic and multi-omic pathway databases
+data("krbpaths")
+data("mo_krbpaths")
+
+# comparison enrichment in transcriptional data
+transdata.comp.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_comparison",
+  eset = tset,
+  assay_name = "transcriptomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist
+)
+
+# comparison enrichment in proteomics data
+# this is the same code used above, just repeated here for clarity
+protdata.comp.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_comparison",
+  eset = pset,
+  assay_name = "proteomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist
+)
+
+# comparison enrichment in phosphoproteomics data
+phosphodata.comp.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_comparison",
+  eset = phset,
+  assay_name = "phosphoproteomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist, id_column = "hgnc_id"
+)
+
+
+# set enrichment in transcriptomics data
+# perform the comparison t-test
+tset <- leapR::calcTTest(tset, assay_name = "transcriptomics", 
+                         shortlist, longlist)
+
+
+## now we need to run enrichment in sets with target list, not eset
+transdata.set.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  eset = tset,
+  assay_name = "transcriptomics",
+  enrichment_method = "enrichment_in_sets",
+  primary_columns = "pvalue",
+  greaterthan = FALSE, threshold = 0.05
+)
+
+
+pset <- leapR::calcTTest(pset, assay_name = "proteomics",
+                         shortlist, longlist)
+
+protdata.set.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  eset = pset,
+  assay_name = "proteomics",
+  enrichment_method = "enrichment_in_sets",
+  primary_columns = "pvalue",
+  greaterthan = FALSE, threshold = 0.05
+)
+
+
+phset <- leapR::calcTTest(phset, assay_name = "phosphoproteomics", 
+                          shortlist, longlist)
+
+phosphodata.set.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_in_sets",
+  id_column = "hgnc_id",
+  assay_name = "phosphoproteomics",
+  eset = phset, primary_columns = "pvalue",
+  greaterthan = FALSE, threshold = 0.05
+)
+
+# order enrichment in transcriptomics data
+transdata.order.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_in_order",
+  eset = tset,
+  assay_name = "transcriptomics",
+  primary_columns = "difference"
+)
+
+# order enrichment in proteomics data
+protdata.order.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_in_order",
+  eset = pset,
+  assay_name = "proteomics",
+  primary_columns = "difference"
+)
+
+# order enrichment in phosphoproteomics data
+
+
+phosphodata.order.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_in_order",
+  id_column = "hgnc_id",
+  method = 'ztest', 
+  eset = phset,
+  assay_name = "phosphoproteomics",
+  primary_columns = "difference"
+)
+
+# correlation difference in transcriptomics data
+transdata.corr.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "correlation_comparison",
+  eset = tset,
+  assay_name = "transcriptomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist
+)
+# correlation difference in proteomics data
+protdata.corr.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "correlation_comparison",
+  eset = pset,
+  assay_name = "proteomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist
+)
+# correlation difference in phosphoproteomics data
+phosphodata.corr.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "correlation_comparison",
+  eset = phset,
+  assay_name = "phosphoproteomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist, id_column = "hgnc_id"
+)
+
+# combine the omics data into one with prefix tags
+comboset <- leapR::combine_omics(list(pset, phset, tset), 
+                                 c(NA, "hgnc_id", NA))
+
+# comparison enrichment for combodata
+# when we use expression set, we do not need to use the mo_krbpaths 
+#since the  id mapping column is used
+combodata.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths, # mo_krbpaths,
+  enrichment_method = "enrichment_comparison",
+  eset = comboset,
+  assay_name = "combined",
+  primary_columns = shortlist,
+  secondary_columns = longlist, id_column = "id"
+)
+
+
+# set enrichment in combo data
+# perform the comparison t test
+comboset <- leapR::calcTTest(comboset,
+                             assay_name = "combined", 
+                             shortlist, longlist)
+
+combodata.set.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_in_sets",
+  eset = comboset, primary_columns = "pvalue",
+  assay_name = "combined",
+  id_column = "id",
+  greaterthan = FALSE, threshold = 0.05
+)
+
+# order enrichment in combo data
+combodata.order.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "enrichment_in_order",
+  assay_name = "combined",
+  eset = comboset, primary_columns = "difference",
+  id_column = "id"
+)
+
+# correlation difference in combo data
+combodata.corr.enrichment.svl <- leapR::leapR(
+  geneset = krbpaths,
+  enrichment_method = "correlation_comparison",
+  eset = comboset,
+  assay_name = "combined",
+  primary_columns = shortlist,
+  id_column = "id",
+  secondary_columns = longlist
+)
+
+
+# now take all these results and combine them into one figure
+all_results <- list(
+  transdata.comp.enrichment.svl,
+  protdata.comp.enrichment.svl,
+  phosphodata.comp.enrichment.svl,
+  combodata.enrichment.svl,
+  transdata.set.enrichment.svl,
+  protdata.set.enrichment.svl,
+  phosphodata.set.enrichment.svl,
+  combodata.set.enrichment.svl,
+  transdata.order.enrichment.svl,
+  protdata.order.enrichment.svl,
+  phosphodata.order.enrichment.svl,
+  combodata.order.enrichment.svl,
+  transdata.corr.enrichment.svl,
+  protdata.corr.enrichment.svl,
+  phosphodata.corr.enrichment.svl,
+  combodata.corr.enrichment.svl
+)
+
+pathways_of_interest <- c(
+  "KEGG_APOPTOSIS",
+  "KEGG_CELL_CYCLE",
+  "KEGG_ERBB_SIGNALING_PATHWAY",
+  "KEGG_FOCAL_ADHESION",
+  "KEGG_INSULIN_SIGNALING_PATHWAY",
+  "KEGG_MAPK_SIGNALING_PATHWAY",
+  "KEGG_MISMATCH_REPAIR",
+  "KEGG_MTOR_SIGNALING_PATHWAY",
+  "KEGG_OXIDATIVE_PHOSPHORYLATION",
+  "KEGG_P53_SIGNALING_PATHWAY",
+  "KEGG_PATHWAYS_IN_CANCER",
+  "KEGG_PROTEASOME",
+  "KEGG_RIBOSOME",
+  "KEGG_VEGF_SIGNALING_PATHWAY",
+  "KEGG_WNT_SIGNALING_PATHWAY"
+)
+
+
+results.frame <- data.frame(
+  pathway = pathways_of_interest,
+  td.comp = all_results[[1]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  pd.comp = all_results[[2]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  fd.comp = all_results[[3]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  cd.comp = all_results[[4]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  td.set = all_results[[5]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  pd.set = all_results[[6]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  fd.set = all_results[[7]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  cd.set = all_results[[8]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  td.order = all_results[[9]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  pd.order = all_results[[10]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  fd.order = all_results[[11]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  cd.order = all_results[[12]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  td.corr = all_results[[13]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  pd.corr = all_results[[14]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  fd.corr = all_results[[15]][pathways_of_interest, "BH_pvalue"] < 0.05,
+  cd.corr = all_results[[16]][pathways_of_interest, "BH_pvalue"] < 0.05
+)
+
+results.frame.or <- data.frame(
+  pathway = pathways_of_interest,
+  td.comp = all_results[[1]][pathways_of_interest, "oddsratio"],
+  pd.comp = all_results[[2]][pathways_of_interest, "oddsratio"],
+  fd.comp = all_results[[3]][pathways_of_interest, "oddsratio"],
+  cd.comp = all_results[[4]][pathways_of_interest, "oddsratio"],
+  td.set = log(all_results[[5]][pathways_of_interest, "oddsratio"], 2),
+  pd.set = log(all_results[[6]][pathways_of_interest, "oddsratio"], 2),
+  fd.set = log(all_results[[7]][pathways_of_interest, "oddsratio"], 2),
+  cd.set = log(all_results[[8]][pathways_of_interest, "oddsratio"], 2),
+  td.order = all_results[[9]][pathways_of_interest, "oddsratio"],
+  pd.order = all_results[[10]][pathways_of_interest, "oddsratio"],
+  fd.order = all_results[[11]][pathways_of_interest, "oddsratio"],
+  cd.order = all_results[[12]][pathways_of_interest, "oddsratio"],
+  td.corr = all_results[[13]][pathways_of_interest, "oddsratio"],
+  pd.corr = all_results[[14]][pathways_of_interest, "oddsratio"],
+  fd.corr = all_results[[15]][pathways_of_interest, "oddsratio"],
+  cd.corr = all_results[[16]][pathways_of_interest, "oddsratio"]
+)
+
+rownames(results.frame) <- results.frame[, 1]
+rownames(results.frame.or) <- results.frame.or[, 1]
+results.frame.sig <- results.frame[, 2:17] * results.frame.or[, 2:17]
+
+heatmap.2(as.matrix(results.frame.sig[, c(1:4, 9:16)]), Colv = NA, 
+          trace = "none", breaks = c(-1, -.1, -0.0001, 0, 0.1, 1), 
+          col = c("blue", "lightblue", "grey", "pink", "red"), 
+          dendrogram = "none")
+

+

Figure 3. An application of the KSEA-like approach in leapR as +applied to our example data. In this example we are looking for known +substrate sets of kinases (from Phosphosite Plus) that are enriched in +the short vs long comparison of phosphopeptides.

+
+# this comparison of abundance in substrates between case and control
+#     is lopsided in the sense that phosphorylation levels were previously
+#     reported to be overall higher in the short survivors. Thus the
+#     results are not terribly interesting (all kinases are in the same 
+# direction)
+phosphodata.ksea.comp.svl <- leapR::leapR(
+  geneset = kinasesubstrates,
+  enrichment_method = "enrichment_comparison",
+  eset = phset,
+  assay_name = "phosphoproteomics",
+  primary_columns = shortlist, secondary_columns = longlist
+)
+
+
+# thus for the example we'll look at correlation between known substrates 
+#   in the   case v control conditions
+phosphodata.ksea.corr.svl <- leapR::leapR(
+  geneset = kinasesubstrates,
+  enrichment_method = "correlation_comparison",
+  eset = phset,
+  assay_name = "phosphoproteomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist
+)
+
+# for the example we are using an UNCORRECTED PVALUE
+#     which will allow us to plot more values, but
+#     for real applications it's necessary to use the
+#     CORRECTED PVALUE
+
+# here are all the kinases *significant (*uncorrected) from the analysis
+or <- order(phosphodata.ksea.corr.svl[, "pvalue"])
+ksea_result <- phosphodata.ksea.corr.svl[or, ][1:9, ]
+ksea_cols <- rep("grey", 9)
+ksea_cols[which(ksea_result[, "oddsratio"] > 0)] <- "black"
+
+# plot left panel: correlation significance of top most significant kinases
+barplot(ksea_result[, "oddsratio"],
+  horiz = TRUE, xlim = c(-1, 0.5),
+  names.arg = rownames(ksea_result), las = 1, col = ksea_cols
+)
+

+
+
+# plot right panel: abundance comparison results of the same kinases
+barplot(phosphodata.ksea.comp.svl[rownames(ksea_result), "oddsratio"],
+  horiz = TRUE, names.arg = rownames(ksea_result), las = 1, col = "black"
+)
+

+
+
+ + + +
+ + + +
+

+

Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

+
+ +
+

+

Site built with pkgdown 2.2.0.

+
+ +
+
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zM+QFfc0y`2ra&t1vmhj@A1nFBl)nD;#L_P1=PgYpRd}~Vt6p3Wx`C-r(>$%U?sgZB z>!jYiRSe%YFl}Sv+!+d`s*abMgX9*!(j18uxtGS@{YFMa%T1R)shcFd$%Xce4D2`Y zwSPTeKg~vHTj_IGq%uOpz_e@I?9?O77ws!45sfT1@nl}JQ(C$#S(nhZL*F6MFwfys zsw_%$a;(bYhQ>jo-*lpW%Clg4rZ +Articles • leapR + + +
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+

Articles

+
+ +
+

All vignettes

+

+ +
leapR Paper Examples
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leapR
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leapR Order Enrichment Tests
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+

Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

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+

Site built with pkgdown 2.2.0.

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+ +
+ + + + + + + + diff --git a/docs/articles/leapR.html b/docs/articles/leapR.html new file mode 100644 index 0000000..768bdd2 --- /dev/null +++ b/docs/articles/leapR.html @@ -0,0 +1,869 @@ + + + + + + + +leapR • leapR + + + + + + + + + + + +
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+ + + + +
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+

leapR

+ + + + + +
+ + + +
+

Installation +

+

This is intended to be a short introduction to the leapR +package. First we need to load the required libraries:

+
+# install from bioconductor
+if (!require(BiocManager)) {
+  install.packages('BiocManager')
+  BiocManager::install('leapR')
+}
+
+
+

Load libraries needed +

+
+# load the core libraries
+library(leapR)
+library(gplots)
+library(rmarkdown)
+# plotting helpers used in this vignette
+library(ggplot2)
+library(dplyr)
+library(tibble)
+library(stringr)
+
+
+

Introduction +

+
+

Definitions +

+

Dataset - an expression dataset, contained in the +Bioconductor object, that at the bare minimum has a matrix of components +(rows) measured in the same system under multiple different conditions +(columns)

+

Component - the things being measured, genes, proteins, +methylation site, phosphosite, etc. For functional (currently) the +component must be associated with a gene name. That is, there’s not +currently a way to calculate pathway enrichment using lipids.

+

Pathway - a set of components that works together to +accomplish something or are related to each other in some other way. +This includes classic signaling and metabolic pathways, but also +molecular function and localization categories and other groups of +related components, like genome location, conservation, etc.

+

Condition - a sample where the treatment, environmental +conditions, patient, time point or some combination of those is +varied.

+

The overall idea for functional enrichment is to determine which +pathways are statistically over-represented in one group versus another, +display statistically differential abundance from one group to another, +or are statistically differentially distributed in a ranked list based +on the abundance of one sample. Each of these purposes has a different +underlying statistical test (or family of tests) and the results of each +can be interpreted in somewhat different ways. The purpose of this +vignette is to give the user a very brief introduction on how to use the +package, not to discuss the underlying statistical choices that need to +be made when analyzing such data.

+

There are a number of caveats (probably non-exhaustive) with doing +this kind of analysis.

+
+
+

Important points for consideration +

+
+

Data normalization +

+

One important point is to use data that’s been normalized in a +particular way to do these analyses. Data here has been normalized as a +Z score by row (gene/protein/etc.). So, for each row, calculate +the mean and standard deviation across all the conditions (columns) and +then express as a Z score.

+

Here’s why. All high-throughput technologies (microarray, RNAseq, +MS-assisted proteomics, metabolomics, lipidomics, etc.) suffer from the +same limitation. The detectability of each molecule being detected +(protein, RNA, etc.) is different and, in general, it’s impossible to +accurately determine how detectable each one is. The multi-omic +functional enrichment process lumps together measurements from different +components (proteins, genes, etc.) to summarize a pathway. If the +component measurements aren’t directly comparable (they aren’t) then +this can and will introduce significant systematic errors and won’t +produce the results you’re looking for. Careful consideration must be +given that the results of the analysis reflect the question being asked +and that the normalization method hasn’t obscured the desired +results.

+
+
+

Background +

+

The background of comparison for functional enrichment is always +important, but it mainly impacts the Fisher’s exact tests in the +examples below. The background answers the question: “My functional +group of interest is statistically enriched relative to what?” For +Fisher’s exact tests this is critical. Generally, it is best to compare +enrichment against the components observed in the data (the experiment’s +universe) rather than the universe of all possible components. For +example, a proteomics dataset from plasma may contain a limited set of +proteins compared with all possible human proteins; using the observed +proteins as the background usually yields more meaningful results. Using +all possible proteins will result in substantially different +findings.

+
+
+

Multiple hypothesis correction +

+

When testing the statistical significance of differences in a lot of +pathways it’s necessary to correct for multiple hypotheses. This +essentially accounts for the possibility you might see SOMETHING +significant by chance if you just test enough things- so it moves p +values in a less significant direction. The more things you test, the +greater this move will be. So pathway databases with lots of pathways +are affected more by this correction, making it harder to get a +significant result (which is a good thing actually).

+
+
+

Pathway databases +

+

Two ‘databases’ (organized text files) are included for pathways. The +example is taken from the NCI’s Pathway Interaction Database (PID) and +covers signaling pathways in human - but is no longer being actively +maintained. They can be loaded as follows:

+
+data(ncipid)
+
+
+

Identifiers +

+

The identifiers (gene names, e.g.) for the data input MUST match the +identifiers used in the pathway database. The two included human +databases use the HGNC-approved gene names. Which means your data has to +use the same identifiers.

+
+
+

Example data +

+

A sample data set is included that is from the CPTAC study of 169 +ovarian tumors. We include the dataset as a object, containing three +assays (transcriptomics, global proteomics, and phosphoproteomics) to +enable interoperability with other tools, and store example file as +rda on Figshare +as example.

+

This data can be loaded as follows:

+
+# Inspect the `pset` SummarizedExperiment.
+str(pset)
+#> Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
+#>   ..@ colData        :Formal class 'DFrame' [package "S4Vectors"] with 6 slots
+#>   .. .. ..@ rownames       : chr [1:174] "TCGA-09-1664" "TCGA-09-2056" "TCGA-13-1404" "TCGA-13-1409" ...
+#>   .. .. ..@ nrows          : int 174
+#>   .. .. ..@ elementType    : chr "ANY"
+#>   .. .. ..@ elementMetadata: NULL
+#>   .. .. ..@ metadata       : list()
+#>   .. .. ..@ listData       : Named list()
+#>   ..@ assays         :Formal class 'SimpleAssays' [package "SummarizedExperiment"] with 1 slot
+#>   .. .. ..@ data:Formal class 'SimpleList' [package "S4Vectors"] with 4 slots
+#>   .. .. .. .. ..@ listData       :List of 1
+#>   .. .. .. .. .. ..$ proteomics: num [1:18632, 1:174] -4.0649 -0.1398 -0.0366 0.768 0.2437 ...
+#>   .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
+#>   .. .. .. .. .. .. .. ..$ : chr [1:18632] "C9orf152" "ELMO2" "RPS11" "CREB3L1" ...
+#>   .. .. .. .. .. .. .. ..$ : chr [1:174] "TCGA-09-1664" "TCGA-09-2056" "TCGA-13-1404" "TCGA-13-1409" ...
+#>   .. .. .. .. ..@ elementType    : chr "ANY"
+#>   .. .. .. .. ..@ elementMetadata: NULL
+#>   .. .. .. .. ..@ metadata       : list()
+#>   ..@ NAMES          : chr [1:18632] "C9orf152" "ELMO2" "RPS11" "CREB3L1" ...
+#>   ..@ elementMetadata:Formal class 'DFrame' [package "S4Vectors"] with 6 slots
+#>   .. .. ..@ rownames       : NULL
+#>   .. .. ..@ nrows          : int 18632
+#>   .. .. ..@ elementType    : chr "ANY"
+#>   .. .. ..@ elementMetadata: NULL
+#>   .. .. ..@ metadata       : list()
+#>   .. .. ..@ listData       : Named list()
+#>   ..@ metadata       : list()
+dim(SummarizedExperiment::assay(pset, "proteomics"))
+#> [1] 18632   174
+head(rownames(pset))
+#> [1] "C9orf152" "ELMO2"    "RPS11"    "CREB3L1"  "PNMA1"    "MMP2"
+head(colnames(pset))
+#> [1] "TCGA-09-1664" "TCGA-09-2056" "TCGA-13-1404" "TCGA-13-1409" "TCGA-13-1410"
+#> [6] "TCGA-13-1482"
+

We also include some groups of patients to compare stored as R data +objects:

+
+data(shortlist)
+data(longlist)
+
+## columns that we want to use for results
+
+cols_to_display <- c("ingroup_n", "outgroup_n", "background_n", 
+                     "pvalue", "BH_pvalue")
+

The data are now loaded and ready to go through some of the +examples.

+
+
+
+
+

Examples +

+

We include five examples of how to use this tool, depending on the +analysis at hand. ## Comparison of one condition/group versus another +condition/group.

+

There are a number of ways to do this. I generally use a simple +approach which assesses the statistical difference in distributions +between the abundance values from all the members of a pathway in all +the group members from one group with those from the other group using a +t test.

+
+

Caveat +

+

This is a ‘bag of values’ approach and it does not pay attention to +the relationships between values in different groups (i.e. that each +group has measurements for the same component). There are likely issues +that rise because of this and caveats associated with it. However, it +works fairly well.

+
+
+

Description +

+

In this example we are assessing the enrichment of pathways in a +group of short surviving patients versus in a group of long +surviving
+patients. We can also do a single patient-to-patient comparison or +compare a single patient to a group of patients.

+
+
+

Interpretation +

+

Better corrected p-values are more enriched. However, you can get +good p-values when the algorithm only considers a limited number of +components from a pathway. That is, the pathway may have 30 members and +the p-value is coming from values from just 3 members. You can look at +the ingroup_n column from the result matrix to see this +(and screen out if desired).

+

It is VERY important to also consider the effect size. That is, the +difference between the mean of one group and the mean of the other +group. If there are large numbers of components in the pathway being +compared it is relatively easy to get a significant p value with small +effect size. Though this may be a real difference it is often not as +interesting as a smaller group with worse p value and greater effect +size. You can look at the effect size by comparing the +ingroup_mean and outgroup_mean columns.

+
+# in this example we lump a bunch of patients together (the 'short survivors')
+# and compare them to another group (the 'long survivors')
+
+### using enrichment_wrapper function
+protdata.enrichment.svl <- leapR::leapR(
+  geneset = ncipid,
+  enrichment_method = "enrichment_comparison",
+  eset = pset,
+  assay_name = "proteomics",
+  primary_columns = shortlist,
+  secondary_columns = longlist
+)
+
+or <- order(unlist(protdata.enrichment.svl[, "pvalue"]))
+rmarkdown::paged_table(protdata.enrichment.svl[or, cols_to_display])
+
+ +
+
+# another application is to compare just one patient against another
+# (this would be the  equivalent of comparing one time point to another)
+
+### using enrichment_wrapper function
+protdata.enrichment.svl.ovo <- leapR::leapR(
+  geneset = ncipid,
+  enrichment_method = "enrichment_comparison",
+  eset = pset,
+  assay_name = "proteomics",
+  primary_columns = shortlist[1],
+  secondary_columns = longlist[1]
+)
+or <- order(unlist(protdata.enrichment.svl.ovo[, "pvalue"]))
+rmarkdown::paged_table(protdata.enrichment.svl.ovo[or, cols_to_display])
+
+ +
+

When we only compare one sample to another, we get no enriched +pathways.

+
+
+

Fisher’s exact test +

+

For this test I use Fisher’s exact which is a simple comparison of +the overlap of two sets (think of it like a statistical Venn diagram +with two groups). It’s also referred to as a hypergeometric test.

+
+

Caveats +

+

Caveat 1. Fisher’s exact does not consider abundance values +but only lists of components. Generally this requires some separation of +a group of interest using differential expression, module membership +(from a network for example), or some other method.

+

Caveat 2. The choice of background for comparison can make a +big difference on outcome. For example, in a proteomics experiment where +you’re looking at enrichment in a group of highly differentially +expressed proteins, you could choose to use all possible proteins as a +background, or you could use just those proteins that were observed by +proteomics (generally a much more limited set). The second option is +generally the best since the first options will result in (partly to +mostly) functions that are enriched in proteins that are seen in +proteomics. That is, the most abundant proteins, which is generally not +the desired outcome.

+
+
+

Description +

+

In the example below I construct a genelist of interest using a +simple abundance threshold on the data then use a background of all the +genes in the example dataset (which is a limited number). I then do a +simple hierarchical clustering on the data, extract modules, and step +through each module to calculate enrichment for them, outputting the +results into a separate text file.

+
+
+

Interpretation +

+

As with the t test comparison above it is important to look at the +number of pathway members included in the comparison (look at the +in_path column). There is no ‘effect size’ problem with Fisher’s exact +since it’s just a set comparison, but it’s important to note that +significant p values can arise from a pathway being +underrepresented in the genelist, which often times is not the +desired result. The foldx column gives a ratio of in versus not in the +genelist, values > 1 being enriched and <1 being depleted.

+
+# for this example we will construct a list of genes from the expression data
+#     to emulate what you might be inputting
+genelist <- rownames(pset)[which(SummarizedExperiment::assay(pset, 
+                                                    "proteomics")[, 1] > 0.5)]
+
+protdata.enrichment.sets.test <- leapR::leapR(
+  geneset = ncipid,
+  enrichment_method = "enrichment_in_sets",
+  eset = pset,
+  assay_name = "proteomics",
+  targets = genelist
+)
+or <- order(protdata.enrichment.sets.test[, "pvalue"])
+rmarkdown::paged_table(protdata.enrichment.sets.test[or, cols_to_display])
+
+ +
+
+
+
+
+# in this example we construct some modules from the hierarchical clustering
+#   of the   data
+protdata_naf <- SummarizedExperiment::assay(pset, "proteomics")
+
+# hierarchical clustering is not too happy with lots of missing values
+#    so we'll do a zero fill on this to get the modules
+protdata_naf[which(is.na(protdata_naf))] <- 0
+
+# construct the hierarchical clustering using the 'wardD' method, which
+#    seems to give more even sized modules
+protdata_hc <- hclust(dist(protdata_naf), method = "ward.D2")
+
+# arbitrarily we'll chop the clusters into 5 modules
+modules <- cutree(protdata_hc, k = 5)
+
+## sara: created list
+clusters <- lapply(unique(modules), function(x) names(which(modules == x)))
+
+# modules is a named list of values where each value is a module
+#         number and the name is the gene name
+
+# To do enrichment for one module (module 1 in this case) do this
+protdata.enrichment.sets.module_1 <- leapR::leapR(
+  geneset = ncipid,
+  enrichment_method = "enrichment_in_sets",
+  eset = pset,
+  assay_name = "proteomics",
+  targets = names(modules[which(modules == 1)])
+)
+
+# To do enrichment on all modules and return the list of enrichment results
+protdata.enrichment.sets.modules <- do.call(rbind, 
+                                            leapR::cluster_enrichment(
+                                                eset = pset,
+                                                assay_name= 'proteomics',
+                                                geneset = ncipid,
+                                                clusters = clusters, 
+                                                sigfilter = 0.25))
+## nothing is enriched
+rmarkdown::paged_table(protdata.enrichment.sets.modules[, cols_to_display])
+
+ +
+
+
Visualizing Fisher’s exact results +
+
+# Plot the top enriched gene sets from Fisher's exact test
+# Stars indicate significance (None seen here)
+plot_leapr_bar(
+  protdata.enrichment.sets.test,
+  title = "Fisher's Exact Test: Top Enriched Pathways",
+  top_n = 10,
+  star_thresholds = c(0.05, 0.01, 0.001),
+  wrap = 40
+)
+

+
+
+
+
+

The Kolmogorov–Smirnov test (KS) +

+

Similar to the popular GSEA, KS tests whether a group of components +(the pathway) is distributed in a statistically significant manner in a +ranked list of components. That is, if all the members of the pathway +are clustered together at the top of the list (highly abundant, e.g.) or +at the bottom of the list (low abundance, e.g.) this will return good p +values. I should note that GSEA uses a more sophisticated approach than +this and their application has a lot of bells and whistles.

+
+

Description +

+

In the example below I’m simply calculating enrichment for one of the +patients in the list (arbitrarily selected). The ranking value is +relative protein abundance in this case, but can be any continuous +measure or derived value. For example, you could calculate the topology +of all proteins in a network and use the topology measure (degree) as +the measure.

+
+
+

Interpretation +

+

Similar to the other examples be cautious of pathways with good p +values that consider a small number of pathway numbers (in_path column). +The MeanPath column gives a measure that shows how far above or below +the median the mean rank of the pathway is (normalized to -1,1). The +Zscore column is a Zscore calculated on the basis of the mean percentage +rank of the pathway relative to the mean of the entire list divided by +the standard deviation of the pathway rank. The foldx column expresses +the mean percentage rank of the pathway relative to the entire list - +closer to 0 is higher in the list and closer to 1 is closer to the +bottom of the list. Each of these should give consistent results, but +will be somewhat different.

+
+# This is how you calculate enrichment in a ranked list 
+# (for example from topology)
+### using enrichment_wrapper function
+protdata.enrichment.order <- leapR::leapR(
+  geneset = ncipid, "enrichment_in_order",
+  eset = pset,
+  method = 'ks',
+  assay_name = "proteomics",
+  primary_columns = shortlist[1]
+)
+
+
+or <- order(protdata.enrichment.order[, "pvalue"])
+rmarkdown::paged_table(protdata.enrichment.order[or, cols_to_display])
+
+ +
+
+
Visualizing KS test results +
+
+# Plot the ranked enrichment results
+plot_leapr_bar(
+  protdata.enrichment.order,
+  title = "Kolmogorov-Smirnov Test: Ranked Enrichment",
+  top_n = 12,
+  fill_sig = "#2166AC",
+  fill_ns = "#B2DFDB",
+  wrap = 38
+)
+

+
+
+
+

KS alternative: the one-sample Z test +

+

Given that the KS test has not always been the best for +gene set enrichment we also implement the one-sample z test.

+
+# This is how you calculate enrichment in a ranked list 
+# (for example from topology)
+### using enrichment_wrapper function
+protdata.enrichment.order <- leapR::leapR(
+  geneset = ncipid, "enrichment_in_order",
+  eset = pset,
+  method = 'ztest',
+  assay_name = "proteomics",
+  primary_columns = shortlist[1]
+)
+
+
+or <- order(protdata.enrichment.order[, "pvalue"])
+rmarkdown::paged_table(protdata.enrichment.order[or, cols_to_display])
+
+ +
+
+
+plot_leapr_bar(
+  protdata.enrichment.order,
+  title = "Z Test: Ranked Enrichment",
+  top_n = 12,
+  fill_sig = "#2166AC",
+  fill_ns = "#B2DFDB",
+  wrap = 38
+)
+

+
+
+
+

Enrichment in Correlation +

+

The idea here is to use the correlation of pathway members to each +other versus to non-pathway members as a way to assess functional +enrichment. This idea seems sound- pathways that are varying in a +correlated way across a bunch of conditions (say time points or +patients) may be more active and more important than others. However, +more testing and validation is needed to show that this is the case.

+
+

Interpretation +

+

The ingroup_mean gives the mean correlation of the +pathway members to each other and outgroup_mean gives the correlation of +the pathway members to non-pathway members. Background_mean gives the +mean correlation of all non-pathway members. The pvalue and +BH_pvalue are for the pathway members to each other versus +those pathway members to non-pathway components. The +pvalue_background and BH_pvalue_background are +for the pathway member correlation relative to non-pathway member +correlation (which is similar but slightly different than the other +p-values).

+
+### using enrichment_wrapper function
+protdata.enrichment.correlation <- leapR::leapR(
+  geneset = ncipid,
+  enrichment_method = "correlation_enrichment",
+  assay_name = "proteomics",
+  eset = pset
+)
+
+or <- order(protdata.enrichment.correlation[, "pvalue"])
+rmarkdown::paged_table(head(protdata.enrichment.correlation[or, 
+                                                      cols_to_display]))
+
+ +
+
+
+protdata.enrichment.correlation.short <- leapR::leapR(
+  geneset = ncipid,
+  enrichment_method = "correlation_enrichment",
+  assay_name = "proteomics",
+  eset = pset[, shortlist]
+)
+or <- order(protdata.enrichment.correlation.short[, "pvalue"])
+rmarkdown::paged_table(head(protdata.enrichment.correlation.short[or, 
+                                                        cols_to_display]))
+
+ +
+
+
+protdata.enrichment.correlation.long <- leapR::leapR(
+  geneset = ncipid,
+  enrichment_method = "correlation_enrichment",
+  assay_name = "proteomics",
+  eset = pset[, longlist]
+)
+or <- order(protdata.enrichment.correlation.long[, "pvalue"])
+rmarkdown::paged_table(head(protdata.enrichment.correlation.long[or, 
+                                                            cols_to_display]))
+
+ +
+
+
Visualizing correlation enrichment results +
+
+# Compare correlation patterns across conditions
+plot_leapr_bar(
+  protdata.enrichment.correlation.short,
+  title = "Correlation Enrichment: Short Survivors",
+  top_n = 10,
+  fill_sig = "#1B9E77",
+  fill_ns = "#D8F0E8",
+  wrap = 36
+)
+

+
+
+
+
+

Phosphoproteomics data analysis +

+

In this example we will use phosphoproteomics data to assess the +enrichment in known kinase substrates (a proxy for kinase activity)

+
+data("kinasesubstrates")
+
+# for an individual tumor calculate the Kinase-Substrate 
+# Enrichment (similar to KSEA)
+#     This uses the site-specific phosphorylation data to determine 
+# which kinases
+#     might be active by assessing the enrichment of the 
+# phosphorylation of their known substrates
+
+phosphodata.ksea.order <- leapR::leapR(
+  geneset = kinasesubstrates,
+  enrichment_method = "enrichment_in_order",
+  assay_name = "phosphoproteomics",
+  eset = phset,
+  method = 'ztest', 
+  primary_columns = "TCGA-13-1484")
+
+or <- order(phosphodata.ksea.order[, "pvalue"])
+rmarkdown::paged_table(phosphodata.ksea.order[or, cols_to_display])
+
+ +
+
+
+
+# now do the same thing but use a threshold
+phosphodata.sets.order <- leapR::leapR(
+  geneset = kinasesubstrates,
+  enrichment_method = "enrichment_in_sets",
+  eset = phset,
+  assay_name = "phosphoproteomics",
+  threshold = 0.5,
+  primary_columns = "TCGA-13-1484"
+)
+or <- order(phosphodata.sets.order[, "pvalue"])
+rmarkdown::paged_table(phosphodata.sets.order[or, cols_to_display])
+
+ +
+
+
Visualizing kinase substrate enrichment +
+
+plot <- plot_leapr_bar(
+  phosphodata.sets.order,
+  title            = "Kinase Substrate Enrichment (Phosphoproteomics)",
+  top_n            = 15,
+  star_thresholds  = c(0.05, 0.01, 1e-3),
+  wrap             = 36,
+  fill_sig         = "#0F766E",  # dark teal for significant
+  fill_ns          = "#99F6E4",  # light teal for non-significant
+  outline          = "grey30",
+  axis_text_y_size = 7,
+  axis_text_x_size = 8
+)
+
+plot
+

+
+
+# You can also modify the plot further using standard ggplot2 arguments
+plot + ggplot2::labs(
+  y = expression(-log[10]("adjusted p-value")),
+  caption = "* p<0.05, ** p<0.01, *** p<0.001"
+)
+

+

Lastly we print out the session info!

+
+sessionInfo()
+#> R version 4.5.2 (2025-10-31)
+#> Platform: aarch64-apple-darwin20
+#> Running under: macOS Tahoe 26.2
+#> 
+#> Matrix products: default
+#> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
+#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
+#> 
+#> locale:
+#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+#> 
+#> time zone: America/Los_Angeles
+#> tzcode source: internal
+#> 
+#> attached base packages:
+#> [1] stats     graphics  grDevices utils     datasets  methods   base     
+#> 
+#> other attached packages:
+#> [1] stringr_1.6.0    tibble_3.3.1     dplyr_1.1.4      ggplot2_4.0.1   
+#> [5] rmarkdown_2.30   gplots_3.3.0     leapR_0.99.7     BiocStyle_2.38.0
+#> 
+#> loaded via a namespace (and not attached):
+#>  [1] SummarizedExperiment_1.40.0 gtable_0.3.6               
+#>  [3] xfun_0.56                   bslib_0.10.0               
+#>  [5] htmlwidgets_1.6.4           caTools_1.18.3             
+#>  [7] Biobase_2.70.0              lattice_0.22-7             
+#>  [9] tzdb_0.5.0                  vctrs_0.7.1                
+#> [11] tools_4.5.2                 bitops_1.0-9               
+#> [13] generics_0.1.4              stats4_4.5.2               
+#> [15] pkgconfig_2.0.3             Matrix_1.7-4               
+#> [17] KernSmooth_2.23-26          RColorBrewer_1.1-3         
+#> [19] S7_0.2.1                    desc_1.4.3                 
+#> [21] S4Vectors_0.48.0            lifecycle_1.0.5            
+#> [23] compiler_4.5.2              farver_2.1.2               
+#> [25] textshaping_1.0.4           Seqinfo_1.0.0              
+#> [27] htmltools_0.5.9             sass_0.4.10                
+#> [29] yaml_2.3.12                 pkgdown_2.2.0              
+#> [31] pillar_1.11.1               jquerylib_0.1.4            
+#> [33] DelayedArray_0.36.0         cachem_1.1.0               
+#> [35] abind_1.4-8                 gtools_3.9.5               
+#> [37] tidyselect_1.2.1            digest_0.6.39              
+#> [39] stringi_1.8.7               bookdown_0.46              
+#> [41] labeling_0.4.3              fastmap_1.2.0              
+#> [43] grid_4.5.2                  cli_3.6.5                  
+#> [45] SparseArray_1.10.8          magrittr_2.0.4             
+#> [47] S4Arrays_1.10.1             readr_2.1.6                
+#> [49] withr_3.0.2                 scales_1.4.0               
+#> [51] XVector_0.50.0              matrixStats_1.5.0          
+#> [53] otel_0.2.0                  ragg_1.5.0                 
+#> [55] hms_1.1.4                   evaluate_1.0.5             
+#> [57] knitr_1.51                  GenomicRanges_1.62.1       
+#> [59] IRanges_2.44.0              rlang_1.1.7                
+#> [61] glue_1.8.0                  BiocManager_1.30.27        
+#> [63] BiocGenerics_0.56.0         rstudioapi_0.18.0          
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+#> [69] fs_1.6.6
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Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

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Site built with pkgdown 2.2.0.

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If isCallable(callback) is false, throw a TypeError exception. + // See: http://es5.github.com/#x9.11 + if (typeof callback !== "function") { + throw new TypeError(callback + ' is not a function'); + } + + // 5. If thisArg was supplied, let T be thisArg; else let + // T be undefined. + if (arguments.length > 1) { + T = thisArg; + } + + // 6. Let k be 0 + k = 0; + + // 7. Repeat, while k < len + while (k < len) { + + var kValue; + + // a. Let Pk be ToString(k). + // This is implicit for LHS operands of the in operator + // b. Let kPresent be the result of calling the HasProperty + // internal method of O with argument Pk. + // This step can be combined with c + // c. If kPresent is true, then + if (k in O) { + + // i. Let kValue be the result of calling the Get internal + // method of O with argument Pk. + kValue = O[k]; + + // ii. Call the Call internal method of callback with T as + // the this value and argument list containing kValue, k, and O. + callback.call(T, kValue, k, O); + } + // d. Increase k by 1. + k++; + } + // 8. return undefined + }; +} + +// Production steps of ECMA-262, Edition 5, 15.4.4.19 +// Reference: http://es5.github.io/#x15.4.4.19 +if (!Array.prototype.map) { + + Array.prototype.map = function(callback, thisArg) { + + var T, A, k; + + if (this == null) { + throw new TypeError(' this is null or not defined'); + } + + // 1. Let O be the result of calling ToObject passing the |this| + // value as the argument. + var O = Object(this); + + // 2. Let lenValue be the result of calling the Get internal + // method of O with the argument "length". + // 3. Let len be ToUint32(lenValue). + var len = O.length >>> 0; + + // 4. If IsCallable(callback) is false, throw a TypeError exception. + // See: http://es5.github.com/#x9.11 + if (typeof callback !== 'function') { + throw new TypeError(callback + ' is not a function'); + } + + // 5. If thisArg was supplied, let T be thisArg; else let T be undefined. + if (arguments.length > 1) { + T = thisArg; + } + + // 6. Let A be a new array created as if by the expression new Array(len) + // where Array is the standard built-in constructor with that name and + // len is the value of len. + A = new Array(len); + + // 7. Let k be 0 + k = 0; + + // 8. Repeat, while k < len + while (k < len) { + + var kValue, mappedValue; + + // a. Let Pk be ToString(k). + // This is implicit for LHS operands of the in operator + // b. Let kPresent be the result of calling the HasProperty internal + // method of O with argument Pk. + // This step can be combined with c + // c. If kPresent is true, then + if (k in O) { + + // i. Let kValue be the result of calling the Get internal + // method of O with argument Pk. + kValue = O[k]; + + // ii. Let mappedValue be the result of calling the Call internal + // method of callback with T as the this value and argument + // list containing kValue, k, and O. + mappedValue = callback.call(T, kValue, k, O); + + // iii. Call the DefineOwnProperty internal method of A with arguments + // Pk, Property Descriptor + // { Value: mappedValue, + // Writable: true, + // Enumerable: true, + // Configurable: true }, + // and false. + + // In browsers that support Object.defineProperty, use the following: + // Object.defineProperty(A, k, { + // value: mappedValue, + // writable: true, + // enumerable: true, + // configurable: true + // }); + + // For best browser support, use the following: + A[k] = mappedValue; + } + // d. Increase k by 1. + k++; + } + + // 9. return A + return A; + }; +} + +var PagedTable = function (pagedTable) { + var me = this; + + var source = function(pagedTable) { + var sourceElems = [].slice.call(pagedTable.children).filter(function(e) { + return e.hasAttribute("data-pagedtable-source"); + }); + + if (sourceElems === null || sourceElems.length !== 1) { + throw("A single data-pagedtable-source was not found"); + } + + return JSON.parse(sourceElems[0].innerHTML); + }(pagedTable); + + var options = function(source) { + var options = typeof(source.options) !== "undefined" && + source.options !== null ? source.options : {}; + + var columns = typeof(options.columns) !== "undefined" ? options.columns : {}; + var rows = typeof(options.rows) !== "undefined" ? options.rows : {}; + + var positiveIntOrNull = function(value) { + return parseInt(value) >= 0 ? parseInt(value) : null; + }; + + return { + pages: positiveIntOrNull(options.pages), + rows: { + min: positiveIntOrNull(rows.min), + max: positiveIntOrNull(rows.max), + total: positiveIntOrNull(rows.total) + }, + columns: { + min: positiveIntOrNull(columns.min), + max: positiveIntOrNull(columns.max), + total: positiveIntOrNull(columns.total) + } + }; + }(source); + + var Measurer = function() { + + // set some default initial values that will get adjusted in runtime + me.measures = { + padding: 12, + character: 8, + height: 15, + defaults: true + }; + + me.calculate = function(measuresCell) { + if (!me.measures.defaults) + return; + + var measuresCellStyle = window.getComputedStyle(measuresCell, null); + + var newPadding = parsePadding(measuresCellStyle.paddingLeft) + + parsePadding(measuresCellStyle.paddingRight); + + var sampleString = "ABCDEFGHIJ0123456789"; + var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length); + + if (newPadding <= 0 || newCharacter <= 0) + return; + + me.measures.padding = newPadding; + me.measures.character = newCharacter; + me.measures.height = measuresCell.clientHeight; + me.measures.defaults = false; + }; + + return me; + }; + + var Page = function(data, options) { + var me = this; + + var defaults = { + max: 7, + rows: 10 + }; + + var totalPages = function() { + return Math.ceil(data.length / me.rows); + }; + + me.number = 0; + me.max = options.pages !== null ? options.pages : defaults.max; + me.visible = me.max; + me.rows = options.rows.min !== null ? options.rows.min : defaults.rows; + me.total = totalPages(); + + me.setRows = function(newRows) { + me.rows = newRows; + me.total = totalPages(); + }; + + me.setPageNumber = function(newPageNumber) { + if (newPageNumber < 0) newPageNumber = 0; + if (newPageNumber >= me.total) newPageNumber = me.total - 1; + + me.number = newPageNumber; + }; + + me.setVisiblePages = function(visiblePages) { + me.visible = Math.min(me.max, visiblePages); + me.setPageNumber(me.number); + }; + + me.getVisiblePageRange = function() { + var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0); + var end = me.number + Math.floor(me.visible / 2) + 1; + var pageCount = me.total; + + if (start < 0) { + var diffToStart = 0 - start; + start += diffToStart; + end += diffToStart; + } + + if (end > pageCount) { + var diffToEnd = end - pageCount; + start -= diffToEnd; + end -= diffToEnd; + } + + start = start < 0 ? 0 : start; + end = end >= pageCount ? pageCount : end; + + var first = false; + var last = false; + + if (start > 0 && me.visible > 1) { + start = start + 1; + first = true; + } + + if (end < pageCount && me.visible > 2) { + end = end - 1; + last = true; + } + + return { + first: first, + start: start, + end: end, + last: last + }; + }; + + me.getRowStart = function() { + var rowStart = page.number * page.rows; + if (rowStart < 0) + rowStart = 0; + + return rowStart; + }; + + me.getRowEnd = function() { + var rowStart = me.getRowStart(); + return Math.min(rowStart + me.rows, data.length); + }; + + me.getPaddingRows = function() { + var rowStart = me.getRowStart(); + var rowEnd = me.getRowEnd(); + return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0; + }; + }; + + var Columns = function(data, columns, options) { + var me = this; + + me.defaults = { + min: 5 + }; + + me.number = 0; + me.visible = 0; + me.total = columns.length; + me.subset = []; + me.padding = 0; + me.min = options.columns.min !== null ? options.columns.min : me.defaults.min; + me.max = options.columns.max !== null ? options.columns.max : null; + me.widths = {}; + + var widthsLookAhead = Math.max(100, options.rows.min); + var paddingColChars = 10; + + me.emptyNames = function() { + columns.forEach(function(column) { + if (columns.label !== null && columns.label !== "") + return false; + }); + + return true; + }; + + var parsePadding = function(value) { + return parseInt(value) >= 0 ? parseInt(value) : 0; + }; + + me.calculateWidths = function(measures) { + columns.forEach(function(column) { + var maxChars = Math.max( + column.label.toString().length, + column.type.toString().length + ); + + for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) { + maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length); + } + + me.widths[column.name] = { + // width in characters + chars: maxChars, + // width for the inner html columns + inner: maxChars * measures.character, + // width adding outer styles like padding + outer: maxChars * measures.character + measures.padding + }; + }); + }; + + me.getWidth = function() { + var widthOuter = 0; + for (var idxCol = 0; idxCol < me.subset.length; idxCol++) { + var columnName = me.subset[idxCol].name; + widthOuter = widthOuter + me.widths[columnName].outer; + } + + widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character; + + if (me.hasMoreLeftColumns()) { + widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; + } + + if (me.hasMoreRightColumns()) { + widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; + } + + return widthOuter; + }; + + me.updateSlice = function() { + if (me.number + me.visible >= me.total) + me.number = me.total - me.visible; + + if (me.number < 0) me.number = 0; + + me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total)); + + me.subset = me.subset.map(function(column) { + Object.keys(column).forEach(function(colKey) { + column[colKey] = column[colKey] === null ? "" : column[colKey].toString(); + }); + + column.width = null; + return column; + }); + }; + + me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) { + me.number = columnNumber; + me.visible = newVisibleColumns; + me.padding = paddingCount; + + me.updateSlice(); + }; + + me.incColumnNumber = function(increment) { + me.number = me.number + increment; + }; + + me.setColumnNumber = function(newNumber) { + me.number = newNumber; + }; + + me.setPaddingCount = function(newPadding) { + me.padding = newPadding; + }; + + me.getPaddingCount = function() { + return me.padding; + }; + + me.hasMoreLeftColumns = function() { + return me.number > 0; + }; + + me.hasMoreRightColumns = function() { + return me.number + me.visible < me.total; + }; + + me.updateSlice(0); + return me; + }; + + var data = source.data; + var page = new Page(data, options); + var measurer = new Measurer(data, options); + var columns = new Columns(data, source.columns, options); + + var table = null; + var tableDiv = null; + var header = null; + var footer = null; + var tbody = null; + + // Caches pagedTable.clientWidth, specially for webkit + var cachedPagedTableClientWidth = null; + + var onChangeCallbacks = []; + + var clearSelection = function() { + if(document.selection && document.selection.empty) { + document.selection.empty(); + } else if(window.getSelection) { + var sel = window.getSelection(); + sel.removeAllRanges(); + } + }; + + var columnNavigationWidthPX = 5; + + var renderColumnNavigation = function(increment, backwards) { + var arrow = document.createElement("div"); + arrow.setAttribute("style", + "border-top: " + columnNavigationWidthPX + "px solid transparent;" + + "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" + + "border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;"); + + var header = document.createElement("th"); + header.appendChild(arrow); + header.setAttribute("style", + "cursor: pointer;" + + "vertical-align: middle;" + + "min-width: " + columnNavigationWidthPX + "px;" + + "width: " + columnNavigationWidthPX + "px;"); + + header.onclick = function() { + columns.incColumnNumber(backwards ? -1 : increment); + + me.animateColumns(backwards); + renderFooter(); + + clearSelection(); + triggerOnChange(); + }; + + return header; + }; + + var maxColumnWidth = function(width) { + var padding = 80; + var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0); + + return parseInt(width) > 0 ? + Math.min(columnMax, parseInt(width)) + "px" : + columnMax + "px"; + }; + + var clearHeader = function() { + var thead = pagedTable.querySelectorAll("thead")[0]; + thead.innerHTML = ""; + }; + + var renderHeader = function(clear) { + cachedPagedTableClientWidth = pagedTable.clientWidth; + + var fragment = document.createDocumentFragment(); + + header = document.createElement("tr"); + fragment.appendChild(header); + + if (columns.number > 0) + header.appendChild(renderColumnNavigation(-columns.visible, true)); + + columns.subset = columns.subset.map(function(columnData) { + var column = document.createElement("th"); + column.setAttribute("align", columnData.align); + column.style.textAlign = columnData.align; + + column.style.maxWidth = maxColumnWidth(null); + if (columnData.width) { + column.style.minWidth = + column.style.maxWidth = maxColumnWidth(columnData.width); + } + + var columnName = document.createElement("div"); + columnName.setAttribute("class", "pagedtable-header-name"); + if (columnData.label === "") { + columnName.innerHTML = " "; + } + else { + columnName.appendChild(document.createTextNode(columnData.label)); + } + column.appendChild(columnName); + + var columnType = document.createElement("div"); + columnType.setAttribute("class", "pagedtable-header-type"); + if (columnData.type === "") { + columnType.innerHTML = " "; + } + else { + columnType.appendChild(document.createTextNode("<" + columnData.type + ">")); + } + column.appendChild(columnType); + + header.appendChild(column); + + columnData.element = column; + + return columnData; + }); + + for (var idx = 0; idx < columns.getPaddingCount(); idx++) { + var paddingCol = document.createElement("th"); + paddingCol.setAttribute("class", "pagedtable-padding-col"); + header.appendChild(paddingCol); + } + + if (columns.number + columns.visible < columns.total) + header.appendChild(renderColumnNavigation(columns.visible, false)); + + if (typeof(clear) == "undefined" || clear) clearHeader(); + var thead = pagedTable.querySelectorAll("thead")[0]; + thead.appendChild(fragment); + }; + + me.animateColumns = function(backwards) { + var thead = pagedTable.querySelectorAll("thead")[0]; + + var headerOld = thead.querySelectorAll("tr")[0]; + var tbodyOld = table.querySelectorAll("tbody")[0]; + + me.fitColumns(backwards); + + renderHeader(false); + + header.style.opacity = "0"; + header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)"; + header.style.transition = "transform 200ms linear, opacity 200ms"; + header.style.transitionDelay = "0"; + + renderBody(false); + + if (headerOld) { + headerOld.style.position = "absolute"; + headerOld.style.transform = "translateX(0px)"; + headerOld.style.opacity = "1"; + headerOld.style.transition = "transform 100ms linear, opacity 100ms"; + headerOld.setAttribute("class", "pagedtable-remove-head"); + if (headerOld.style.transitionEnd) { + headerOld.addEventListener("transitionend", function() { + var headerOldByClass = thead.querySelector(".pagedtable-remove-head"); + if (headerOldByClass) thead.removeChild(headerOldByClass); + }); + } + else { + thead.removeChild(headerOld); + } + } + + if (tbodyOld) table.removeChild(tbodyOld); + + tbody.style.opacity = "0"; + tbody.style.transition = "transform 200ms linear, opacity 200ms"; + tbody.style.transitionDelay = "0ms"; + + // force relayout + window.getComputedStyle(header).opacity; + window.getComputedStyle(tbody).opacity; + + if (headerOld) { + headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)"; + headerOld.style.opacity = "0"; + } + + header.style.transform = "translateX(0px)"; + header.style.opacity = "1"; + + tbody.style.opacity = "1"; + } + + me.onChange = function(callback) { + onChangeCallbacks.push(callback); + }; + + var triggerOnChange = function() { + onChangeCallbacks.forEach(function(onChange) { + onChange(); + }); + }; + + var clearBody = function() { + if (tbody) { + table.removeChild(tbody); + tbody = null; + } + }; + + var renderBody = function(clear) { + cachedPagedTableClientWidth = pagedTable.clientWidth + + var fragment = document.createDocumentFragment(); + + var pageData = data.slice(page.getRowStart(), page.getRowEnd()); + + pageData.forEach(function(dataRow, idxRow) { + var htmlRow = document.createElement("tr"); + htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd"); + + if (columns.hasMoreLeftColumns()) + htmlRow.appendChild(document.createElement("td")); + + columns.subset.forEach(function(columnData) { + var cellName = columnData.name; + var dataCell = dataRow[cellName]; + var htmlCell = document.createElement("td"); + + if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell"); + if (dataCell === "__NA__") dataCell = "NA"; + + var cellText = document.createTextNode(dataCell); + htmlCell.appendChild(cellText); + if (dataCell.length > 50) { + htmlCell.setAttribute("title", dataCell); + } + htmlCell.setAttribute("align", columnData.align); + htmlCell.style.textAlign = columnData.align; + htmlCell.style.maxWidth = maxColumnWidth(null); + if (columnData.width) { + htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width); + } + htmlRow.appendChild(htmlCell); + }); + + for (var idx = 0; idx < columns.getPaddingCount(); idx++) { + var paddingCol = document.createElement("td"); + paddingCol.setAttribute("class", "pagedtable-padding-col"); + htmlRow.appendChild(paddingCol); + } + + if (columns.hasMoreRightColumns()) + htmlRow.appendChild(document.createElement("td")); + + fragment.appendChild(htmlRow); + }); + + for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) { + var paddingRow = document.createElement("tr"); + + var paddingCellRow = document.createElement("td"); + paddingCellRow.innerHTML = " "; + paddingCellRow.setAttribute("colspan", "100%"); + paddingRow.appendChild(paddingCellRow); + + fragment.appendChild(paddingRow); + } + + if (typeof(clear) == "undefined" || clear) clearBody(); + tbody = document.createElement("tbody"); + tbody.appendChild(fragment); + + table.appendChild(tbody); + }; + + var getLabelInfo = function() { + var pageStart = page.getRowStart(); + var pageEnd = page.getRowEnd(); + var totalRows = data.length; + + var totalRowsLabel = options.rows.total ? options.rows.total : totalRows; + var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,'); + + var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows"; + if (totalRows < page.rows) { + infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : ""); + } + if (columns.total > columns.visible) { + var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total; + + infoText = infoText + " | " + (columns.number + 1) + "-" + + (Math.min(columns.number + columns.visible, columns.total)) + + " of " + totalColumnsLabel + " columns"; + } + + return infoText; + }; + + var clearFooter = function() { + footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0]; + footer.innerHTML = ""; + + return footer; + }; + + var createPageLink = function(idxPage) { + var pageLink = document.createElement("a"); + pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index"; + pageLink.setAttribute("class", pageLinkClass); + pageLink.setAttribute("data-page-index", idxPage); + pageLink.onclick = function() { + page.setPageNumber(parseInt(this.getAttribute("data-page-index"))); + renderBody(); + renderFooter(); + + triggerOnChange(); + }; + + pageLink.appendChild(document.createTextNode(idxPage + 1)); + + return pageLink; + } + + var renderFooter = function() { + footer = clearFooter(); + + var next = document.createElement("a"); + next.appendChild(document.createTextNode("Next")); + next.onclick = function() { + page.setPageNumber(page.number + 1); + renderBody(); + renderFooter(); + + triggerOnChange(); + }; + if (data.length > page.rows) footer.appendChild(next); + + var pageNumbers = document.createElement("div"); + pageNumbers.setAttribute("class", "pagedtable-indexes"); + + var pageRange = page.getVisiblePageRange(); + + if (pageRange.first) { + var pageLink = createPageLink(0); + pageNumbers.appendChild(pageLink); + + var pageSeparator = document.createElement("div"); + pageSeparator.setAttribute("class", "pagedtable-index-separator-left"); + pageSeparator.appendChild(document.createTextNode("...")) + pageNumbers.appendChild(pageSeparator); + } + + for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) { + var pageLink = createPageLink(idxPage); + + pageNumbers.appendChild(pageLink); + } + + if (pageRange.last) { + var pageSeparator = document.createElement("div"); + pageSeparator.setAttribute("class", "pagedtable-index-separator-right"); + pageSeparator.appendChild(document.createTextNode("...")) + pageNumbers.appendChild(pageSeparator); + + var pageLink = createPageLink(page.total - 1); + pageNumbers.appendChild(pageLink); + } + + if (data.length > page.rows) footer.appendChild(pageNumbers); + + var previous = document.createElement("a"); + previous.appendChild(document.createTextNode("Previous")); + previous.onclick = function() { + page.setPageNumber(page.number - 1); + renderBody(); + renderFooter(); + + triggerOnChange(); + }; + if (data.length > page.rows) footer.appendChild(previous); + + var infoLabel = document.createElement("div"); + infoLabel.setAttribute("class", "pagedtable-info"); + infoLabel.setAttribute("title", getLabelInfo()); + infoLabel.appendChild(document.createTextNode(getLabelInfo())); + footer.appendChild(infoLabel); + + var enabledClass = "pagedtable-index-nav"; + var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled"; + previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass); + next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass); + }; + + var measuresCell = null; + + var renderMeasures = function() { + var measuresTable = document.createElement("table"); + measuresTable.style.visibility = "hidden"; + measuresTable.style.position = "absolute"; + measuresTable.style.whiteSpace = "nowrap"; + measuresTable.style.height = "auto"; + measuresTable.style.width = "auto"; + + var measuresRow = document.createElement("tr"); + measuresTable.appendChild(measuresRow); + + measuresCell = document.createElement("td"); + var sampleString = "ABCDEFGHIJ0123456789"; + measuresCell.appendChild(document.createTextNode(sampleString)); + + measuresRow.appendChild(measuresCell); + + tableDiv.appendChild(measuresTable); + } + + me.init = function() { + tableDiv = document.createElement("div"); + pagedTable.appendChild(tableDiv); + var pagedTableClass = data.length > 0 ? + "pagedtable pagedtable-not-empty" : + "pagedtable pagedtable-empty"; + + if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) { + pagedTableClass = pagedTableClass + " pagedtable-empty-columns"; + } + + tableDiv.setAttribute("class", pagedTableClass); + + renderMeasures(); + measurer.calculate(measuresCell); + columns.calculateWidths(measurer.measures); + + table = document.createElement("table"); + table.setAttribute("cellspacing", "0"); + table.setAttribute("class", "table table-condensed"); + tableDiv.appendChild(table); + + table.appendChild(document.createElement("thead")); + + var footerDiv = document.createElement("div"); + footerDiv.setAttribute("class", "pagedtable-footer"); + tableDiv.appendChild(footerDiv); + + // if the host has not yet provided horizontal space, render hidden + if (tableDiv.clientWidth <= 0) { + tableDiv.style.opacity = "0"; + } + + me.render(); + + // retry seizing columns later if the host has not provided space + function retryFit() { + if (tableDiv.clientWidth <= 0) { + setTimeout(retryFit, 100); + } else { + me.render(); + triggerOnChange(); + } + } + if (tableDiv.clientWidth <= 0) { + retryFit(); + } + }; + + var registerWidths = function() { + columns.subset = columns.subset.map(function(column) { + column.width = columns.widths[column.name].inner; + return column; + }); + }; + + var parsePadding = function(value) { + return parseInt(value) >= 0 ? parseInt(value) : 0; + }; + + me.fixedHeight = function() { + return options.rows.max != null; + } + + me.fitRows = function() { + if (me.fixedHeight()) + return; + + measurer.calculate(measuresCell); + + var rows = options.rows.min !== null ? options.rows.min : 0; + var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0; + var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0; + + if (pagedTable.offsetHeight > 0) { + var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight; + rows = Math.floor((availableHeight) / measurer.measures.height); + } + + rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows; + + page.setRows(rows); + } + + // The goal of this function is to add as many columns as possible + // starting from left-to-right, when the right most limit is reached + // it tries to add columns from the left as well. + // + // When startBackwards is true columns are added from right-to-left + me.fitColumns = function(startBackwards) { + measurer.calculate(measuresCell); + columns.calculateWidths(measurer.measures); + + if (tableDiv.clientWidth > 0) { + tableDiv.style.opacity = 1; + } + + var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1; + var columnNumber = columns.number; + var paddingCount = 0; + + // track a list of added columns as we build the visible ones to allow us + // to remove columns when they don't fit anymore. + var columnHistory = []; + + var lastTableHeight = 0; + var backwards = startBackwards; + + var tableDivStyle = window.getComputedStyle(tableDiv, null); + var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) + + parsePadding(tableDivStyle.paddingRight); + + var addPaddingCol = false; + var currentWidth = 0; + + while (true) { + columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount); + currentWidth = columns.getWidth(); + + if (tableDiv.clientWidth - tableDivPadding < currentWidth) { + break; + } + + columnHistory.push({ + columnNumber: columnNumber, + visibleColumns: visibleColumns, + paddingCount: paddingCount + }); + + if (columnHistory.length > 100) { + console.error("More than 100 tries to fit columns, aborting"); + break; + } + + if (columns.max !== null && + columns.visible + columns.getPaddingCount() >= columns.max) { + break; + } + + // if we run out of right-columns + if (!backwards && columnNumber + columns.visible >= columns.total) { + // if we started adding right-columns, try adding left-columns + if (!startBackwards && columnNumber > 0) { + backwards = true; + } + else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { + break; + } + else { + paddingCount = paddingCount + 1; + } + } + + // if we run out of left-columns + if (backwards && columnNumber == 0) { + // if we started adding left-columns, try adding right-columns + if (startBackwards && columnNumber + columns.visible < columns.total) { + backwards = false; + } + else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { + break; + } + else { + paddingCount = paddingCount + 1; + } + } + + // when moving backwards try fitting left columns first + if (backwards && columnNumber > 0) { + columnNumber = columnNumber - 1; + } + + if (columnNumber + visibleColumns < columns.total) { + visibleColumns = visibleColumns + 1; + } + } + + var lastRenderableColumn = { + columnNumber: columnNumber, + visibleColumns: visibleColumns, + paddingCount: paddingCount + }; + + if (columnHistory.length > 0) { + lastRenderableColumn = columnHistory[columnHistory.length - 1]; + } + + columns.setVisibleColumns( + lastRenderableColumn.columnNumber, + lastRenderableColumn.visibleColumns, + lastRenderableColumn.paddingCount); + + if (pagedTable.offsetWidth > 0) { + page.setVisiblePages(Math.max(Math.ceil(1.0 * (pagedTable.offsetWidth - 250) / 40), 2)); + } + + registerWidths(); + }; + + me.fit = function(startBackwards) { + me.fitRows(); + me.fitColumns(startBackwards); + } + + me.render = function() { + me.fitColumns(false); + + // render header/footer to measure height accurately + renderHeader(); + renderFooter(); + + me.fitRows(); + renderBody(); + + // re-render footer to match new rows + renderFooter(); + } + + var resizeLastWidth = -1; + var resizeLastHeight = -1; + var resizeNewWidth = -1; + var resizeNewHeight = -1; + var resizePending = false; + + me.resize = function(newWidth, newHeight) { + + function resizeDelayed() { + resizePending = false; + + if ( + (resizeNewWidth !== resizeLastWidth) || + (!me.fixedHeight() && resizeNewHeight !== resizeLastHeight) + ) { + resizeLastWidth = resizeNewWidth; + resizeLastHeight = resizeNewHeight; + + setTimeout(resizeDelayed, 200); + resizePending = true; + } else { + me.render(); + triggerOnChange(); + + resizeLastWidth = -1; + resizeLastHeight = -1; + } + } + + resizeNewWidth = newWidth; + resizeNewHeight = newHeight; + + if (!resizePending) resizeDelayed(); + }; +}; + +var PagedTableDoc; +(function (PagedTableDoc) { + var allPagedTables = []; + + PagedTableDoc.initAll = function() { + allPagedTables = []; + + var pagedTables = [].slice.call(document.querySelectorAll('[data-pagedtable="false"],[data-pagedtable=""]')); + pagedTables.forEach(function(pagedTable, idx) { + pagedTable.setAttribute("data-pagedtable", "true"); + pagedTable.setAttribute("pagedtable-page", 0); + pagedTable.setAttribute("class", "pagedtable-wrapper"); + + var pagedTableInstance = new PagedTable(pagedTable); + pagedTableInstance.init(); + + allPagedTables.push(pagedTableInstance); + }); + }; + + PagedTableDoc.resizeAll = function() { + allPagedTables.forEach(function(pagedTable) { + pagedTable.render(); + }); + }; + + window.addEventListener("resize", PagedTableDoc.resizeAll); + + return PagedTableDoc; +})(PagedTableDoc || (PagedTableDoc = {})); + +window.onload = function() { + PagedTableDoc.initAll(); +}; diff --git a/docs/authors.html b/docs/authors.html new file mode 100644 index 0000000..b06fd3b --- /dev/null +++ b/docs/authors.html @@ -0,0 +1,123 @@ + +Authors and Citation • leapR + + +
+
+ + + +
+
+
+
+

Authors and Citation

+
+ + +
  • +

    Sara Gosline. Author, maintainer. +

    +
    • +
  • +

    Jason McDermott. Author. +

    +
    • +
  • +

    Jeremy Jacobson. Author. +

    +
    • +
  • +

    Vincent Danna. Contributor. +

    +
    • +
  • +

    National Institutes of Health. Funder. +

    +
    • +
+
+
+

Citation

+ +
+
+ + +

Gosline S, McDermott J, Jacobson J (2026). +leapR: Layered enrichment analysis of pathways R. +R package version 0.99.7. +

+ 
@Manual{,
+  title = {leapR: Layered enrichment analysis of pathways R},
+  author = {Sara Gosline and Jason McDermott and Jeremy Jacobson},
+  year = {2026},
+  note = {R package version 0.99.7},
+}
+ +
+ +
+ + + +
+

Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

+
+ +
+

Site built with pkgdown 2.2.0.

+
+ +
+ + + + + + + + diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css new file mode 100644 index 0000000..5a85941 --- /dev/null +++ b/docs/bootstrap-toc.css @@ -0,0 +1,60 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ + +/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ + +/* All levels of nav */ +nav[data-toggle='toc'] .nav > li > a { + display: block; + padding: 4px 20px; + font-size: 13px; + font-weight: 500; + color: #767676; +} +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 19px; + color: #563d7c; + text-decoration: none; + background-color: transparent; + border-left: 1px solid #563d7c; +} +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 18px; + font-weight: bold; + color: #563d7c; + background-color: transparent; + border-left: 2px solid #563d7c; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} +nav[data-toggle='toc'] .nav .nav > li > a { + padding-top: 1px; + padding-bottom: 1px; + padding-left: 30px; + font-size: 12px; + font-weight: normal; +} +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 29px; +} +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 28px; + font-weight: 500; +} + +/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ +nav[data-toggle='toc'] .nav > .active > ul { + display: block; +} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js new file mode 100644 index 0000000..1cdd573 --- /dev/null +++ b/docs/bootstrap-toc.js @@ -0,0 +1,159 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ +(function() { + 'use strict'; + + window.Toc = { + helpers: { + // return all matching elements in the set, or their descendants + findOrFilter: function($el, selector) { + // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ + // http://stackoverflow.com/a/12731439/358804 + var $descendants = $el.find(selector); + return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); + }, + + generateUniqueIdBase: function(el) { + var text = $(el).text(); + var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); + return anchor || el.tagName.toLowerCase(); + }, + + generateUniqueId: function(el) { + var anchorBase = this.generateUniqueIdBase(el); + for (var i = 0; ; i++) { + var anchor = anchorBase; + if (i > 0) { + // add suffix + anchor += '-' + i; + } + // check if ID already exists + if (!document.getElementById(anchor)) { + return anchor; + } + } + }, + + generateAnchor: function(el) { + if (el.id) { + return el.id; + } else { + var anchor = this.generateUniqueId(el); + el.id = anchor; + return anchor; + } + }, + + createNavList: function() { + return $('
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+ $("#search-input").focus(); + } + }); + + $(document).ready(function() { + // do keyword highlighting + /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ + var mark = function() { + + var referrer = document.URL ; + var paramKey = "q" ; + + if (referrer.indexOf("?") !== -1) { + var qs = referrer.substr(referrer.indexOf('?') + 1); + var qs_noanchor = qs.split('#')[0]; + var qsa = qs_noanchor.split('&'); + var keyword = ""; + + for (var i = 0; i < qsa.length; i++) { + var currentParam = qsa[i].split('='); + + if (currentParam.length !== 2) { + continue; + } + + if (currentParam[0] == paramKey) { + keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); + } + } + + if (keyword !== "") { + $(".contents").unmark({ + done: function() { + $(".contents").mark(keyword); + } + }); + } + } + }; + + mark(); + }); +}); + +/* Search term highlighting ------------------------------*/ + +function matchedWords(hit) { + var words = []; + + var hierarchy = hit._highlightResult.hierarchy; + // loop to fetch from lvl0, lvl1, etc. + for (var idx in hierarchy) { + words = words.concat(hierarchy[idx].matchedWords); + } + + var content = hit._highlightResult.content; + if (content) { + words = words.concat(content.matchedWords); + } + + // return unique words + var words_uniq = [...new Set(words)]; + return words_uniq; +} + +function updateHitURL(hit) { + + var words = matchedWords(hit); + var url = ""; + + if (hit.anchor) { + url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; + } else { + url = hit.url + '?q=' + escape(words.join(" ")); + } + + return url; +} diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..877fbd4 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,214 @@ + + + + + + + +Layered enrichment analysis of pathways R • leapR + + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    leapR +

    +

    Layered Enrichment Analysis of Pathways in R (leapR) a tool that carries out statistical enrichment analysis on single- or multi-omics data.

    +
    +

    Install +

    +

    leapR is available through Bioconductor repository here

    +
    if (!require("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+BiocManager::install(version = "3.21")
+BiocManager::install('BiocStyle')
+BiocManager::install('leapR')
    +

    Once you have successfully installed the package you can load the vignette to read examples using the vignette('leapR') command.

    +
    +
    +

    Basic Usage +

    +

    The primary function of the leapR package is the leapR function itself. This function serves a wrapper to run different styles of enrichment functions on the data. The package contains other functions to support pathway information and multi-omics datasets.

    +
    +

    Enrichment calls +

    +

    Here is a list of enrichment arguments that can be called with the leapR command.

    + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ArgumentDescription
    enrichment_in_setsCalculates enrichment in pathway membership in a list (e.g. highly differential proteins) relative to background using Fisher’s exact test.
    enrichment_in_orderCalculates enrichment of pathways based on a ranked list using the Kolmogorov-Smirnov test
    enrichment_comparisonCompares the distribution of abundances between two sets of conditions for each pathway using a t test
    enrichment_in_pathwaysCompares the distribution of abundances in a pathway with the background distribution of abundances using a t test
    correlation_enrichmentCalculates the enrichment of a pathway based on correlation between pathway members across conditions versus correlation between members not in the pathway
    enrichment_in_relationshipsCalculates the enrichment of a pathway in specified interactions relative to non-pathway members
    +
    +
    +

    Data examples +

    +

    We included examples of including proteomics data and transcriptomics data from 169 high-grade serous ovarian cancer (HGSOC) tumors previously studied and lists of the short- and long- surviving patients from that cohort.

    +
    +
    +

    Gene pathway examples +

    +

    We included two different gene pathways. An NCI pathway database (Pathway Information Database; PID) of signaling pathways and the MSIGDB set of gene collections from various sources.

    +
    +
    +
    +
    + + +
    + + +
    +

    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    +

    Site built with pkgdown 2.2.0.

    +
    + +
    +
    + + + + + + + + diff --git a/docs/link.svg b/docs/link.svg new file mode 100644 index 0000000..88ad827 --- /dev/null +++ b/docs/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/docs/pkgdown.css b/docs/pkgdown.css new file mode 100644 index 0000000..80ea5b8 --- /dev/null +++ b/docs/pkgdown.css @@ -0,0 +1,384 @@ +/* Sticky footer */ + +/** + * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ + * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css + * + * .Site -> body > .container + * .Site-content -> body > .container .row + * .footer -> footer + * + * Key idea seems to be to ensure that .container and __all its parents__ + * have height set to 100% + * + */ + +html, body { + height: 100%; +} + +body { + position: relative; +} + +body > .container { + display: flex; + height: 100%; + flex-direction: column; +} + +body > .container .row { + flex: 1 0 auto; +} + +footer { + margin-top: 45px; + padding: 35px 0 36px; 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+ + var nav_path = paths(links[i].pathname); + + var length = prefix_length(nav_path, cur_path); + if (length > max_length) { + max_length = length; + pos = i; + } + } + + // Add class to parent
  • , and enclosing
  • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); + + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $("div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); + } +})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml new file mode 100644 index 0000000..ce30917 --- /dev/null +++ b/docs/pkgdown.yml @@ -0,0 +1,8 @@ +pandoc: 3.7.0.2 +pkgdown: 2.2.0 +pkgdown_sha: ~ +articles: + examples: examples.html + leapR: leapR.html + order-enrichment: order-enrichment.html +last_built: 2026-02-08T00:00Z diff --git a/docs/reference/calcTTest.html b/docs/reference/calcTTest.html new file mode 100644 index 0000000..5afb30b --- /dev/null +++ b/docs/reference/calcTTest.html @@ -0,0 +1,148 @@ + +calcTTest — calcTTest • leapR + + +
    +
    + + + +
    +
    +
    +

    calcTTest

    + + +
    + +
    +

    calculates a t-test for two distributions of data on a per-gene basis +append results to ExpressionSet with two extra columns: `pvalue` and +`difference` for each feature

    +
    + +
    + 
    calcTTest(eset, assay_name, group1, group2)
    +
    + +
    +

    Arguments

    + + +
    eset
    +

    SummarizedExperiment

    + + +
    assay_name
    +

    name of assay

    + + +
    group1
    +

    List of samples comprising group 1

    + + +
    group2
    +

    List of samples comprising group 2

    + +
    +
    +

    Value

    +

    An Expression set with two columns added to the featureData +slot: pvalue, and estimate

    +
    + +
    +

    Examples

    + 
    
+        library(leapR)
+        url <- "https://api.figshare.com/v2/file/download/56536214"
+        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
+        load('transData.rda')
+        p <- file.remove("transData.rda")
+
+        # read in the pathways
+        data("ncipid")
+
+        # read in the patient groups
+        data("shortlist")
+        data("longlist")
+        calcTTest(tset, 'transcriptomics', shortlist, longlist)
+#> class: SummarizedExperiment 
+#> dim: 1999 174 
+#> metadata(0):
+#> assays(1): transcriptomics
+#> rownames(1999): NOC2L ISG15 ... ARL6 MINA
+#> rowData names(2): pvalue difference
+#> colnames(174): TCGA-13-1484 TCGA-13-1495 ... TCGA-61-1995 TCGA-61-2008
+#> colData names(0):
+
    +
    +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/cluster_enrichment.html b/docs/reference/cluster_enrichment.html new file mode 100644 index 0000000..abd3cec --- /dev/null +++ b/docs/reference/cluster_enrichment.html @@ -0,0 +1,166 @@ + +cluster_enrichment — cluster_enrichment • leapR + + +
    +
    + + + +
    +
    +
    +

    cluster_enrichment

    + + +
    + +
    +

    Cluster enrichment Run enrichment (Fisher's exact) on clusters (lists of +identifier groups)

    +
    + +
    + 
    cluster_enrichment(eset, assay_name, geneset, clusters, sigfilter = 0.05)
    +
    + +
    +

    Arguments

    + + +
    eset
    +

    is an SummarizedExperiment containing data that is clustered

    + + +
    assay_name
    +

    is the name of the assay

    + + +
    geneset
    +

    is a GeneSet object for pathway annotation

    + + +
    clusters
    +

    is a list of clusters (gene lists) to calculate enrichment +on, generally the result of the `cutree` function

    + + +
    sigfilter
    +

    minimum significance threshold default is .05

    + +
    +
    +

    Value

    +

    data frame with enrichment results

    +
    +
    +

    Details

    +

    This function will calculate enrichment (Fisher's exact test for + membership overlap) on

    +

    a series of lists of genes, such as from a set of clusters. The +results are returned as

    +

    a list of results matrices in the order of the input clusters.

    +
    + +
    +

    Examples

    + 
            library(leapR)
+
+        # read in the example transcriptomic data
+        url <- "https://api.figshare.com/v2/file/download/56536214"
+        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
+        load('transData.rda')
+        p <- file.remove("transData.rda")
+
+        # read in the pathways
+        data("ncipid")
+
+        # for the example we will limit the number of transcripts considered
+        #- arbitrarily in this case
+        transdata <- SummarizedExperiment::assay(tset,'transcriptomics')
+        transdata[which(is.na(transdata),arr.ind=TRUE)]<-0.0
+        # perform heirarchical clustering on the  data
+        transdata.hc <- hclust(dist(transdata), method="ward.D2")
+
+        transdata.hc.clusters <- cutree(transdata.hc, k=5)
+        clust.list <- lapply(seq_len(5), function(x) {
+           return(names(which(transdata.hc.clusters==x)))})
+        #calculates enrichment for each of the clusters individually a
+        #and returns a list of enrichment results
+        transdata.hc.enrichment <- leapR::cluster_enrichment(eset=tset,
+                assay_name='transcriptomics',
+                geneset=ncipid,
+                clusters=clust.list)
+
+
+
+
    +
    +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/combine_omics.html b/docs/reference/combine_omics.html new file mode 100644 index 0000000..064cd5f --- /dev/null +++ b/docs/reference/combine_omics.html @@ -0,0 +1,156 @@ + +combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids. — combine_omics • leapR + + +
    +
    + + + +
    +
    +
    +

    combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

    + + +
    + +
    +

    combine_omics +Combine two or more omics matrices into one multi-omics matrix with +'tagged' ids.

    +
    + +
    + 
    combine_omics(omics_list, id_list = rep(NA, length(omics_list)))
    +
    + +
    +

    Arguments

    + + +
    omics_list
    +

    Is a list of SummarizedExperiment each with +one assay

    + + +
    id_list
    +

    List of identifiers to use, in the same order as the +omics_list elements. If an element +is `NA`, then rownames are used.

    + +
    +
    +

    Value

    +

    SummarizedExperiment with an additional assay called + `combined`

    +
    +
    +

    Details

    +

    This combines matrices of different omics types together and +adds prefix tags to the ids.

    +
    + +
    +

    Examples

    + 
            library(leapR)
+        url <- 'https://api.figshare.com/v2/file/download/56536217'
+
+        pdata <- download.file(url,method='libcurl',destfile='protData.rda')
+#> Warning: downloaded length 18732111 != reported length 23253677
+#> Warning: URL 'https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/56536217/protData.rda?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20260208/eu-west-1/s3/aws4_request&X-Amz-Date=20260208T000035Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=3d35d9a6edc15872990b7dbabedeef681c1d49bcda05fa883149680acf838771': Timeout of 60 seconds was reached
+#> Error in download.file(url, method = "libcurl", destfile = "protData.rda"): download from 'https://api.figshare.com/v2/file/download/56536217' failed
+        load('protData.rda')
+#> Error in load("protData.rda"): error reading from connection
+        p <- file.remove("protData.rda")
+
+        url <- "https://api.figshare.com/v2/file/download/56536214"
+        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
+        load('transData.rda')
+        p <- file.remove("transData.rda")
+
+        url <- 'https://api.figshare.com/v2/file/download/56536211'
+        phdata<-download.file(url,method='libcurl',destfile = 'phosData.rda')
+        #phosphodata<-read.csv("phdata",check.names=FALSE,row.names=1)
+        load('phosData.rda')
+        p <- file.remove('phosData.rda')# read in the example protein data
+
+
+        # merge the three datasets by rows and add prefix tags for
+        # different omics types
+        multi_omics <- combine_omics(list(pset, tset, phset),
+                    list(NA,NA,'hgnc_id'))
+#> Error: object 'pset' not found
+
+
+
    +
    +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/correlation_comparison_enrichment.html b/docs/reference/correlation_comparison_enrichment.html new file mode 100644 index 0000000..3786472 --- /dev/null +++ b/docs/reference/correlation_comparison_enrichment.html @@ -0,0 +1,136 @@ + +correlation_comparison_enrichment — correlation_comparison_enrichment • leapR + + +
    +
    + + + +
    +
    +
    +

    correlation_comparison_enrichment

    + + +
    + +
    +

    # internal function to calculate enrichment in differences in correlation +# between two groups +# access through the leapr wrapper

    +
    + +
    + 
    correlation_comparison_enrichment(
+  geneset,
+  eset,
+  assay_name,
+  set1,
+  set2,
+  mapping_column = NA
+)
    +
    + +
    +

    Arguments

    + + +
    geneset
    +

    pathway to use for enrichment

    + + +
    eset
    +

    SummarizedExperiment with abundance matrix

    + + +
    assay_name
    +

    name of assay

    + + +
    set1
    +

    first set to use

    + + +
    set2
    +

    second set to use

    + + +
    mapping_column
    +

    Column to use for id mapping within rowData

    + +
    +
    +

    Value

    +

    data frame with enrichment results

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/correlation_enrichment.html b/docs/reference/correlation_enrichment.html new file mode 100644 index 0000000..9b35842 --- /dev/null +++ b/docs/reference/correlation_enrichment.html @@ -0,0 +1,119 @@ + +correlation_enrichment — correlation_enrichment • leapR + + +
    +
    + + + +
    +
    +
    +

    correlation_enrichment

    + + +
    + +
    +

    # calculate enrichment in correlation between pathway members +# access through leapr wrapper

    +
    + +
    + 
    correlation_enrichment(geneset, eset, assay_name, mapping_column = NA)
    +
    + +
    +

    Arguments

    + + +
    geneset
    +

    Geneset list

    + + +
    eset
    +

    a SummarizedExperiment object

    + + +
    assay_name
    +

    name of assay

    + + +
    mapping_column
    +

    Column to use to map identifiers, if not rownames

    + +
    +
    +

    Value

    +

    list of enrichment statistic table and correlation matrix

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/enrichment_in_abundance.html b/docs/reference/enrichment_in_abundance.html new file mode 100644 index 0000000..eaf5c29 --- /dev/null +++ b/docs/reference/enrichment_in_abundance.html @@ -0,0 +1,160 @@ + +enrichment_in_abundance — enrichment_in_abundance • leapR + + +
    +
    + + + +
    +
    +
    +

    enrichment_in_abundance

    + + +
    + +
    +

    Enrichment in abundance calculates enrichment in pathways by the difference +in abundance of the pathway members.

    +
    + +
    + 
    enrichment_in_abundance(
+  geneset,
+  eset,
+  assay_name,
+  mapping_column = NULL,
+  abundance_column = NULL,
+  fdr = 0,
+  matchset = NULL,
+  sample_comparison = NULL,
+  min_p_threshold = NULL,
+  sample_n = NULL,
+  silence_try_errors = TRUE
+)
    +
    + +
    +

    Arguments

    + + +
    geneset
    +

    Gene set to calculate enrichment

    + + +
    eset
    +

    Molecular abundance data in `SummarizedExperiment` format

    + + +
    assay_name
    +

    Name of assay to compare

    + + +
    mapping_column
    +

    Column to use to map identifiers

    + + +
    abundance_column
    +

    Columns to use to quantify abundance

    + + +
    fdr
    +

    number of times to sample for FDR value

    + + +
    matchset
    +

    Name of a set to use for enrichment

    + + +
    sample_comparison
    +

    list of samples to use as comparison. if missing +background (eset) is used

    + + +
    min_p_threshold
    +

    Only include p-values lower than this

    + + +
    sample_n
    +

    size of sample to use

    + + +
    silence_try_errors
    +

    set to true to silence try errors

    + +
    +
    +

    Value

    +

    data frame of enrichment result

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/enrichment_in_groups.html b/docs/reference/enrichment_in_groups.html new file mode 100644 index 0000000..18a646b --- /dev/null +++ b/docs/reference/enrichment_in_groups.html @@ -0,0 +1,160 @@ + +enrichment_in_groups — enrichment_in_groups • leapR + + +
    +
    + + + +
    +
    +
    +

    enrichment_in_groups

    + + +
    + +
    +

    Calculate the enrichment in pathways using Fisher's exact or +Kolmogorov-Smirnov test, using either the abundance column to identify +feature or the targets list. access through leapr wrapper

    +
    + +
    + 
    enrichment_in_groups(
+  geneset,
+  targets = c(),
+  background = NULL,
+  assay_name = NULL,
+  method = "fishers",
+  minsize = 5,
+  mapping_column = NULL,
+  abundance_column = NULL,
+  randomize = FALSE,
+  silence_try_errors = TRUE
+)
    +
    + +
    +

    Arguments

    + + +
    geneset
    +

    geneset to use for enrichment

    + + +
    targets
    +

    targets to use for enrichment

    + + +
    background
    +

    `SummarizedExperiment` describing background to use

    + + +
    assay_name
    +

    is the name of the assay to use from the background

    + + +
    method
    +

    method to use for statistical test, options are +'fishers', 'ks', 'ztest', or 'chisq'. Remember that KS test assumes normality, so it would be good +to log your data before calling. NOTE: if you do not call `suppressWarnings` then +the KS test will warn you about ties.

    + + +
    minsize
    +

    minimum size of set

    + + +
    mapping_column
    +

    column name of mapping identifiers

    + + +
    abundance_column
    +

    columns mapping abundance, either in the `assay` +matrix or `rowData`

    + + +
    randomize
    +

    true/false whether to randomize

    + + +
    silence_try_errors
    +

    true/false to silence errors

    + +
    +
    +

    Value

    +

    data frame with enrichment results

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/enrichment_in_relationships.html b/docs/reference/enrichment_in_relationships.html new file mode 100644 index 0000000..cdf54ca --- /dev/null +++ b/docs/reference/enrichment_in_relationships.html @@ -0,0 +1,129 @@ + +enrichment_in_relationships — enrichment_in_relationships • leapR + + +
    +
    + + + +
    +
    +
    +

    enrichment_in_relationships

    + + +
    + +
    +

    enrichment_in_relationships function description is a general way to +determine if a pathway +is enriched in relationships (interactions, correlation) between its members +# access through leapr wrapper

    +
    + +
    + 
    enrichment_in_relationships(
+  geneset,
+  relationships,
+  idmap = NA,
+  silence_try_errors = TRUE
+)
    +
    + +
    +

    Arguments

    + + +
    geneset
    +

    List of pathways in gmt format

    + + +
    relationships
    +

    table of relationship information, e.g. correlation

    + + +
    idmap
    +

    list of identifiers to use for mapping, the names of the items +should agree with names of features in matrix

    + + +
    silence_try_errors
    +

    boolean to silence errors

    + +
    +
    +

    Value

    +

    table of enrichment statistics

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/get_pathway_information.html b/docs/reference/get_pathway_information.html new file mode 100644 index 0000000..d3ba365 --- /dev/null +++ b/docs/reference/get_pathway_information.html @@ -0,0 +1,128 @@ + +get_pathway_information — get_pathway_information • leapR + + +
    +
    + + + +
    +
    +
    +

    get_pathway_information

    + + +
    + +
    +

    get_pathway_information extracts information about a pathway from a +GeneSet object

    +
    + +
    + 
    get_pathway_information(geneset, path, remove.tags = FALSE)
    +
    + +
    +

    Arguments

    + + +
    geneset
    +

    is a GeneSet object for pathway annotation

    + + +
    path
    +

    is the name of the gene set pathway to be return

    + + +
    remove.tags
    +

    boolean indicating whether to remove tags

    + +
    +
    +

    Value

    +

    list of pathway information

    +
    + +
    +

    Examples

    + 
    
+     library(leapR)
+
+     # load example gene set
+     data("ncipid")
+
+     tnfpathway = get_pathway_information(ncipid, "tnfpathway")
+
+
+
    +
    +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 0000000..bbdc585 --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,155 @@ + +Package index • leapR + + +
    +
    + + + +
    +
    +
    +

    Reference

    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    All functions

    +

    +
    +

    calcTTest()

    +

    calcTTest

    +

    cluster_enrichment()

    +

    cluster_enrichment

    +

    combine_omics()

    +

    combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

    +

    correlation_comparison_enrichment()

    +

    correlation_comparison_enrichment

    +

    correlation_enrichment()

    +

    correlation_enrichment

    +

    enrichment_in_abundance()

    +

    enrichment_in_abundance

    +

    enrichment_in_groups()

    +

    enrichment_in_groups

    +

    enrichment_in_relationships()

    +

    enrichment_in_relationships

    +

    get_pathway_information()

    +

    get_pathway_information

    +

    kinasesubstrates

    +

    Kinase substrate lists

    +

    krbpaths

    +

    KEGG, Reactome, BioCarta Pathways

    +

    leapR()

    +

    leapR

    +

    longlist

    +

    Long list of patient samples

    +

    ncipid

    +

    NCI Gene lists

    +

    plot_leapr_bar()

    +

    Plot leapR pathway bars (single panel)

    +

    read_gene_sets()

    +

    read_gene_sets

    +

    shortlist

    +

    A list of pathways and genes that comprise these pathways from msigdb

    + + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/kinasesubstrates.html b/docs/reference/kinasesubstrates.html new file mode 100644 index 0000000..9f22087 --- /dev/null +++ b/docs/reference/kinasesubstrates.html @@ -0,0 +1,113 @@ + +Kinase substrate lists — kinasesubstrates • leapR + + +
    +
    + + + +
    +
    +
    +

    Kinase substrate lists

    + + +
    + +
    +

    Kinase substrate lists

    +
    + +
    + 
    kinasesubstrates
    +
    + +
    +

    Format

    +

    A list with 4 items

    names
    +

    The names of the kinases

    + +
    desc
    +

    Short description of the kinase

    + +
    sizes
    +

    Length of the substrate list

    + +
    matrix
    +

    Substrate list for the kinase

    + + +
    +
    +

    Source

    +

    PhosphositePlus

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/krbpaths.html b/docs/reference/krbpaths.html new file mode 100644 index 0000000..05f0480 --- /dev/null +++ b/docs/reference/krbpaths.html @@ -0,0 +1,113 @@ + +KEGG, Reactome, BioCarta Pathways — krbpaths • leapR + + +
    +
    + + + +
    +
    +
    +

    KEGG, Reactome, BioCarta Pathways

    + + +
    + +
    +

    KEGG, Reactome, BioCarta Pathways

    +
    + +
    + 
    krbpaths
    +
    + +
    +

    Format

    +

    A list with 4 items

    names
    +

    The names of the pathways

    + +
    desc
    +

    Short description of the pathways

    + +
    sizes
    +

    Number of genes in the signaling pathways

    + +
    matrix
    +

    Matrix containing the genes in the pathways

    + + +
    +
    +

    Source

    +

    https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/leapR-package.html b/docs/reference/leapR-package.html new file mode 100644 index 0000000..7a41a1e --- /dev/null +++ b/docs/reference/leapR-package.html @@ -0,0 +1,97 @@ + +leapR: Layered enrichment analysis of pathways R — leapR-package • leapR + + +
    +
    + + + +
    +
    +
    +

    leapR: Layered enrichment analysis of pathways R

    + + +
    + +
    +

    leapR is a package that identifies pathways that are enriched across diverse 'omics experiments. It leverages any tabular expression data (proteomics, transcriptomics) using the `SummarizedExperiment` object. It works with any pathway in the .gct file format.

    +
    + + +
    +

    Author

    +

    Maintainer: Sara Gosline sara.gosline@pnnl.gov (ORCID)

    +

    Authors:

    Other contributors:

    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/leapR.html b/docs/reference/leapR.html new file mode 100644 index 0000000..f2ff791 --- /dev/null +++ b/docs/reference/leapR.html @@ -0,0 +1,340 @@ + +leapR — leapR • leapR + + +
    +
    + + + +
    +
    +
    +

    leapR

    + + +
    + +
    +

    leapR is a wrapper function that consolidates multiple enrichment methods.

    +
    + +
    + 
    leapR(geneset, enrichment_method, eset, assay_name, ...)
    +
    + +
    +

    Arguments

    + + +
    geneset
    +

    is a list of four vectors, gene names, gene descriptions, gene +sizes and a matrix of genes. It represents .gmt format pathway files.

    + + +
    enrichment_method
    +

    is a character string specifying the method of +enrichment to be performed, one of: "enrichment_comparison", +"enrichment_in_order", "enrichment_in_sets", "enrichment_in_pathway", +"correlation_enrichment".

    + + +
    eset
    +

    is a `SummarizedExperiment` object containing expression data, +with features as rows and n sample/conditions as columns.

    + + +
    assay_name
    +

    is the assay to be analyzed within the `eset`. +Recommended to describe the data type (e.g. transcriptomics, proteomics) +so that it can be integrated in `combine_omics`

    + + +
    ...
    +

    further arguments

    + +
    +
    +

    Value

    +

    data frame with results

    +
    +
    +

    Details

    +

    Further arguments and enrichment method optional argument information:

    id_columnIs a character string, present in the rowData slot, +that is used to specify a column for identifiers to map to enrichment +libraries. +If missing, the rownames of the SummarizedExperiment assay will be used.
    primary_columnsIs a character vector composed of column names from +eset (either in the `assay` or in the `rowData`), +that specifies a set of primary columns to calculate enrichment on. +The meaning of this varies according to the enrichment method used - see +the descriptions for each method below. +This is an optional argument used with 'enrichment_in_order', +'enrichment_in_sets', and 'enrichment_comparison' methods.
    secondary_columns
    Is a character vector of column names for comparison, +pulled from the `assay` of the SummarizedExperiment. This is an +optional argument used with 'enrichment_comparison' methods.
    thresholdIs a numeric value, an optional argument used with +'enrichment_in sets' method which filters out abundance values or p-values +(depending on what `primary_columns` is used) +either above or below it.
    greaterthan
    Is a logical value that defaults to TRUE, it's used with +'enrichment_in_sets' method. +When set to TRUE, genes with `primary_columns` value above the +threshold argument are kept. +When set to FALSE genes with `primary_columns` value below the +threshold argument are kept. +This is an optional argument used with 'enrichment_in_sets' method.
    minsizeIs a numeric value, an optional argument used with +'enrichment_in_sets' and 'enrichment_in_order".
    fdr
    A numerical value which specifies how many times to randomly +sample genes to calculate an empirical false discovery rate, is an optional +argument used with 'enrichment_comparison' method.
    min_p_thresholdIs a numeric value, a lower p-value threshold and is an +optional argument used with 'enrichment_comparison' method.
    sample_n
    Is a way to subsample the number of components considered for +each calculation randomly. This is an optional argument used with +'enrichment_comparison' method.

    Enrichment Methods: +

    +enrichment_comparison +
    +Compares the distribution of abundances between two sets of +conditions for each pathway using a t test. For each pathway in +geneset uses a t test to compare the distribution of abundance +values/numbers in eset primary_columns with those in +eset secondary_columns. Lower p-values for pathways indicate +that the expression of the pathway is significantly different between the +set of conditions in primary_columns and the set of conditions in +secondary_columns. +Optionally, users can specify fdr which will calculate an empirical +p-value by randomizing abundances fdr number of times. If the +min_p_threshold is specified the method will only return pathways +with an adjusted p-value lower than the specified threshold. If +sample_n is specified the method will subsample the +pathway members to the specified number of components. +

    +enrichment_in_order +
    +Calculates enrichment of pathways based on a ranked list using the +Kolmogorov-Smirnov test. For each pathway in geneset uses a +Kolmogorov-Smirnov test for rank order to test if the distribution of ranked +abundance values in the eset primary_columns is significant +relative to a random distribution. Note that currently +primary_columns only accepts a single column for this method. +

    +enrichment_in_sets +
    +Calculates enrichment in pathway membership in a list (e.g. highly +differential proteins) relative to background using Fisher's exact test. For +each pathway in geneset uses a Fisher's exact test over- or under- +representation of a list of components specified. If targets are +specified this must be a vector of identifiers to serve as the target list +for comparison. If eset and primary_columns are specified then +threshold specifies a threshold value for determining the target list +of components to test. Specifying greaterthan to be False +will result in components with values lower than the specified +threshold. If eset is a data frame or matrix, the background +used for calculation will be taken as the rownames of eset +

    +enrichment_in_pathway +
    +Compares the distribution of abundances in a pathway with the background +distribution of abundances using a t test. For each pathway in +geneset calculates the significance of the difference between the +abundances from pathway members versus abundance of non-pathway members in +the set of conditions specified by primary_columns. Optionally, users +can specify fdr which will calculate an empirical p-value by +randomizing abundances fdr number of times. If the +min_p_threshold is specified the method will only return pathways +with an adjusted p-value lower than the specified threshold. If +sample_n is specified the method will subsample the +pathway members to the specified number of components. +

    +correlation_enrichment +
    +Calculates the enrichment of a pathway based on correlation between pathway +members across conditions versus correlation between members not in the +pathway. For each pathway in geneset calculates the pairwise +correlation between all pathway members and non-pathway members +across the specified primary_columns conditions in eset. Note +that for large matrices this can take a long time. A p-value is calculated +based on comparing the correlation within the members of a pathway with the +correlation values between members of the pathway and non-members of the +pathway. +

    +
    + +
    +

    Examples

    + 
            library(leapR)
+
+ # read in the example abundance data
+ # read in the example transcriptomic data
+ tdata <- download.file("https://api.figshare.com/v2/file/download/56536214",
+      method='libcurl',destfile='transData.rda')
+ load('transData.rda')
+ p <- file.remove("transData.rda")
+
+ # read in the pathways
+ data("ncipid")
+
+ # read in the patient groups
+ data("shortlist")
+ data("longlist")
+
+ # use enrichment_comparison to calculate enrichment in one set of
+ # conditions (shortlist) and another (longlist)
+ short_v_long = leapR(geneset=ncipid, assay_name='transcriptomics',
+              enrichment_method='enrichment_comparison',
+              eset=tset, primary_columns=shortlist,
+               secondary_columns=longlist)
+
+ # use enrichment_in_sets to calculate the most enriched pathways
+ # from the highest abundance proteins
+ #     from one condition
+ onept_sets = leapR(geneset=ncipid, assay_name='transcriptomics',
+               enrichment_method='enrichment_in_sets',
+               eset=tset, primary_columns="TCGA-13-1484", threshold=1.5)
+
+ # use enrichment_in_order to calculate the most enriched pathways from the
+ # same condition
+ # Note: that this uses the entire set of abundance values and their order -
+ # whereas the previous example uses a hard threshold to get a short list of
+ # most abundant proteins and calculates enrichment based on set overlap.
+ # The results are likely to be similar - but with some notable differences.
+ onept_order = leapR(geneset=ncipid, assay_name='transcriptomics',
+               enrichment_method='enrichment_in_order',
+               eset=tset, primary_columns="TCGA-13-1484")
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+#> Warning: p-value will be approximate in the presence of ties
+
+ # use enrichment_in_pathway to calculate the most enriched pathways in a
+ # set of conditions based on abundance in the pathway members versus
+ # abundance in non-pathway members
+ short_pathways = leapR(geneset=ncipid, assay_name='transcriptomics',
+               enrichment_method='enrichment_in_pathway',
+               eset=tset, primary_columns=shortlist)
+
+ # use correlation_enrichment to calculate the most enriched pathways in
+ # correlation across the shortlist conditions
+ short_correlation_pathways = leapR(geneset=ncipid,
+                assay_name='transcriptomics',
+                enrichment_method='correlation_enrichment',
+                eset=tset, primary_columns=shortlist)
+
+
+
    +
    +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/longlist.html b/docs/reference/longlist.html new file mode 100644 index 0000000..422310c --- /dev/null +++ b/docs/reference/longlist.html @@ -0,0 +1,97 @@ + +Long list of patient samples — longlist • leapR + + +
    +
    + + + +
    +
    +
    +

    Long list of patient samples

    + + +
    + +
    +

    Long list of patient samples

    +
    + +
    + 
    longlist
    +
    + +
    +

    Format

    +

    An object of class character of length 37.

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/ncipid.html b/docs/reference/ncipid.html new file mode 100644 index 0000000..876449e --- /dev/null +++ b/docs/reference/ncipid.html @@ -0,0 +1,113 @@ + +NCI Gene lists — ncipid • leapR + + +
    +
    + + + +
    +
    +
    +

    NCI Gene lists

    + + +
    + +
    +

    A list of pathways and the genes that comprise these pathways

    +
    + +
    + 
    ncipid
    +
    + +
    +

    Format

    +

    A list with 4 items

    names
    +

    The names of the signaling pathways

    + +
    desc
    +

    Short description of the pathways

    + +
    sizes
    +

    Number of genes in the signaling pathways

    + +
    matrix
    +

    Matrix containing the genes in the pathways

    + + +
    +
    +

    Source

    +

    NCIPID

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/plot_leapr_bar.html b/docs/reference/plot_leapr_bar.html new file mode 100644 index 0000000..36df698 --- /dev/null +++ b/docs/reference/plot_leapr_bar.html @@ -0,0 +1,159 @@ + +Plot leapR pathway bars (single panel) — plot_leapr_bar • leapR + + +
    +
    + + + +
    +
    +
    +

    Plot leapR pathway bars (single panel)

    + + +
    + +
    +

    This plotting helper expects leapR generated results to plot. +It will use BH_pvalue if present, otherwise pvalue.

    +
    + +
    + 
    plot_leapr_bar(
+  res_df,
+  title = NULL,
+  top_n = 15,
+  star_thresholds = c(0.05, 0.01, 0.001),
+  wrap = 42,
+  max_stars = 5L,
+  fill_sig = "#2C7BB6",
+  fill_ns = "#BFD7FF",
+  outline = NA,
+  axis_text_y_size = 8,
+  axis_text_x_size = 9
+)
    +
    + +
    +

    Arguments

    + + +
    res_df
    +

    A leapR df containing BH_pvalue (or pvalue) and a pathway/term label column.

    + + +
    title
    +

    Plot title.

    + + +
    top_n
    +

    Number of top pathways/genes to display.

    + + +
    star_thresholds
    +

    list of numeric significance thresholds for star annotations.

    + + +
    wrap
    +

    Wrap width for pathway labels (helps formatting).

    + + +
    max_stars
    +

    Maximum number of stars to draw per bar (default 5).

    + + +
    fill_sig
    +

    Fill color for significant bars

    + + +
    fill_ns
    +

    Fill color for non-significant bars.

    + + +
    outline
    +

    Bar border color.

    + + +
    axis_text_y_size
    +

    Font size for y-axis (category) labels.

    + + +
    axis_text_x_size
    +

    Font size for x-axis (numeric) labels.

    + +
    +
    +

    Value

    +

    A ggplot2 object (or NULL if nothing to plot).

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/read_gene_sets.html b/docs/reference/read_gene_sets.html new file mode 100644 index 0000000..c7aba8f --- /dev/null +++ b/docs/reference/read_gene_sets.html @@ -0,0 +1,134 @@ + +read_gene_sets — read_gene_sets • leapR + + +
    +
    + + + +
    +
    +
    +

    read_gene_sets

    + + +
    + +
    +

    read_gene_sets is a function to import external pathway +database files in .gmt format

    +
    + +
    + 
    read_gene_sets(
+  gsfile,
+  gene.labels = NA,
+  gs.size.threshold.min = 5,
+  gs.size.threshold.max = 15000
+)
    +
    + +
    +

    Arguments

    + + +
    gsfile
    +

    is a gene set file, for example a .gmt file +(gene matrix transposed file format)

    + + +
    gene.labels
    +

    defaults to NA

    + + +
    gs.size.threshold.min
    +

    defaults to 5

    + + +
    gs.size.threshold.max
    +

    defaults to 15000

    + +
    +
    +

    Value

    +

    gene set object

    +

    geneset list

    +
    + +
    +

    Examples

    + 
    gfile <- system.file('extdata','h.all.v2024.1.Hs.symbols.gmt',
+          package='leapR')
+glist <- read_gene_sets(gfile)
+
+
    +
    +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/docs/reference/shortlist.html b/docs/reference/shortlist.html new file mode 100644 index 0000000..3443649 --- /dev/null +++ b/docs/reference/shortlist.html @@ -0,0 +1,98 @@ + +A list of pathways and genes that comprise these pathways from msigdb — shortlist • leapR + + +
    +
    + + + +
    +
    +
    +

    A list of pathways and genes that comprise these pathways from msigdb

    + + +
    + +
    +

    A list of pathways and genes that comprise these pathways from msigdb

    +
    + +
    + 
    shortlist
    +
    + +
    +

    Format

    +

    a list with 4 items

    +

    Short list of patient samples

    +
    + +
    + +
    + + +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    + +
    +

    Site built with pkgdown 2.2.0.

    +
    + +
    + + + + + + + + diff --git a/vignettes/order-enrichment.Rmd b/vignettes/order-enrichment.Rmd new file mode 100644 index 0000000..a3159c9 --- /dev/null +++ b/vignettes/order-enrichment.Rmd @@ -0,0 +1,126 @@ +--- +title: "leapR Order Enrichment Tests" +output: + BiocStyle::html_document: + toc: true + toc_depth: 2 +vignette: | + %\VignetteIndexEntry{leapR} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r knitr, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + cache = FALSE +) +``` + + +# Load libraries needed + +```{r libraries, message=FALSE, warning = FALSE} +# load the core libraries +library(leapR) +library(gplots) +library(rmarkdown) +# plotting helpers used in this vignette +library(ggplot2) +library(dplyr) +library(tibble) +library(stringr) +``` + + +# Load our test proteomics dataset +```{r load data} +url <- "https://api.figshare.com/v2/file/download/56536217" +pdata <- download.file(url, method = "libcurl", destfile = "protData.rda") +# as.matrix() +load("protData.rda") + +p <- file.remove("protData.rda") + +data(shortlist) +data(longlist) + +## columns that we want to use for results + +cols_to_display <- c("ingroup_n", "outgroup_n", "background_n", + "pvalue", "BH_pvalue") +``` + +# Compare enrichment methods +We want to evaluate how various samples compare in enrichmnet + +```{r compare} + +i = 8 + +cor.res <- do.call(rbind,lapply(1:length(shortlist), function (i) { + protdata.enrichment.ks <- leapR::leapR( + geneset = ncipid, "enrichment_in_order", + eset = pset, + minsize = 5, + assay_name = "proteomics", + primary_columns = shortlist[i] + ) + + colnames(protdata.enrichment.ks) <- paste('ks',colnames(protdata.enrichment.ks),sep='.') + + + protdata.enrichment.cs <- leapR::leapR( + geneset = ncipid, "enrichment_in_order", + method = 'chisq', + eset = pset, minsize = 5, + assay_name = "proteomics", + primary_columns = shortlist[i] + ) + colnames(protdata.enrichment.cs) <- paste('chisq',colnames(protdata.enrichment.cs),sep='.') + + protdata.enrichment.zt <- leapR::leapR( + geneset = ncipid, "enrichment_in_order", + eset = pset, minsize = 5, + method = 'ztest', + assay_name = "proteomics", + primary_columns = shortlist[i] + ) + + colnames(protdata.enrichment.zt) <- paste('ztest',colnames(protdata.enrichment.zt),sep='.') + + paths <- rownames(protdata.enrichment.ks) + allvals <- cbind(protdata.enrichment.cs[paths,], + protdata.enrichment.ks[paths,], + protdata.enrichment.zt[paths,]) + + kz <- cor(allvals$ks.BH_pvalue, allvals$ztest.BH_pvalue, use = 'p') + + kc <- cor(allvals$ks.BH_pvalue, allvals$chisq.BH_pvalue, use = 'p') + + cz <- cor(allvals$chisq.BH_pvalue, allvals$ztest.BH_pvalue, use = 'p') + + kz <- cor(allvals$ks.pvalue, allvals$ztest.pvalue, use = 'p') + + kc <- cor(allvals$ks.pvalue, allvals$chisq.pvalue, use = 'p') + + cz <- cor(allvals$chisq.pvalue, allvals$ztest.pvalue, use = 'p') + + return(c(kz = kc, kc=kc, cz = cz)) +})) + +``` + +We can compare the corrected p-values + +```{r compare pvals} +library(ggplot2) + +cor.res |> as.data.frame() |> + tidyr::pivot_longer(c(1:3),names_to='test_comparison',values_to='p cor') |> + ggplot(aes(x=test_comparison, y= `p cor`)) + geom_jitter() + + + +``` \ No newline at end of file From cf251ca76940ff93207e55096d7853fccea99433 Mon Sep 17 00:00:00 2001 From: Sara JC Gosline Date: Wed, 11 Feb 2026 10:45:32 -0800 Subject: [PATCH 2/3] add pkgdown --- .Rbuildignore | 3 + .github/workflows/pkgdown.yaml | 2 +- .gitignore | 1 + _pkgdown.yml | 4 + docs/404.html | 124 +++--- docs/LICENSE-text.html | 99 ++--- docs/articles/examples.html | 123 +++--- docs/articles/index.html | 94 ++--- docs/articles/leapR.html | 122 +++--- docs/authors.html | 116 +++--- docs/bootstrap-toc.css | 60 --- docs/bootstrap-toc.js | 159 -------- docs/docsearch.css | 148 ------- docs/docsearch.js | 85 ---- docs/index.html | 123 +++--- docs/pkgdown.css | 384 ------------------ docs/pkgdown.js | 218 ++++++---- docs/pkgdown.yml | 5 +- docs/reference/calcTTest.html | 121 +++--- docs/reference/cluster_enrichment.html | 124 +++--- docs/reference/combine_omics.html | 130 +++--- .../correlation_comparison_enrichment.html | 117 +++--- docs/reference/correlation_enrichment.html | 116 +++--- docs/reference/enrichment_in_abundance.html | 116 +++--- docs/reference/enrichment_in_groups.html | 117 +++--- .../enrichment_in_relationships.html | 118 +++--- docs/reference/get_pathway_information.html | 120 +++--- docs/reference/index.html | 299 ++++++++------ docs/reference/kinasesubstrates.html | 113 ++---- docs/reference/krbpaths.html | 113 ++---- docs/reference/leapR-package.html | 105 ++--- docs/reference/leapR.html | 121 +++--- docs/reference/longlist.html | 109 ++--- docs/reference/ncipid.html | 113 ++---- docs/reference/plot_leapr_bar.html | 116 +++--- docs/reference/read_gene_sets.html | 120 +++--- docs/reference/shortlist.html | 109 ++--- vignettes/order-enrichment.Rmd | 1 + 38 files changed, 1468 insertions(+), 2800 deletions(-) create mode 100644 _pkgdown.yml delete mode 100644 docs/bootstrap-toc.css delete mode 100644 docs/bootstrap-toc.js delete mode 100644 docs/docsearch.css delete mode 100644 docs/docsearch.js delete mode 100644 docs/pkgdown.css diff --git a/.Rbuildignore b/.Rbuildignore index 9ad5db4..0e5321c 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -5,3 +5,6 @@ ^.*\.gitignore ^LICENSE\.md$ ^.git*$ +^_pkgdown\.yml$ +^docs$ +^pkgdown$ diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml index bfc9f4d..cea5906 100644 --- a/.github/workflows/pkgdown.yaml +++ b/.github/workflows/pkgdown.yaml @@ -2,7 +2,7 @@ # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: [main, master] + branches: main pull_request: release: types: [published] diff --git a/.gitignore b/.gitignore index a1046bb..4c37638 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ doc Meta inst/doc +docs diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..69b0979 --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,4 @@ +url: https://pnnl-github.io/leapR +template: + bootstrap: 5 + diff --git a/docs/404.html b/docs/404.html index 52be5b5..a4be44e 100644 --- a/docs/404.html +++ b/docs/404.html @@ -4,111 +4,79 @@ - + Page not found (404) • leapR - - - - - - - + + + + + - - - -
    -
    -
    -

    Page not found (404)

    +
    Content not found. Please use links in the navbar. -
    - - - +
    - -
    -

    -

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    -
    -

    -

    Site built with pkgdown 2.2.0.

    +
    +

    Site built with pkgdown 2.2.0.

    -
    +
    - diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 7599ea4..b910367 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -1,59 +1,40 @@ -License • leapR - - -
    -
    +
    +
    +
    -
    -
    -

    License

    + 
    Copyright 2025 Battelle Memorial Institute
@@ -87,26 +68,18 @@ 
    License
    
 POSSIBILITY OF SUCH DAMAGE.
    -
    +
    - +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    - - -
    -

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    +

    Site built with pkgdown 2.2.0.

    -
    -

    Site built with pkgdown 2.2.0.

    -
    - -
    - + diff --git a/docs/articles/examples.html b/docs/articles/examples.html index 54af499..0efac56 100644 --- a/docs/articles/examples.html +++ b/docs/articles/examples.html @@ -4,84 +4,64 @@ - + leapR Paper Examples • leapR - - - - - - - + + + + + - + + Skip to contents -
    -
    -
    -
    -

    leapR Paper Examples

    +

    leapR Paper Examples

    - - +
    examples.Rmd
    @@ -486,33 +466,26 @@

    Figure 2)

    -
    - - - + - - diff --git a/docs/articles/index.html b/docs/articles/index.html index 66ae0b5..b6a94b0 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,64 +1,44 @@ -Articles • leapR - - -
    -
    +
    +
    +
    -
    -
    -

    Articles

    All vignettes

    -

    +
    leapR Paper Examples
    @@ -67,20 +47,18 @@

    All vignettes

    leapR Order Enrichment Tests
    -
    -
    +
    -
    -

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    -
    -

    Site built with pkgdown 2.2.0.

    +
    +

    Site built with pkgdown 2.2.0.

    -
    - +
    diff --git a/docs/articles/leapR.html b/docs/articles/leapR.html index 768bdd2..1ef5e6b 100644 --- a/docs/articles/leapR.html +++ b/docs/articles/leapR.html @@ -4,85 +4,64 @@ - + leapR • leapR - - - - - - - + + + + + - + + Skip to contents -
    -
    +
    -
    -
    -

    leapR

    +
    + +

    leapR

    - - +
    leapR.Rmd
    @@ -837,33 +816,26 @@
    Visualizing kinase substrate en
    -
    - - - + -
    -

    -

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    -
    -

    -

    Site built with pkgdown 2.2.0.

    +
    +

    Site built with pkgdown 2.2.0.

    -
    +
    - diff --git a/docs/authors.html b/docs/authors.html index b06fd3b..6eead4c 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -1,62 +1,43 @@ -Authors and Citation • leapR - - -
    -
    +
    +
    +
    -
    -
    -
    -
    -

    Authors and Citation

    -
    +

    Authors and Citation

    +
    +
    +

    Authors

    • Sara Gosline. Author, maintainer. @@ -79,41 +60,36 @@

      Authors and Citation

    -
    -
    -

    Citation

    - -
    -
    +
    +

    Citation

    +

    -

    Gosline S, McDermott J, Jacobson J (2026). +

    Gosline S, McDermott J, Jacobson J (2026). leapR: Layered enrichment analysis of pathways R. R package version 0.99.7.

    - 
    @Manual{,
+      
    @Manual{,
   title = {leapR: Layered enrichment analysis of pathways R},
   author = {Sara Gosline and Jason McDermott and Jeremy Jacobson},
   year = {2026},
   note = {R package version 0.99.7},
 }
    
+
    -
    - -
    - +
    -
    -

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    +
    +

    Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

    -
    -

    Site built with pkgdown 2.2.0.

    +
    +

    Site built with pkgdown 2.2.0.

    -
    - +
    diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css deleted file mode 100644 index 5a85941..0000000 --- a/docs/bootstrap-toc.css +++ /dev/null @@ -1,60 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ - -/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ - -/* All levels of nav */ -nav[data-toggle='toc'] .nav > li > a { - display: block; - padding: 4px 20px; - font-size: 13px; - font-weight: 500; - color: #767676; -} -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 19px; - color: #563d7c; - text-decoration: none; - background-color: transparent; - border-left: 1px solid #563d7c; -} -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 18px; - font-weight: bold; - color: #563d7c; - background-color: transparent; - border-left: 2px solid #563d7c; -} - -/* Nav: second level (shown on .active) */ -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} -nav[data-toggle='toc'] .nav .nav > li > a { - padding-top: 1px; - padding-bottom: 1px; - padding-left: 30px; - font-size: 12px; - font-weight: normal; -} -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 29px; -} -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 28px; - font-weight: 500; -} - -/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ -nav[data-toggle='toc'] .nav > .active > ul { - display: block; -} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js deleted file mode 100644 index 1cdd573..0000000 --- a/docs/bootstrap-toc.js +++ /dev/null @@ -1,159 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ -(function() { - 'use strict'; - - window.Toc = { - helpers: { - // return all matching elements in the set, or their descendants - findOrFilter: function($el, selector) { - // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ - // http://stackoverflow.com/a/12731439/358804 - var $descendants = $el.find(selector); - return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); - }, - - generateUniqueIdBase: function(el) { - var text = $(el).text(); - var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); - return anchor || el.tagName.toLowerCase(); - }, - - generateUniqueId: function(el) { - var anchorBase = this.generateUniqueIdBase(el); - for (var i = 0; ; i++) { - var anchor = anchorBase; - if (i > 0) { - // add suffix - anchor += '-' + i; - } - // check if ID already exists - if (!document.getElementById(anchor)) { - return anchor; - } - } - }, - - generateAnchor: function(el) { - if (el.id) { - return el.id; - } else { - var anchor = this.generateUniqueId(el); - el.id = anchor; - return anchor; - } - }, - - createNavList: function() { - return $('
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- } // else use the current $context - - $context.append($newNav); - - $prevNav = $newNav; - }); - }, - - parseOps: function(arg) { - var opts; - if (arg.jquery) { - opts = { - $nav: arg - }; - } else { - opts = arg; - } - opts.$scope = opts.$scope || $(document.body); - return opts; - } - }, - - // accepts a jQuery object, or an options object - init: function(opts) { - opts = this.helpers.parseOps(opts); - - // ensure that the data attribute is in place for styling - opts.$nav.attr('data-toggle', 'toc'); - - var $topContext = this.helpers.createChildNavList(opts.$nav); - var topLevel = this.helpers.getTopLevel(opts.$scope); - var $headings = this.helpers.getHeadings(opts.$scope, topLevel); - this.helpers.populateNav($topContext, topLevel, $headings); - } - }; - - $(function() { - $('nav[data-toggle="toc"]').each(function(i, el) { - var $nav = $(el); - Toc.init($nav); - }); - }); -})(); diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; -} - -.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { - margin-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion { - padding: 0; - overflow: visible -} - -.algolia-autocomplete .algolia-docsearch-suggestion--category-header { - padding: .125rem 1rem; - margin-top: 0; - font-size: 1.3em; - font-weight: 500; - color: #00008B; - border-bottom: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { - float: none; - padding-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { - float: none; - width: auto; - padding: 0; - text-align: left -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content { - float: none; - width: auto; - padding: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content::before { - display: none -} - -.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { - padding-top: .75rem; - margin-top: .75rem; - border-top: 1px solid rgba(0, 0, 0, .1) -} - -.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { - display: block; - padding: .1rem 1rem; - margin-bottom: 0.1; - font-size: 1.0em; - font-weight: 400 - /* display: none */ -} - -.algolia-autocomplete .algolia-docsearch-suggestion--title { - display: block; - padding: .25rem 1rem; - margin-bottom: 0; - font-size: 0.9em; - font-weight: 400 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--text { - padding: 0 1rem .5rem; - margin-top: -.25rem; - font-size: 0.8em; - font-weight: 400; - line-height: 1.25 -} - -.algolia-autocomplete .algolia-docsearch-footer { - width: 110px; - height: 20px; - z-index: 3; - margin-top: 10.66667px; - float: right; - font-size: 0; - line-height: 0; -} - -.algolia-autocomplete .algolia-docsearch-footer--logo { - background-image: url("data:image/svg+xml;utf8,"); - background-repeat: no-repeat; - background-position: 50%; - background-size: 100%; - overflow: hidden; - text-indent: -9000px; - width: 100%; - height: 100%; - display: block; - transform: translate(-8px); -} - -.algolia-autocomplete .algolia-docsearch-suggestion--highlight { - color: #FF8C00; - background: rgba(232, 189, 54, 0.1) -} - - -.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { - box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) -} - -.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { - background-color: rgba(192, 192, 192, .15) -} diff --git a/docs/docsearch.js b/docs/docsearch.js deleted file mode 100644 index b35504c..0000000 --- a/docs/docsearch.js +++ /dev/null @@ -1,85 +0,0 @@ -$(function() { - - // register a handler to move the focus to the search bar - // upon pressing shift + "/" (i.e. "?") - $(document).on('keydown', function(e) { - if (e.shiftKey && e.keyCode == 191) { - e.preventDefault(); - $("#search-input").focus(); - } - }); - - $(document).ready(function() { - // do keyword highlighting - /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ - var mark = function() { - - var referrer = document.URL ; - var paramKey = "q" ; - - if (referrer.indexOf("?") !== -1) { - var qs = referrer.substr(referrer.indexOf('?') + 1); - var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html index 877fbd4..72b8497 100644 --- a/docs/index.html +++ b/docs/index.html @@ -4,79 +4,56 @@ - + Layered enrichment analysis of pathways R • leapR - - - - - - + + + + + + - - - - -
      -
      -
      -
      +
      +
      +
      +

      leapR

      Layered Enrichment Analysis of Pathways in R (leapR) a tool that carries out statistical enrichment analysis on single- or multi-omics data.

      @@ -148,10 +125,7 @@

      Gene pathway examples

      -
      - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index 80ea5b8..0000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,384 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; -} -footer .pkgdown { - text-align: right; -} -footer p { - margin-bottom: 0; -} - -img.icon { - float: right; -} - -/* Ensure in-page images don't run outside their container */ -.contents img { - max-width: 100%; - height: auto; -} - -/* Fix bug in bootstrap (only seen in firefox) */ -summary { - display: list-item; -} - -/* Typographic tweaking ---------------------------------*/ - -.contents .page-header { - margin-top: calc(-60px + 1em); -} - -dd { - margin-left: 3em; -} - -/* Section anchors ---------------------------------*/ - -a.anchor { - display: none; - margin-left: 5px; - width: 20px; - height: 20px; - - background-image: url(./link.svg); - background-repeat: no-repeat; - background-size: 20px 20px; - background-position: center center; -} - -h1:hover .anchor, -h2:hover .anchor, -h3:hover .anchor, -h4:hover .anchor, -h5:hover .anchor, -h6:hover .anchor { - display: inline-block; -} - -/* Fixes for fixed navbar --------------------------*/ - -.contents h1, .contents h2, .contents h3, .contents h4 { - padding-top: 60px; - margin-top: -40px; -} - -/* Navbar submenu --------------------------*/ - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu>.dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover>.dropdown-menu { - display: block; -} - -.dropdown-submenu>a:after { - display: block; - content: " "; - float: right; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; - border-width: 5px 0 5px 5px; - border-left-color: #cccccc; - margin-top: 5px; - margin-right: -10px; -} - -.dropdown-submenu:hover>a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left>.dropdown-menu { - left: -100%; - margin-left: 10px; - border-radius: 6px 0 6px 6px; -} - -/* Sidebar --------------------------*/ - -#pkgdown-sidebar { - margin-top: 30px; - position: -webkit-sticky; - position: sticky; - top: 70px; -} - -#pkgdown-sidebar h2 { - font-size: 1.5em; - margin-top: 1em; -} - -#pkgdown-sidebar h2:first-child { - margin-top: 0; -} - -#pkgdown-sidebar .list-unstyled li { - margin-bottom: 0.5em; -} - -/* bootstrap-toc tweaks ------------------------------------------------------*/ - -/* All levels of nav */ - -nav[data-toggle='toc'] .nav > li > a { - padding: 4px 20px 4px 6px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; -} - -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 5px; - color: inherit; - border-left: 1px solid #878787; -} - -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 5px; - font-size: 1.5rem; - font-weight: 400; - color: inherit; - border-left: 2px solid #878787; -} - -/* Nav: second level (shown on .active) */ - -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} - -nav[data-toggle='toc'] .nav .nav > li > a { - padding-left: 16px; - font-size: 1.35rem; -} - -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 15px; -} - -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 15px; - font-weight: 500; - font-size: 1.35rem; -} - -/* orcid ------------------------------------------------------------------- */ - -.orcid { - font-size: 16px; - color: #A6CE39; - /* margins are required by official ORCID trademark and display guidelines */ - margin-left:4px; - margin-right:4px; - vertical-align: middle; -} - -/* Reference index & topics ----------------------------------------------- */ - -.ref-index th {font-weight: normal;} - -.ref-index td {vertical-align: top; min-width: 100px} -.ref-index .icon {width: 40px;} -.ref-index .alias {width: 40%;} -.ref-index-icons .alias {width: calc(40% - 40px);} -.ref-index .title {width: 60%;} - -.ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} -.ref-arguments .name {width: 20%;} -.ref-arguments .desc {width: 80%;} - -/* Nice scrolling for wide elements --------------------------------------- */ - -table { - display: block; - overflow: auto; -} - -/* Syntax highlighting ---------------------------------------------------- */ - -pre, code, pre code { - background-color: #f8f8f8; - color: #333; -} -pre, pre code { - white-space: pre-wrap; - word-break: break-all; - overflow-wrap: break-word; -} - -pre { - border: 1px solid #eee; -} - -pre .img, pre .r-plt { - margin: 5px 0; -} - -pre .img img, pre .r-plt img { - background-color: #fff; -} - -code a, pre a { - color: #375f84; -} - -a.sourceLine:hover { - text-decoration: none; -} - -.fl {color: #1514b5;} -.fu {color: #000000;} /* function */ -.ch,.st {color: #036a07;} /* string */ -.kw {color: #264D66;} /* keyword */ -.co {color: #888888;} /* comment */ - -.error {font-weight: bolder;} -.warning {font-weight: bolder;} - -/* Clipboard --------------------------*/ - -.hasCopyButton { - position: relative; -} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - -/* mark.js ----------------------------*/ - -mark { - background-color: rgba(255, 255, 51, 0.5); - border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} - -/* Section anchors --------------------------------- - Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 -*/ - -div.csl-bib-body { } -div.csl-entry { - clear: both; -} -.hanging-indent div.csl-entry { - margin-left:2em; - text-indent:-2em; -} -div.csl-left-margin { - min-width:2em; - float:left; -} -div.csl-right-inline { - margin-left:2em; - padding-left:1em; -} -div.csl-indent { - margin-left: 2em; -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 6f0eee4..0a5573a 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -1,108 +1,162 @@ /* http://gregfranko.com/blog/jquery-best-practices/ */ -(function($) { - $(function() { +(function ($) { + $(function () { - $('.navbar-fixed-top').headroom(); + $('nav.navbar').headroom(); - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); + Toc.init({ + $nav: $("#toc"), + $scope: $("main h2, main h3, main h4, main h5, main h6") }); - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } + if ($('#toc').length) { + $('body').scrollspy({ + target: '#toc', + offset: $("nav.navbar").outerHeight() + 1 + }); } - // Add class to parent
    • , and enclosing
    • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / + // Activate popovers + $('[data-bs-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } + $('[data-bs-toggle="tooltip"]').tooltip(); - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); + /* Clipboard --------------------------*/ - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle = element.getAttribute('data-bs-original-title'); + element.setAttribute('data-bs-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-bs-original-title', tooltipOriginalTitle); } - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } + if (ClipboardJS.isSupported()) { + $(document).ready(function () { + var copyButton = ""; - return(haystack.length); - } + $("div.sourceCode").addClass("hasCopyButton"); - /* Clipboard --------------------------*/ + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } + // Initialize tooltips: + $('.btn-copy-ex').tooltip({ container: 'body' }); - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; + // Initialize clipboard: + var clipboard = new ClipboardJS('[data-clipboard-copy]', { + text: function (trigger) { + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + } + }); - $("div.sourceCode").addClass("hasCopyButton"); + clipboard.on('success', function (e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); + clipboard.on('error', function (e) { + changeTooltipMessage(e.trigger, 'Press Ctrl+C or Command+C to copy'); + }); - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); + }); + } - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + /* Search marking --------------------------*/ + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("main#main"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], } }); + } - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); + /* Search --------------------------*/ + /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ + // Initialise search index on focus + var fuse; + $("#search-input").focus(async function (e) { + if (fuse) { + return; + } - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); + $(e.target).addClass("loading"); + var response = await fetch($("#search-input").data("search-index")); + var data = await response.json(); + + var options = { + keys: ["what", "text", "code"], + ignoreLocation: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); }); - } + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + var q; + async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + if (items.length === 0) { + items = [{ dir: "Sorry 😿", previous_headings: "", title: "No results found.", what: "No results found.", path: window.location.href }]; + } + } + callback(items); + } + $("#search-input").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.title == s.what) { + return `${s.dir} >
      ${s.title}
      `; + } else if (s.previous_headings == "") { + return `${s.dir} >
      ${s.title}
      > ${s.what}`; + } else { + return `${s.dir} >
      ${s.title}
      > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function (event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); })(window.jQuery || window.$) + +document.addEventListener('keydown', function (event) { + // Check if the pressed key is '/' + if (event.key === '/') { + event.preventDefault(); // Prevent any default action associated with the '/' key + document.getElementById('search-input').focus(); // Set focus to the search input + } +}); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index ce30917..78646a0 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -5,4 +5,7 @@ articles: examples: examples.html leapR: leapR.html order-enrichment: order-enrichment.html -last_built: 2026-02-08T00:00Z +last_built: 2026-02-11T18:27Z +urls: + reference: https://pnnl-github.io/leapR/reference + article: https://pnnl-github.io/leapR/articles diff --git a/docs/reference/calcTTest.html b/docs/reference/calcTTest.html index 5afb30b..6028732 100644 --- a/docs/reference/calcTTest.html +++ b/docs/reference/calcTTest.html @@ -1,77 +1,60 @@ -calcTTest — calcTTest • leapRcalcTTest — calcTTest • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      calcTTest

      +

      calcTTest

      - +
      calcTTest.Rd
      -
      +

      calculates a t-test for two distributions of data on a per-gene basis append results to ExpressionSet with two extra columns: `pvalue` and `difference` for each feature

      -
      +
      +

      Usage

      calcTTest(eset, assay_name, group1, group2)
      -
      -

      Arguments

      +
      +

      Arguments

      eset
      @@ -90,14 +73,14 @@

      Arguments

      List of samples comprising group 2

      -
      -

      Value

      +
      +

      Value

      An Expression set with two columns added to the featureData slot: pvalue, and estimate

      -
      -

      Examples

      +
      +

      Examples

      
         library(leapR)
         url <- "https://api.figshare.com/v2/file/download/56536214"
@@ -122,23 +105,19 @@ 
      Examples
      
 #> colData names(0):
      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/cluster_enrichment.html b/docs/reference/cluster_enrichment.html index abd3cec..2df9b6d 100644 --- a/docs/reference/cluster_enrichment.html +++ b/docs/reference/cluster_enrichment.html @@ -1,75 +1,57 @@ -cluster_enrichment — cluster_enrichment • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      cluster_enrichment

      +

      cluster_enrichment

      - +
      cluster_enrichment.Rd
      -
      +

      Cluster enrichment Run enrichment (Fisher's exact) on clusters (lists of identifier groups)

      -
      +
      +

      Usage

      cluster_enrichment(eset, assay_name, geneset, clusters, sigfilter = 0.05)
      -
      -

      Arguments

      +
      +

      Arguments

      eset
      @@ -93,12 +75,12 @@

      Arguments

      minimum significance threshold default is .05

      -
      -

      Value

      +
      +

      Value

      data frame with enrichment results

      -
      -

      Details

      +
      +

      Details

      This function will calculate enrichment (Fisher's exact test for membership overlap) on

      a series of lists of genes, such as from a set of clusters. The @@ -106,8 +88,8 @@

      Details

      a list of results matrices in the order of the input clusters.

      -
      -

      Examples

      +
      +

      Examples

              library(leapR)
 
         # read in the example transcriptomic data
@@ -140,23 +122,19 @@ 
      Examples
      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/combine_omics.html b/docs/reference/combine_omics.html index 064cd5f..dd852c2 100644 --- a/docs/reference/combine_omics.html +++ b/docs/reference/combine_omics.html @@ -1,77 +1,60 @@ -combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids. — combine_omics • leapRcombine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids. — combine_omics • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

      +

      combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

      - +
      combine_omics.Rd
      -
      +

      combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

      -
      +
      +

      Usage

      combine_omics(omics_list, id_list = rep(NA, length(omics_list)))
      -
      -

      Arguments

      +
      +

      Arguments

      omics_list
      @@ -85,28 +68,24 @@

      Arguments

      is `NA`, then rownames are used.

      • -
      -

      Value

      +
      +

      Value

      SummarizedExperiment with an additional assay called `combined`

      -
      -

      Details

      +
      +

      Details

      This combines matrices of different omics types together and adds prefix tags to the ids.

      -
      -

      Examples

      +
      +

      Examples

              library(leapR)
         url <- 'https://api.figshare.com/v2/file/download/56536217'
 
         pdata <- download.file(url,method='libcurl',destfile='protData.rda')
-#> Warning: downloaded length 18732111 != reported length 23253677
-#> Warning: URL 'https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/56536217/protData.rda?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20260208/eu-west-1/s3/aws4_request&X-Amz-Date=20260208T000035Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=3d35d9a6edc15872990b7dbabedeef681c1d49bcda05fa883149680acf838771': Timeout of 60 seconds was reached
-#> Error in download.file(url, method = "libcurl", destfile = "protData.rda"): download from 'https://api.figshare.com/v2/file/download/56536217' failed
         load('protData.rda')
-#> Error in load("protData.rda"): error reading from connection
         p <- file.remove("protData.rda")
 
         url <- "https://api.figshare.com/v2/file/download/56536214"
@@ -125,28 +104,23 @@ 
      Examples
      
         # different omics types
         multi_omics <- combine_omics(list(pset, tset, phset),
                     list(NA,NA,'hgnc_id'))
-#> Error: object 'pset' not found
      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/correlation_comparison_enrichment.html b/docs/reference/correlation_comparison_enrichment.html index 3786472..a873852 100644 --- a/docs/reference/correlation_comparison_enrichment.html +++ b/docs/reference/correlation_comparison_enrichment.html @@ -1,72 +1,55 @@ -correlation_comparison_enrichment — correlation_comparison_enrichment • leapRcorrelation_comparison_enrichment — correlation_comparison_enrichment • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      correlation_comparison_enrichment

      +

      correlation_comparison_enrichment

      - +
      correlation_comparison_enrichment.Rd
      -
      +

      # internal function to calculate enrichment in differences in correlation # between two groups # access through the leapr wrapper

      -
      +
      +

      Usage

      correlation_comparison_enrichment(
   geneset,
   eset,
@@ -77,8 +60,8 @@ 
      correlation_comparison_enrichment
      
 )
      -
      -

      Arguments

      +
      +

      Arguments

      geneset
      @@ -105,28 +88,24 @@

      Arguments

      Column to use for id mapping within rowData

      -
      -

      Value

      +
      +

      Value

      data frame with enrichment results

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/correlation_enrichment.html b/docs/reference/correlation_enrichment.html index 9b35842..7d70347 100644 --- a/docs/reference/correlation_enrichment.html +++ b/docs/reference/correlation_enrichment.html @@ -1,75 +1,57 @@ -correlation_enrichment — correlation_enrichment • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      correlation_enrichment

      +

      correlation_enrichment

      - +
      correlation_enrichment.Rd
      -
      +

      # calculate enrichment in correlation between pathway members # access through leapr wrapper

      -
      +
      +

      Usage

      correlation_enrichment(geneset, eset, assay_name, mapping_column = NA)
      -
      -

      Arguments

      +
      +

      Arguments

      geneset
      @@ -88,28 +70,24 @@

      Arguments

      Column to use to map identifiers, if not rownames

      -
      -

      Value

      +
      +

      Value

      list of enrichment statistic table and correlation matrix

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/enrichment_in_abundance.html b/docs/reference/enrichment_in_abundance.html index eaf5c29..f77a424 100644 --- a/docs/reference/enrichment_in_abundance.html +++ b/docs/reference/enrichment_in_abundance.html @@ -1,70 +1,52 @@ -enrichment_in_abundance — enrichment_in_abundance • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      enrichment_in_abundance

      +

      enrichment_in_abundance

      - +
      enrichment_in_abundance.Rd
      -
      +

      Enrichment in abundance calculates enrichment in pathways by the difference in abundance of the pathway members.

      -
      +
      +

      Usage

      enrichment_in_abundance(
   geneset,
   eset,
@@ -80,8 +62,8 @@ 
      enrichment_in_abundance
      
 )
      -
      -

      Arguments

      +
      +

      Arguments

      geneset
      @@ -129,28 +111,24 @@

      Arguments

      set to true to silence try errors

      -
      -

      Value

      +
      +

      Value

      data frame of enrichment result

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/enrichment_in_groups.html b/docs/reference/enrichment_in_groups.html index 18a646b..1d7ccff 100644 --- a/docs/reference/enrichment_in_groups.html +++ b/docs/reference/enrichment_in_groups.html @@ -1,72 +1,55 @@ -enrichment_in_groups — enrichment_in_groups • leapRenrichment_in_groups — enrichment_in_groups • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      enrichment_in_groups

      +

      enrichment_in_groups

      - +
      enrichment_in_groups.Rd
      -
      +

      Calculate the enrichment in pathways using Fisher's exact or Kolmogorov-Smirnov test, using either the abundance column to identify feature or the targets list. access through leapr wrapper

      -
      +
      +

      Usage

      enrichment_in_groups(
   geneset,
   targets = c(),
@@ -81,8 +64,8 @@ 
      enrichment_in_groups
      
 )
      -
      -

      Arguments

      +
      +

      Arguments

      geneset
      @@ -129,28 +112,24 @@

      Arguments

      true/false to silence errors

      -
      -

      Value

      +
      +

      Value

      data frame with enrichment results

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/enrichment_in_relationships.html b/docs/reference/enrichment_in_relationships.html index cdf54ca..72d95d7 100644 --- a/docs/reference/enrichment_in_relationships.html +++ b/docs/reference/enrichment_in_relationships.html @@ -1,74 +1,58 @@ -enrichment_in_relationships — enrichment_in_relationships • leapRenrichment_in_relationships — enrichment_in_relationships • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      enrichment_in_relationships

      +

      enrichment_in_relationships

      - +
      enrichment_in_relationships.Rd
      -
      +

      enrichment_in_relationships function description is a general way to determine if a pathway is enriched in relationships (interactions, correlation) between its members # access through leapr wrapper

      -
      +
      +

      Usage

      enrichment_in_relationships(
   geneset,
   relationships,
@@ -77,8 +61,8 @@ 
      enrichment_in_relationships
      
 )
      -
      -

      Arguments

      +
      +

      Arguments

      geneset
      @@ -98,28 +82,24 @@

      Arguments

      boolean to silence errors

      -
      -

      Value

      +
      +

      Value

      table of enrichment statistics

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/get_pathway_information.html b/docs/reference/get_pathway_information.html index d3ba365..23fa80f 100644 --- a/docs/reference/get_pathway_information.html +++ b/docs/reference/get_pathway_information.html @@ -1,75 +1,57 @@ -get_pathway_information — get_pathway_information • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      get_pathway_information

      +

      get_pathway_information

      - +
      get_pathway_information.Rd
      -
      +

      get_pathway_information extracts information about a pathway from a GeneSet object

      -
      +
      +

      Usage

      get_pathway_information(geneset, path, remove.tags = FALSE)
      -
      -

      Arguments

      +
      +

      Arguments

      geneset
      @@ -84,13 +66,13 @@

      Arguments

      boolean indicating whether to remove tags

      -
      -

      Value

      +
      +

      Value

      list of pathway information

      -
      -

      Examples

      +
      +

      Examples

      
      library(leapR)
 
@@ -102,23 +84,19 @@ 
      Examples
      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/index.html b/docs/reference/index.html index bbdc585..1578a2b 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -1,151 +1,182 @@ -Package index • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      Reference

      +

      Package index

      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      -

      All functions

      -

      -
      -

      calcTTest()

      -

      calcTTest

      -

      cluster_enrichment()

      -

      cluster_enrichment

      -

      combine_omics()

      -

      combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

      -

      correlation_comparison_enrichment()

      -

      correlation_comparison_enrichment

      -

      correlation_enrichment()

      -

      correlation_enrichment

      -

      enrichment_in_abundance()

      -

      enrichment_in_abundance

      -

      enrichment_in_groups()

      -

      enrichment_in_groups

      -

      enrichment_in_relationships()

      -

      enrichment_in_relationships

      -

      get_pathway_information()

      -

      get_pathway_information

      -

      kinasesubstrates

      -

      Kinase substrate lists

      -

      krbpaths

      -

      KEGG, Reactome, BioCarta Pathways

      -

      leapR()

      -

      leapR

      -

      longlist

      -

      Long list of patient samples

      -

      ncipid

      -

      NCI Gene lists

      -

      plot_leapr_bar()

      -

      Plot leapR pathway bars (single panel)

      -

      read_gene_sets()

      -

      read_gene_sets

      -

      shortlist

      -

      A list of pathways and genes that comprise these pathways from msigdb

      - - -
      +
      +

      All functions

      -
      + + +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      +
      +

      Site built with pkgdown 2.2.0.

      +
      +
      diff --git a/docs/reference/kinasesubstrates.html b/docs/reference/kinasesubstrates.html index 9f22087..e88b2c8 100644 --- a/docs/reference/kinasesubstrates.html +++ b/docs/reference/kinasesubstrates.html @@ -1,73 +1,54 @@ -Kinase substrate lists — kinasesubstrates • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      Kinase substrate lists

      +

      Kinase substrate lists

      - +
      kinasesubstrates.Rd
      -
      +

      Kinase substrate lists

      -
      +
      +

      Usage

      kinasesubstrates
      -
      -

      Format

      +
      +

      Format

      A list with 4 items

      names

      The names of the kinases

      @@ -82,28 +63,24 @@

      Format

      -
      -

      Source

      +
      +

      Source

      PhosphositePlus

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/krbpaths.html b/docs/reference/krbpaths.html index 05f0480..dab0ecd 100644 --- a/docs/reference/krbpaths.html +++ b/docs/reference/krbpaths.html @@ -1,73 +1,54 @@ -KEGG, Reactome, BioCarta Pathways — krbpaths • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      KEGG, Reactome, BioCarta Pathways

      +

      KEGG, Reactome, BioCarta Pathways

      - +
      krbpaths.Rd
      -
      +

      KEGG, Reactome, BioCarta Pathways

      -
      +
      +

      Usage

      krbpaths
      -
      -

      Format

      +
      +

      Format

      A list with 4 items

      names

      The names of the pathways

      @@ -82,28 +63,24 @@

      Format

      -
      -

      Source

      +
      +

      Source

      https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/leapR-package.html b/docs/reference/leapR-package.html index 7a41a1e..46b684b 100644 --- a/docs/reference/leapR-package.html +++ b/docs/reference/leapR-package.html @@ -1,93 +1,68 @@ -leapR: Layered enrichment analysis of pathways R — leapR-package • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      leapR: Layered enrichment analysis of pathways R

      +

      leapR: Layered enrichment analysis of pathways R

      - +
      leapR-package.Rd
      -
      +

      leapR is a package that identifies pathways that are enriched across diverse 'omics experiments. It leverages any tabular expression data (proteomics, transcriptomics) using the `SummarizedExperiment` object. It works with any pathway in the .gct file format.

      -
      -

      Author

      +
      +

      Author

      Maintainer: Sara Gosline sara.gosline@pnnl.gov (ORCID)

      Authors:

      Other contributors:

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/leapR.html b/docs/reference/leapR.html index f2ff791..250cc59 100644 --- a/docs/reference/leapR.html +++ b/docs/reference/leapR.html @@ -1,73 +1,54 @@ -leapR — leapR • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      leapR

      +

      leapR

      - +
      leapR.Rd
      -
      +

      leapR is a wrapper function that consolidates multiple enrichment methods.

      -
      +
      +

      Usage

      leapR(geneset, enrichment_method, eset, assay_name, ...)
      -
      -

      Arguments

      +
      +

      Arguments

      geneset
      @@ -97,12 +78,12 @@

      Arguments

      further arguments

      -
      -

      Value

      +
      +

      Value

      data frame with results

      -
      -

      Details

      +
      +

      Details

      Further arguments and enrichment method optional argument information:

      id_columnIs a character string, present in the rowData slot, that is used to specify a column for identifiers to map to enrichment libraries. @@ -199,8 +180,8 @@

      Details


      -
      -

      Examples

      +
      +

      Examples

              library(leapR)
 
  # read in the example abundance data
@@ -314,23 +295,19 @@ 
      Examples
      -
      - - + -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/longlist.html b/docs/reference/longlist.html index 422310c..a6ec82d 100644 --- a/docs/reference/longlist.html +++ b/docs/reference/longlist.html @@ -1,93 +1,70 @@ -Long list of patient samples — longlist • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      Long list of patient samples

      +

      Long list of patient samples

      - +
      longlist.Rd
      -
      +

      Long list of patient samples

      -
      +
      +

      Usage

      longlist
      -
      -

      Format

      +
      +

      Format

      An object of class character of length 37.

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/ncipid.html b/docs/reference/ncipid.html index 876449e..fceb6c8 100644 --- a/docs/reference/ncipid.html +++ b/docs/reference/ncipid.html @@ -1,73 +1,54 @@ -NCI Gene lists — ncipid • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      NCI Gene lists

      +

      NCI Gene lists

      - +
      ncipid.Rd
      -
      +

      A list of pathways and the genes that comprise these pathways

      -
      +
      +

      Usage

      ncipid
      -
      -

      Format

      +
      +

      Format

      A list with 4 items

      names

      The names of the signaling pathways

      @@ -82,28 +63,24 @@

      Format

      -
      -

      Source

      +
      +

      Source

      NCIPID

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/plot_leapr_bar.html b/docs/reference/plot_leapr_bar.html index 36df698..e1688a5 100644 --- a/docs/reference/plot_leapr_bar.html +++ b/docs/reference/plot_leapr_bar.html @@ -1,70 +1,52 @@ -Plot leapR pathway bars (single panel) — plot_leapr_bar • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      Plot leapR pathway bars (single panel)

      +

      Plot leapR pathway bars (single panel)

      - +
      plot_leapr_bar.Rd
      -
      +

      This plotting helper expects leapR generated results to plot. It will use BH_pvalue if present, otherwise pvalue.

      -
      +
      +

      Usage

      plot_leapr_bar(
   res_df,
   title = NULL,
@@ -80,8 +62,8 @@ 
      Plot leapR pathway bars (single panel)
      
 )
      -
      -

      Arguments

      +
      +

      Arguments

      res_df
      @@ -128,28 +110,24 @@

      Arguments

      Font size for x-axis (numeric) labels.

      -
      -

      Value

      +
      +

      Value

      A ggplot2 object (or NULL if nothing to plot).

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/read_gene_sets.html b/docs/reference/read_gene_sets.html index c7aba8f..b15d70c 100644 --- a/docs/reference/read_gene_sets.html +++ b/docs/reference/read_gene_sets.html @@ -1,70 +1,52 @@ -read_gene_sets — read_gene_sets • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      read_gene_sets

      +

      read_gene_sets

      - +
      read_gene_sets.Rd
      -
      +

      read_gene_sets is a function to import external pathway database files in .gmt format

      -
      +
      +

      Usage

      read_gene_sets(
   gsfile,
   gene.labels = NA,
@@ -73,8 +55,8 @@ 
      read_gene_sets
      
 )
      -
      -

      Arguments

      +
      +

      Arguments

      gsfile
      @@ -94,37 +76,33 @@

      Arguments

      defaults to 15000

      -
      -

      Value

      +
      +

      Value

      gene set object

      geneset list

      -
      -

      Examples

      +
      +

      Examples

      gfile <- system.file('extdata','h.all.v2024.1.Hs.symbols.gmt',
           package='leapR')
 glist <- read_gene_sets(gfile)
      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/docs/reference/shortlist.html b/docs/reference/shortlist.html index 3443649..aa77ef6 100644 --- a/docs/reference/shortlist.html +++ b/docs/reference/shortlist.html @@ -1,94 +1,71 @@ -A list of pathways and genes that comprise these pathways from msigdb — shortlist • leapR - - -
      -
      +
      +
      +
      -
      -
      -
      -

      A list of pathways and genes that comprise these pathways from msigdb

      +

      A list of pathways and genes that comprise these pathways from msigdb

      - +
      shortlist.Rd
      -
      +

      A list of pathways and genes that comprise these pathways from msigdb

      -
      +
      +

      Usage

      shortlist
      -
      -

      Format

      +
      +

      Format

      a list with 4 items

      Short list of patient samples

      -
      - -
      +
      -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      +
      +

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      -

      Site built with pkgdown 2.2.0.

      +
      +

      Site built with pkgdown 2.2.0.

      -
      - +
      diff --git a/vignettes/order-enrichment.Rmd b/vignettes/order-enrichment.Rmd index a3159c9..336e161 100644 --- a/vignettes/order-enrichment.Rmd +++ b/vignettes/order-enrichment.Rmd @@ -58,6 +58,7 @@ We want to evaluate how various samples compare in enrichmnet ```{r compare} i = 8 +data("ncipid") cor.res <- do.call(rbind,lapply(1:length(shortlist), function (i) { protdata.enrichment.ks <- leapR::leapR( From 96e17547a870155000e8170ecf065c1c61b89ce3 Mon Sep 17 00:00:00 2001 From: Sara JC Gosline Date: Tue, 10 Mar 2026 15:30:09 -0700 Subject: [PATCH 3/3] removed docs from this branch, then updated --- DESCRIPTION | 2 +- docs/404.html | 82 -- docs/LICENSE-text.html | 89 -- docs/articles/examples.html | 491 ------- .../examples_files/figure-html/figure_2-1.png | Bin 40093 -> 0 bytes .../examples_files/figure-html/figure_3-1.png | Bin 23985 -> 0 bytes .../examples_files/figure-html/figure_3-2.png | Bin 22159 -> 0 bytes docs/articles/index.html | 68 - docs/articles/leapR.html | 841 ------------ .../leapR_files/figure-html/corr_plot-1.png | Bin 57942 -> 0 bytes .../figure-html/fishers_plot-1.png | Bin 54834 -> 0 bytes .../leapR_files/figure-html/ks_plot-1.png | Bin 64650 -> 0 bytes .../leapR_files/figure-html/phos_plot-1.png | Bin 56209 -> 0 bytes .../leapR_files/figure-html/phos_plot-2.png | Bin 57091 -> 0 bytes .../articles/leapR_files/figure-html/zt-1.png | Bin 61272 -> 0 bytes .../pagedtable-1.1/css/pagedtable.css | 142 -- .../pagedtable-1.1/js/pagedtable.js | 1151 ----------------- docs/authors.html | 99 -- docs/index.html | 185 --- docs/link.svg | 12 - docs/pkgdown.js | 162 --- docs/pkgdown.yml | 11 - docs/reference/calcTTest.html | 127 -- docs/reference/cluster_enrichment.html | 144 --- docs/reference/combine_omics.html | 130 -- .../correlation_comparison_enrichment.html | 115 -- docs/reference/correlation_enrichment.html | 97 -- docs/reference/enrichment_in_abundance.html | 138 -- docs/reference/enrichment_in_groups.html | 139 -- .../enrichment_in_relationships.html | 109 -- docs/reference/get_pathway_information.html | 106 -- docs/reference/index.html | 186 --- docs/reference/kinasesubstrates.html | 90 -- docs/reference/krbpaths.html | 90 -- docs/reference/leapR-package.html | 72 -- docs/reference/leapR.html | 317 ----- docs/reference/longlist.html | 74 -- docs/reference/ncipid.html | 90 -- docs/reference/plot_leapr_bar.html | 137 -- docs/reference/read_gene_sets.html | 112 -- docs/reference/shortlist.html | 75 -- 41 files changed, 1 insertion(+), 5682 deletions(-) delete mode 100644 docs/404.html delete mode 100644 docs/LICENSE-text.html delete mode 100644 docs/articles/examples.html delete mode 100644 docs/articles/examples_files/figure-html/figure_2-1.png delete mode 100644 docs/articles/examples_files/figure-html/figure_3-1.png delete mode 100644 docs/articles/examples_files/figure-html/figure_3-2.png delete mode 100644 docs/articles/index.html delete mode 100644 docs/articles/leapR.html delete mode 100644 docs/articles/leapR_files/figure-html/corr_plot-1.png delete mode 100644 docs/articles/leapR_files/figure-html/fishers_plot-1.png delete mode 100644 docs/articles/leapR_files/figure-html/ks_plot-1.png delete mode 100644 docs/articles/leapR_files/figure-html/phos_plot-1.png delete mode 100644 docs/articles/leapR_files/figure-html/phos_plot-2.png delete mode 100644 docs/articles/leapR_files/figure-html/zt-1.png delete mode 100644 docs/articles/leapR_files/pagedtable-1.1/css/pagedtable.css delete mode 100644 docs/articles/leapR_files/pagedtable-1.1/js/pagedtable.js delete mode 100644 docs/authors.html delete mode 100644 docs/index.html delete mode 100644 docs/link.svg delete mode 100644 docs/pkgdown.js delete mode 100644 docs/pkgdown.yml delete mode 100644 docs/reference/calcTTest.html delete mode 100644 docs/reference/cluster_enrichment.html delete mode 100644 docs/reference/combine_omics.html delete mode 100644 docs/reference/correlation_comparison_enrichment.html delete mode 100644 docs/reference/correlation_enrichment.html delete mode 100644 docs/reference/enrichment_in_abundance.html delete mode 100644 docs/reference/enrichment_in_groups.html delete mode 100644 docs/reference/enrichment_in_relationships.html delete mode 100644 docs/reference/get_pathway_information.html delete mode 100644 docs/reference/index.html delete mode 100644 docs/reference/kinasesubstrates.html delete mode 100644 docs/reference/krbpaths.html delete mode 100644 docs/reference/leapR-package.html delete mode 100644 docs/reference/leapR.html delete mode 100644 docs/reference/longlist.html delete mode 100644 docs/reference/ncipid.html delete mode 100644 docs/reference/plot_leapr_bar.html delete mode 100644 docs/reference/read_gene_sets.html delete mode 100644 docs/reference/shortlist.html diff --git a/DESCRIPTION b/DESCRIPTION index 4a6edcd..410a79b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: leapR Title: Layered enrichment analysis of pathways R -Version: 0.99.7 +Version: 0.99.8 Authors@R: c( person("Sara", "Gosline", email = "sara.gosline@pnnl.gov", role = c('aut',"cre"), comment = c(ORCID = "0000-0002-6534-4774")), person("Jason", "McDermott", email = "jason.mcdermott@pnnl.gov", role = "aut"), diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index a4be44e..0000000 --- a/docs/404.html +++ /dev/null @@ -1,82 +0,0 @@ - - - - - - - -Page not found (404) • leapR - - - - - - - - Skip to contents - - -
      -
      -
      - -

      Page not found (404)

      - -
      - -Content not found. Please use links in the navbar. - -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      -
      - - - - - - - diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html deleted file mode 100644 index b910367..0000000 --- a/docs/LICENSE-text.html +++ /dev/null @@ -1,89 +0,0 @@ - -License • leapR - Skip to contents - - -
      -
      -
      - -

      License

      - -
      - -
      Copyright 2025 Battelle Memorial Institute
-
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions
-are met:
-
-
-1. Redistributions of source code must retain the above copyright
-notice, this list of conditions and the following disclaimer.
-
-
-2. Redistributions in binary form must reproduce the above copyright
-notice, this list of conditions and the following disclaimer in the
-documentation and/or other materials provided with the distribution.
-
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
-
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/articles/examples.html b/docs/articles/examples.html deleted file mode 100644 index 0efac56..0000000 --- a/docs/articles/examples.html +++ /dev/null @@ -1,491 +0,0 @@ - - - - - - - -leapR Paper Examples • leapR - - - - - - - - Skip to contents - - -
      - - - - -
      -
      - -

      leapR Paper Examples

      - - - -
      examples.Rmd
      -
      - - - -
      -

      Load libraries needed -

      -
      -# load the core libraries
-library(leapR)
-library(gplots)
-library(rmarkdown)
-# plotting helpers used in this vignette
-library(ggplot2)
-library(dplyr)
-library(tibble)
-library(stringr)
      -
      -
      -

      Example data -

      -

      A sample data set is included that is from the CPTAC study of 169 -ovarian tumors. We include the dataset as a object, containing three -assays (transcriptomics, global proteomics, and phosphoproteomics) to -enable interoperability with other tools, and store example file as -rda on Figshare -as example.

      -

      This data can be loaded as follows:

      -
      -url <- "https://api.figshare.com/v2/file/download/56536217"
-pdata <- download.file(url, method = "libcurl", destfile = "protData.rda")
-#  as.matrix()
-load("protData.rda")
-
-p <- file.remove("protData.rda")
-
-url <- "https://api.figshare.com/v2/file/download/56536214"
-tdata <- download.file(url, method = "libcurl", destfile = "transData.rda")
-load("transData.rda")
-p <- file.remove("transData.rda")
-
-url <- "https://api.figshare.com/v2/file/download/56536211"
-phdata <- download.file(url, method = "libcurl", destfile = "phosData.rda")
-load("phosData.rda")
-p <- file.remove("phosData.rda")
      -

      We also have local data we can load

      -
      -
-data(shortlist)
-data(longlist)
-data(ncipid)
-data(kinasesubstrates)
-## columns that we want to use for results
-
-cols_to_display <- c("ingroup_n", "outgroup_n", "background_n", 
-                     "pvalue", "BH_pvalue")
      -
      -
      -

      Figure 2 -

      -

      We will compare the ability of transcriptomics, proteomics, and -phosphoproteomics to inform about differences between short and long -surviving patient groups. In addition to other methods, we also employ a -calcTTest function that takes two sets of samples from the -SummarizedExperiment object and computes the t-test between -them. The results are then stored in the rowData of the -same object, so that they can be used for enrichment later on.

      -

      This spans the multiple enrichment methods in leapR and also includes -multi-omics.

      -

      The resulting heatmap is presented as Figure 2 in the paper.

      -
      -# load the single omic and multi-omic pathway databases
-data("krbpaths")
-data("mo_krbpaths")
-
-# comparison enrichment in transcriptional data
-transdata.comp.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_comparison",
-  eset = tset,
-  assay_name = "transcriptomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist
-)
-
-# comparison enrichment in proteomics data
-# this is the same code used above, just repeated here for clarity
-protdata.comp.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_comparison",
-  eset = pset,
-  assay_name = "proteomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist
-)
-
-# comparison enrichment in phosphoproteomics data
-phosphodata.comp.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_comparison",
-  eset = phset,
-  assay_name = "phosphoproteomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist, id_column = "hgnc_id"
-)
-
-
-# set enrichment in transcriptomics data
-# perform the comparison t-test
-tset <- leapR::calcTTest(tset, assay_name = "transcriptomics", 
-                         shortlist, longlist)
-
-
-## now we need to run enrichment in sets with target list, not eset
-transdata.set.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  eset = tset,
-  assay_name = "transcriptomics",
-  enrichment_method = "enrichment_in_sets",
-  primary_columns = "pvalue",
-  greaterthan = FALSE, threshold = 0.05
-)
-
-
-pset <- leapR::calcTTest(pset, assay_name = "proteomics",
-                         shortlist, longlist)
-
-protdata.set.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  eset = pset,
-  assay_name = "proteomics",
-  enrichment_method = "enrichment_in_sets",
-  primary_columns = "pvalue",
-  greaterthan = FALSE, threshold = 0.05
-)
-
-
-phset <- leapR::calcTTest(phset, assay_name = "phosphoproteomics", 
-                          shortlist, longlist)
-
-phosphodata.set.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_in_sets",
-  id_column = "hgnc_id",
-  assay_name = "phosphoproteomics",
-  eset = phset, primary_columns = "pvalue",
-  greaterthan = FALSE, threshold = 0.05
-)
-
-# order enrichment in transcriptomics data
-transdata.order.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_in_order",
-  eset = tset,
-  assay_name = "transcriptomics",
-  primary_columns = "difference"
-)
-
-# order enrichment in proteomics data
-protdata.order.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_in_order",
-  eset = pset,
-  assay_name = "proteomics",
-  primary_columns = "difference"
-)
-
-# order enrichment in phosphoproteomics data
-
-
-phosphodata.order.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_in_order",
-  id_column = "hgnc_id",
-  method = 'ztest', 
-  eset = phset,
-  assay_name = "phosphoproteomics",
-  primary_columns = "difference"
-)
-
-# correlation difference in transcriptomics data
-transdata.corr.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "correlation_comparison",
-  eset = tset,
-  assay_name = "transcriptomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist
-)
-# correlation difference in proteomics data
-protdata.corr.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "correlation_comparison",
-  eset = pset,
-  assay_name = "proteomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist
-)
-# correlation difference in phosphoproteomics data
-phosphodata.corr.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "correlation_comparison",
-  eset = phset,
-  assay_name = "phosphoproteomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist, id_column = "hgnc_id"
-)
-
-# combine the omics data into one with prefix tags
-comboset <- leapR::combine_omics(list(pset, phset, tset), 
-                                 c(NA, "hgnc_id", NA))
-
-# comparison enrichment for combodata
-# when we use expression set, we do not need to use the mo_krbpaths 
-#since the  id mapping column is used
-combodata.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths, # mo_krbpaths,
-  enrichment_method = "enrichment_comparison",
-  eset = comboset,
-  assay_name = "combined",
-  primary_columns = shortlist,
-  secondary_columns = longlist, id_column = "id"
-)
-
-
-# set enrichment in combo data
-# perform the comparison t test
-comboset <- leapR::calcTTest(comboset,
-                             assay_name = "combined", 
-                             shortlist, longlist)
-
-combodata.set.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_in_sets",
-  eset = comboset, primary_columns = "pvalue",
-  assay_name = "combined",
-  id_column = "id",
-  greaterthan = FALSE, threshold = 0.05
-)
-
-# order enrichment in combo data
-combodata.order.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "enrichment_in_order",
-  assay_name = "combined",
-  eset = comboset, primary_columns = "difference",
-  id_column = "id"
-)
-
-# correlation difference in combo data
-combodata.corr.enrichment.svl <- leapR::leapR(
-  geneset = krbpaths,
-  enrichment_method = "correlation_comparison",
-  eset = comboset,
-  assay_name = "combined",
-  primary_columns = shortlist,
-  id_column = "id",
-  secondary_columns = longlist
-)
-
-
-# now take all these results and combine them into one figure
-all_results <- list(
-  transdata.comp.enrichment.svl,
-  protdata.comp.enrichment.svl,
-  phosphodata.comp.enrichment.svl,
-  combodata.enrichment.svl,
-  transdata.set.enrichment.svl,
-  protdata.set.enrichment.svl,
-  phosphodata.set.enrichment.svl,
-  combodata.set.enrichment.svl,
-  transdata.order.enrichment.svl,
-  protdata.order.enrichment.svl,
-  phosphodata.order.enrichment.svl,
-  combodata.order.enrichment.svl,
-  transdata.corr.enrichment.svl,
-  protdata.corr.enrichment.svl,
-  phosphodata.corr.enrichment.svl,
-  combodata.corr.enrichment.svl
-)
-
-pathways_of_interest <- c(
-  "KEGG_APOPTOSIS",
-  "KEGG_CELL_CYCLE",
-  "KEGG_ERBB_SIGNALING_PATHWAY",
-  "KEGG_FOCAL_ADHESION",
-  "KEGG_INSULIN_SIGNALING_PATHWAY",
-  "KEGG_MAPK_SIGNALING_PATHWAY",
-  "KEGG_MISMATCH_REPAIR",
-  "KEGG_MTOR_SIGNALING_PATHWAY",
-  "KEGG_OXIDATIVE_PHOSPHORYLATION",
-  "KEGG_P53_SIGNALING_PATHWAY",
-  "KEGG_PATHWAYS_IN_CANCER",
-  "KEGG_PROTEASOME",
-  "KEGG_RIBOSOME",
-  "KEGG_VEGF_SIGNALING_PATHWAY",
-  "KEGG_WNT_SIGNALING_PATHWAY"
-)
-
-
-results.frame <- data.frame(
-  pathway = pathways_of_interest,
-  td.comp = all_results[[1]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  pd.comp = all_results[[2]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  fd.comp = all_results[[3]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  cd.comp = all_results[[4]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  td.set = all_results[[5]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  pd.set = all_results[[6]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  fd.set = all_results[[7]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  cd.set = all_results[[8]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  td.order = all_results[[9]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  pd.order = all_results[[10]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  fd.order = all_results[[11]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  cd.order = all_results[[12]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  td.corr = all_results[[13]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  pd.corr = all_results[[14]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  fd.corr = all_results[[15]][pathways_of_interest, "BH_pvalue"] < 0.05,
-  cd.corr = all_results[[16]][pathways_of_interest, "BH_pvalue"] < 0.05
-)
-
-results.frame.or <- data.frame(
-  pathway = pathways_of_interest,
-  td.comp = all_results[[1]][pathways_of_interest, "oddsratio"],
-  pd.comp = all_results[[2]][pathways_of_interest, "oddsratio"],
-  fd.comp = all_results[[3]][pathways_of_interest, "oddsratio"],
-  cd.comp = all_results[[4]][pathways_of_interest, "oddsratio"],
-  td.set = log(all_results[[5]][pathways_of_interest, "oddsratio"], 2),
-  pd.set = log(all_results[[6]][pathways_of_interest, "oddsratio"], 2),
-  fd.set = log(all_results[[7]][pathways_of_interest, "oddsratio"], 2),
-  cd.set = log(all_results[[8]][pathways_of_interest, "oddsratio"], 2),
-  td.order = all_results[[9]][pathways_of_interest, "oddsratio"],
-  pd.order = all_results[[10]][pathways_of_interest, "oddsratio"],
-  fd.order = all_results[[11]][pathways_of_interest, "oddsratio"],
-  cd.order = all_results[[12]][pathways_of_interest, "oddsratio"],
-  td.corr = all_results[[13]][pathways_of_interest, "oddsratio"],
-  pd.corr = all_results[[14]][pathways_of_interest, "oddsratio"],
-  fd.corr = all_results[[15]][pathways_of_interest, "oddsratio"],
-  cd.corr = all_results[[16]][pathways_of_interest, "oddsratio"]
-)
-
-rownames(results.frame) <- results.frame[, 1]
-rownames(results.frame.or) <- results.frame.or[, 1]
-results.frame.sig <- results.frame[, 2:17] * results.frame.or[, 2:17]
-
-heatmap.2(as.matrix(results.frame.sig[, c(1:4, 9:16)]), Colv = NA, 
-          trace = "none", breaks = c(-1, -.1, -0.0001, 0, 0.1, 1), 
-          col = c("blue", "lightblue", "grey", "pink", "red"), 
-          dendrogram = "none")
      -

      -

      Figure 3. An application of the KSEA-like approach in leapR as -applied to our example data. In this example we are looking for known -substrate sets of kinases (from Phosphosite Plus) that are enriched in -the short vs long comparison of phosphopeptides.

      -
      -# this comparison of abundance in substrates between case and control
-#     is lopsided in the sense that phosphorylation levels were previously
-#     reported to be overall higher in the short survivors. Thus the
-#     results are not terribly interesting (all kinases are in the same 
-# direction)
-phosphodata.ksea.comp.svl <- leapR::leapR(
-  geneset = kinasesubstrates,
-  enrichment_method = "enrichment_comparison",
-  eset = phset,
-  assay_name = "phosphoproteomics",
-  primary_columns = shortlist, secondary_columns = longlist
-)
-
-
-# thus for the example we'll look at correlation between known substrates 
-#   in the   case v control conditions
-phosphodata.ksea.corr.svl <- leapR::leapR(
-  geneset = kinasesubstrates,
-  enrichment_method = "correlation_comparison",
-  eset = phset,
-  assay_name = "phosphoproteomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist
-)
-
-# for the example we are using an UNCORRECTED PVALUE
-#     which will allow us to plot more values, but
-#     for real applications it's necessary to use the
-#     CORRECTED PVALUE
-
-# here are all the kinases *significant (*uncorrected) from the analysis
-or <- order(phosphodata.ksea.corr.svl[, "pvalue"])
-ksea_result <- phosphodata.ksea.corr.svl[or, ][1:9, ]
-ksea_cols <- rep("grey", 9)
-ksea_cols[which(ksea_result[, "oddsratio"] > 0)] <- "black"
-
-# plot left panel: correlation significance of top most significant kinases
-barplot(ksea_result[, "oddsratio"],
-  horiz = TRUE, xlim = c(-1, 0.5),
-  names.arg = rownames(ksea_result), las = 1, col = ksea_cols
-)
      -

      -
      -
-# plot right panel: abundance comparison results of the same kinases
-barplot(phosphodata.ksea.comp.svl[rownames(ksea_result), "oddsratio"],
-  horiz = TRUE, names.arg = rownames(ksea_result), las = 1, col = "black"
-)
      -

      -
      -
      -
      - - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      -
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z_A}_aYD$%b%5%K>Fc#aUd|Oc+E8dKIKJPcEu&DYQmRc=HU!U9P)!7>TR&{X6$!q#D zcVCjY)N)6|F|4kp)Q{;B`GaC}z<4riVKTXY!c)8K-wnG_0f=j|>pzZfVPmz510oAwf$r zTv{1pm!&Y6ZNkaa_?WOJ4*yp{jXwREa!a6;SHx(VV4d7KD@P4{Uge-q;WmdmNwtu$ zqr|Y8ItQn~3q>_HP@teT&(WHBFa~-RVr7u}{QOVr@yx{MA^}aNwQ_4P;lx)iiRE~O zWXXbMwFhP%@&_+Vqyo3~y6oW*`zj1h4%cqTa|s=qyf<|Ye3i!(Fy4@iZgL6ATUuzl zM+QFfc0y`2ra&t1vmhj@A1nFBl)nD;#L_P1=PgYpRd}~Vt6p3Wx`C-r(>$%U?sgZB z>!jYiRSe%YFl}Sv+!+d`s*abMgX9*!(j18uxtGS@{YFMa%T1R)shcFd$%Xce4D2`Y zwSPTeKg~vHTj_IGq%uOpz_e@I?9?O77ws!45sfT1@nl}JQ(C$#S(nhZL*F6MFwfys zsw_%$a;(bYhQ>jo-*lpW%Clg4rZ -Articles • leapR - Skip to contents - - -
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      Articles

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      All vignettes

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      leapR Paper Examples
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      leapR
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      leapR Order Enrichment Tests
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      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

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      Site built with pkgdown 2.2.0.

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      - - - - - - - diff --git a/docs/articles/leapR.html b/docs/articles/leapR.html deleted file mode 100644 index 1ef5e6b..0000000 --- a/docs/articles/leapR.html +++ /dev/null @@ -1,841 +0,0 @@ - - - - - - - -leapR • leapR - - - - - - - - Skip to contents - - -
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      leapR

      - - - -
      leapR.Rmd
      -
      - - - -
      -

      Installation -

      -

      This is intended to be a short introduction to the leapR -package. First we need to load the required libraries:

      -
      -# install from bioconductor
-if (!require(BiocManager)) {
-  install.packages('BiocManager')
-  BiocManager::install('leapR')
-}
      -
      -
      -

      Load libraries needed -

      -
      -# load the core libraries
-library(leapR)
-library(gplots)
-library(rmarkdown)
-# plotting helpers used in this vignette
-library(ggplot2)
-library(dplyr)
-library(tibble)
-library(stringr)
      -
      -
      -

      Introduction -

      -
      -

      Definitions -

      -

      Dataset - an expression dataset, contained in the -Bioconductor object, that at the bare minimum has a matrix of components -(rows) measured in the same system under multiple different conditions -(columns)

      -

      Component - the things being measured, genes, proteins, -methylation site, phosphosite, etc. For functional (currently) the -component must be associated with a gene name. That is, there’s not -currently a way to calculate pathway enrichment using lipids.

      -

      Pathway - a set of components that works together to -accomplish something or are related to each other in some other way. -This includes classic signaling and metabolic pathways, but also -molecular function and localization categories and other groups of -related components, like genome location, conservation, etc.

      -

      Condition - a sample where the treatment, environmental -conditions, patient, time point or some combination of those is -varied.

      -

      The overall idea for functional enrichment is to determine which -pathways are statistically over-represented in one group versus another, -display statistically differential abundance from one group to another, -or are statistically differentially distributed in a ranked list based -on the abundance of one sample. Each of these purposes has a different -underlying statistical test (or family of tests) and the results of each -can be interpreted in somewhat different ways. The purpose of this -vignette is to give the user a very brief introduction on how to use the -package, not to discuss the underlying statistical choices that need to -be made when analyzing such data.

      -

      There are a number of caveats (probably non-exhaustive) with doing -this kind of analysis.

      -
      -
      -

      Important points for consideration -

      -
      -

      Data normalization -

      -

      One important point is to use data that’s been normalized in a -particular way to do these analyses. Data here has been normalized as a -Z score by row (gene/protein/etc.). So, for each row, calculate -the mean and standard deviation across all the conditions (columns) and -then express as a Z score.

      -

      Here’s why. All high-throughput technologies (microarray, RNAseq, -MS-assisted proteomics, metabolomics, lipidomics, etc.) suffer from the -same limitation. The detectability of each molecule being detected -(protein, RNA, etc.) is different and, in general, it’s impossible to -accurately determine how detectable each one is. The multi-omic -functional enrichment process lumps together measurements from different -components (proteins, genes, etc.) to summarize a pathway. If the -component measurements aren’t directly comparable (they aren’t) then -this can and will introduce significant systematic errors and won’t -produce the results you’re looking for. Careful consideration must be -given that the results of the analysis reflect the question being asked -and that the normalization method hasn’t obscured the desired -results.

      -
      -
      -

      Background -

      -

      The background of comparison for functional enrichment is always -important, but it mainly impacts the Fisher’s exact tests in the -examples below. The background answers the question: “My functional -group of interest is statistically enriched relative to what?” For -Fisher’s exact tests this is critical. Generally, it is best to compare -enrichment against the components observed in the data (the experiment’s -universe) rather than the universe of all possible components. For -example, a proteomics dataset from plasma may contain a limited set of -proteins compared with all possible human proteins; using the observed -proteins as the background usually yields more meaningful results. Using -all possible proteins will result in substantially different -findings.

      -
      -
      -

      Multiple hypothesis correction -

      -

      When testing the statistical significance of differences in a lot of -pathways it’s necessary to correct for multiple hypotheses. This -essentially accounts for the possibility you might see SOMETHING -significant by chance if you just test enough things- so it moves p -values in a less significant direction. The more things you test, the -greater this move will be. So pathway databases with lots of pathways -are affected more by this correction, making it harder to get a -significant result (which is a good thing actually).

      -
      -
      -

      Pathway databases -

      -

      Two ‘databases’ (organized text files) are included for pathways. The -example is taken from the NCI’s Pathway Interaction Database (PID) and -covers signaling pathways in human - but is no longer being actively -maintained. They can be loaded as follows:

      -
      -data(ncipid)
      -
      -
      -

      Identifiers -

      -

      The identifiers (gene names, e.g.) for the data input MUST match the -identifiers used in the pathway database. The two included human -databases use the HGNC-approved gene names. Which means your data has to -use the same identifiers.

      -
      -
      -

      Example data -

      -

      A sample data set is included that is from the CPTAC study of 169 -ovarian tumors. We include the dataset as a object, containing three -assays (transcriptomics, global proteomics, and phosphoproteomics) to -enable interoperability with other tools, and store example file as -rda on Figshare -as example.

      -

      This data can be loaded as follows:

      -
      -# Inspect the `pset` SummarizedExperiment.
-str(pset)
-#> Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
-#>   ..@ colData        :Formal class 'DFrame' [package "S4Vectors"] with 6 slots
-#>   .. .. ..@ rownames       : chr [1:174] "TCGA-09-1664" "TCGA-09-2056" "TCGA-13-1404" "TCGA-13-1409" ...
-#>   .. .. ..@ nrows          : int 174
-#>   .. .. ..@ elementType    : chr "ANY"
-#>   .. .. ..@ elementMetadata: NULL
-#>   .. .. ..@ metadata       : list()
-#>   .. .. ..@ listData       : Named list()
-#>   ..@ assays         :Formal class 'SimpleAssays' [package "SummarizedExperiment"] with 1 slot
-#>   .. .. ..@ data:Formal class 'SimpleList' [package "S4Vectors"] with 4 slots
-#>   .. .. .. .. ..@ listData       :List of 1
-#>   .. .. .. .. .. ..$ proteomics: num [1:18632, 1:174] -4.0649 -0.1398 -0.0366 0.768 0.2437 ...
-#>   .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
-#>   .. .. .. .. .. .. .. ..$ : chr [1:18632] "C9orf152" "ELMO2" "RPS11" "CREB3L1" ...
-#>   .. .. .. .. .. .. .. ..$ : chr [1:174] "TCGA-09-1664" "TCGA-09-2056" "TCGA-13-1404" "TCGA-13-1409" ...
-#>   .. .. .. .. ..@ elementType    : chr "ANY"
-#>   .. .. .. .. ..@ elementMetadata: NULL
-#>   .. .. .. .. ..@ metadata       : list()
-#>   ..@ NAMES          : chr [1:18632] "C9orf152" "ELMO2" "RPS11" "CREB3L1" ...
-#>   ..@ elementMetadata:Formal class 'DFrame' [package "S4Vectors"] with 6 slots
-#>   .. .. ..@ rownames       : NULL
-#>   .. .. ..@ nrows          : int 18632
-#>   .. .. ..@ elementType    : chr "ANY"
-#>   .. .. ..@ elementMetadata: NULL
-#>   .. .. ..@ metadata       : list()
-#>   .. .. ..@ listData       : Named list()
-#>   ..@ metadata       : list()
-dim(SummarizedExperiment::assay(pset, "proteomics"))
-#> [1] 18632   174
-head(rownames(pset))
-#> [1] "C9orf152" "ELMO2"    "RPS11"    "CREB3L1"  "PNMA1"    "MMP2"
-head(colnames(pset))
-#> [1] "TCGA-09-1664" "TCGA-09-2056" "TCGA-13-1404" "TCGA-13-1409" "TCGA-13-1410"
-#> [6] "TCGA-13-1482"
      -

      We also include some groups of patients to compare stored as R data -objects:

      -
      -data(shortlist)
-data(longlist)
-
-## columns that we want to use for results
-
-cols_to_display <- c("ingroup_n", "outgroup_n", "background_n", 
-                     "pvalue", "BH_pvalue")
      -

      The data are now loaded and ready to go through some of the -examples.

      -
      -
      -
      -
      -

      Examples -

      -

      We include five examples of how to use this tool, depending on the -analysis at hand. ## Comparison of one condition/group versus another -condition/group.

      -

      There are a number of ways to do this. I generally use a simple -approach which assesses the statistical difference in distributions -between the abundance values from all the members of a pathway in all -the group members from one group with those from the other group using a -t test.

      -
      -

      Caveat -

      -

      This is a ‘bag of values’ approach and it does not pay attention to -the relationships between values in different groups (i.e. that each -group has measurements for the same component). There are likely issues -that rise because of this and caveats associated with it. However, it -works fairly well.

      -
      -
      -

      Description -

      -

      In this example we are assessing the enrichment of pathways in a -group of short surviving patients versus in a group of long -surviving
      -patients. We can also do a single patient-to-patient comparison or -compare a single patient to a group of patients.

      -
      -
      -

      Interpretation -

      -

      Better corrected p-values are more enriched. However, you can get -good p-values when the algorithm only considers a limited number of -components from a pathway. That is, the pathway may have 30 members and -the p-value is coming from values from just 3 members. You can look at -the ingroup_n column from the result matrix to see this -(and screen out if desired).

      -

      It is VERY important to also consider the effect size. That is, the -difference between the mean of one group and the mean of the other -group. If there are large numbers of components in the pathway being -compared it is relatively easy to get a significant p value with small -effect size. Though this may be a real difference it is often not as -interesting as a smaller group with worse p value and greater effect -size. You can look at the effect size by comparing the -ingroup_mean and outgroup_mean columns.

      -
      -# in this example we lump a bunch of patients together (the 'short survivors')
-# and compare them to another group (the 'long survivors')
-
-### using enrichment_wrapper function
-protdata.enrichment.svl <- leapR::leapR(
-  geneset = ncipid,
-  enrichment_method = "enrichment_comparison",
-  eset = pset,
-  assay_name = "proteomics",
-  primary_columns = shortlist,
-  secondary_columns = longlist
-)
-
-or <- order(unlist(protdata.enrichment.svl[, "pvalue"]))
-rmarkdown::paged_table(protdata.enrichment.svl[or, cols_to_display])
      -
      - -
      -
      -# another application is to compare just one patient against another
-# (this would be the  equivalent of comparing one time point to another)
-
-### using enrichment_wrapper function
-protdata.enrichment.svl.ovo <- leapR::leapR(
-  geneset = ncipid,
-  enrichment_method = "enrichment_comparison",
-  eset = pset,
-  assay_name = "proteomics",
-  primary_columns = shortlist[1],
-  secondary_columns = longlist[1]
-)
-or <- order(unlist(protdata.enrichment.svl.ovo[, "pvalue"]))
-rmarkdown::paged_table(protdata.enrichment.svl.ovo[or, cols_to_display])
      -
      - -
      -

      When we only compare one sample to another, we get no enriched -pathways.

      -
      -
      -

      Fisher’s exact test -

      -

      For this test I use Fisher’s exact which is a simple comparison of -the overlap of two sets (think of it like a statistical Venn diagram -with two groups). It’s also referred to as a hypergeometric test.

      -
      -

      Caveats -

      -

      Caveat 1. Fisher’s exact does not consider abundance values -but only lists of components. Generally this requires some separation of -a group of interest using differential expression, module membership -(from a network for example), or some other method.

      -

      Caveat 2. The choice of background for comparison can make a -big difference on outcome. For example, in a proteomics experiment where -you’re looking at enrichment in a group of highly differentially -expressed proteins, you could choose to use all possible proteins as a -background, or you could use just those proteins that were observed by -proteomics (generally a much more limited set). The second option is -generally the best since the first options will result in (partly to -mostly) functions that are enriched in proteins that are seen in -proteomics. That is, the most abundant proteins, which is generally not -the desired outcome.

      -
      -
      -

      Description -

      -

      In the example below I construct a genelist of interest using a -simple abundance threshold on the data then use a background of all the -genes in the example dataset (which is a limited number). I then do a -simple hierarchical clustering on the data, extract modules, and step -through each module to calculate enrichment for them, outputting the -results into a separate text file.

      -
      -
      -

      Interpretation -

      -

      As with the t test comparison above it is important to look at the -number of pathway members included in the comparison (look at the -in_path column). There is no ‘effect size’ problem with Fisher’s exact -since it’s just a set comparison, but it’s important to note that -significant p values can arise from a pathway being -underrepresented in the genelist, which often times is not the -desired result. The foldx column gives a ratio of in versus not in the -genelist, values > 1 being enriched and <1 being depleted.

      -
      -# for this example we will construct a list of genes from the expression data
-#     to emulate what you might be inputting
-genelist <- rownames(pset)[which(SummarizedExperiment::assay(pset, 
-                                                    "proteomics")[, 1] > 0.5)]
-
-protdata.enrichment.sets.test <- leapR::leapR(
-  geneset = ncipid,
-  enrichment_method = "enrichment_in_sets",
-  eset = pset,
-  assay_name = "proteomics",
-  targets = genelist
-)
-or <- order(protdata.enrichment.sets.test[, "pvalue"])
-rmarkdown::paged_table(protdata.enrichment.sets.test[or, cols_to_display])
      -
      - -
      -
      -
-
-
-# in this example we construct some modules from the hierarchical clustering
-#   of the   data
-protdata_naf <- SummarizedExperiment::assay(pset, "proteomics")
-
-# hierarchical clustering is not too happy with lots of missing values
-#    so we'll do a zero fill on this to get the modules
-protdata_naf[which(is.na(protdata_naf))] <- 0
-
-# construct the hierarchical clustering using the 'wardD' method, which
-#    seems to give more even sized modules
-protdata_hc <- hclust(dist(protdata_naf), method = "ward.D2")
-
-# arbitrarily we'll chop the clusters into 5 modules
-modules <- cutree(protdata_hc, k = 5)
-
-## sara: created list
-clusters <- lapply(unique(modules), function(x) names(which(modules == x)))
-
-# modules is a named list of values where each value is a module
-#         number and the name is the gene name
-
-# To do enrichment for one module (module 1 in this case) do this
-protdata.enrichment.sets.module_1 <- leapR::leapR(
-  geneset = ncipid,
-  enrichment_method = "enrichment_in_sets",
-  eset = pset,
-  assay_name = "proteomics",
-  targets = names(modules[which(modules == 1)])
-)
-
-# To do enrichment on all modules and return the list of enrichment results
-protdata.enrichment.sets.modules <- do.call(rbind, 
-                                            leapR::cluster_enrichment(
-                                                eset = pset,
-                                                assay_name= 'proteomics',
-                                                geneset = ncipid,
-                                                clusters = clusters, 
-                                                sigfilter = 0.25))
-## nothing is enriched
-rmarkdown::paged_table(protdata.enrichment.sets.modules[, cols_to_display])
      -
      - -
      -
      -
      Visualizing Fisher’s exact results -
      -
      -# Plot the top enriched gene sets from Fisher's exact test
-# Stars indicate significance (None seen here)
-plot_leapr_bar(
-  protdata.enrichment.sets.test,
-  title = "Fisher's Exact Test: Top Enriched Pathways",
-  top_n = 10,
-  star_thresholds = c(0.05, 0.01, 0.001),
-  wrap = 40
-)
      -

      -
      -
      -
      -
      -

      The Kolmogorov–Smirnov test (KS) -

      -

      Similar to the popular GSEA, KS tests whether a group of components -(the pathway) is distributed in a statistically significant manner in a -ranked list of components. That is, if all the members of the pathway -are clustered together at the top of the list (highly abundant, e.g.) or -at the bottom of the list (low abundance, e.g.) this will return good p -values. I should note that GSEA uses a more sophisticated approach than -this and their application has a lot of bells and whistles.

      -
      -

      Description -

      -

      In the example below I’m simply calculating enrichment for one of the -patients in the list (arbitrarily selected). The ranking value is -relative protein abundance in this case, but can be any continuous -measure or derived value. For example, you could calculate the topology -of all proteins in a network and use the topology measure (degree) as -the measure.

      -
      -
      -

      Interpretation -

      -

      Similar to the other examples be cautious of pathways with good p -values that consider a small number of pathway numbers (in_path column). -The MeanPath column gives a measure that shows how far above or below -the median the mean rank of the pathway is (normalized to -1,1). The -Zscore column is a Zscore calculated on the basis of the mean percentage -rank of the pathway relative to the mean of the entire list divided by -the standard deviation of the pathway rank. The foldx column expresses -the mean percentage rank of the pathway relative to the entire list - -closer to 0 is higher in the list and closer to 1 is closer to the -bottom of the list. Each of these should give consistent results, but -will be somewhat different.

      -
      -# This is how you calculate enrichment in a ranked list 
-# (for example from topology)
-### using enrichment_wrapper function
-protdata.enrichment.order <- leapR::leapR(
-  geneset = ncipid, "enrichment_in_order",
-  eset = pset,
-  method = 'ks',
-  assay_name = "proteomics",
-  primary_columns = shortlist[1]
-)
-
-
-or <- order(protdata.enrichment.order[, "pvalue"])
-rmarkdown::paged_table(protdata.enrichment.order[or, cols_to_display])
      -
      - -
      -
      -
      Visualizing KS test results -
      -
      -# Plot the ranked enrichment results
-plot_leapr_bar(
-  protdata.enrichment.order,
-  title = "Kolmogorov-Smirnov Test: Ranked Enrichment",
-  top_n = 12,
-  fill_sig = "#2166AC",
-  fill_ns = "#B2DFDB",
-  wrap = 38
-)
      -

      -
      -
      -
      -

      KS alternative: the one-sample Z test -

      -

      Given that the KS test has not always been the best for -gene set enrichment we also implement the one-sample z test.

      -
      -# This is how you calculate enrichment in a ranked list 
-# (for example from topology)
-### using enrichment_wrapper function
-protdata.enrichment.order <- leapR::leapR(
-  geneset = ncipid, "enrichment_in_order",
-  eset = pset,
-  method = 'ztest',
-  assay_name = "proteomics",
-  primary_columns = shortlist[1]
-)
-
-
-or <- order(protdata.enrichment.order[, "pvalue"])
-rmarkdown::paged_table(protdata.enrichment.order[or, cols_to_display])
      -
      - -
      -
      -
-plot_leapr_bar(
-  protdata.enrichment.order,
-  title = "Z Test: Ranked Enrichment",
-  top_n = 12,
-  fill_sig = "#2166AC",
-  fill_ns = "#B2DFDB",
-  wrap = 38
-)
      -

      -
      -
      -
      -

      Enrichment in Correlation -

      -

      The idea here is to use the correlation of pathway members to each -other versus to non-pathway members as a way to assess functional -enrichment. This idea seems sound- pathways that are varying in a -correlated way across a bunch of conditions (say time points or -patients) may be more active and more important than others. However, -more testing and validation is needed to show that this is the case.

      -
      -

      Interpretation -

      -

      The ingroup_mean gives the mean correlation of the -pathway members to each other and outgroup_mean gives the correlation of -the pathway members to non-pathway members. Background_mean gives the -mean correlation of all non-pathway members. The pvalue and -BH_pvalue are for the pathway members to each other versus -those pathway members to non-pathway components. The -pvalue_background and BH_pvalue_background are -for the pathway member correlation relative to non-pathway member -correlation (which is similar but slightly different than the other -p-values).

      -
      -### using enrichment_wrapper function
-protdata.enrichment.correlation <- leapR::leapR(
-  geneset = ncipid,
-  enrichment_method = "correlation_enrichment",
-  assay_name = "proteomics",
-  eset = pset
-)
-
-or <- order(protdata.enrichment.correlation[, "pvalue"])
-rmarkdown::paged_table(head(protdata.enrichment.correlation[or, 
-                                                      cols_to_display]))
      -
      - -
      -
      -
-protdata.enrichment.correlation.short <- leapR::leapR(
-  geneset = ncipid,
-  enrichment_method = "correlation_enrichment",
-  assay_name = "proteomics",
-  eset = pset[, shortlist]
-)
-or <- order(protdata.enrichment.correlation.short[, "pvalue"])
-rmarkdown::paged_table(head(protdata.enrichment.correlation.short[or, 
-                                                        cols_to_display]))
      -
      - -
      -
      -
-protdata.enrichment.correlation.long <- leapR::leapR(
-  geneset = ncipid,
-  enrichment_method = "correlation_enrichment",
-  assay_name = "proteomics",
-  eset = pset[, longlist]
-)
-or <- order(protdata.enrichment.correlation.long[, "pvalue"])
-rmarkdown::paged_table(head(protdata.enrichment.correlation.long[or, 
-                                                            cols_to_display]))
      -
      - -
      -
      -
      Visualizing correlation enrichment results -
      -
      -# Compare correlation patterns across conditions
-plot_leapr_bar(
-  protdata.enrichment.correlation.short,
-  title = "Correlation Enrichment: Short Survivors",
-  top_n = 10,
-  fill_sig = "#1B9E77",
-  fill_ns = "#D8F0E8",
-  wrap = 36
-)
      -

      -
      -
      -
      -
      -

      Phosphoproteomics data analysis -

      -

      In this example we will use phosphoproteomics data to assess the -enrichment in known kinase substrates (a proxy for kinase activity)

      -
      -data("kinasesubstrates")
-
-# for an individual tumor calculate the Kinase-Substrate 
-# Enrichment (similar to KSEA)
-#     This uses the site-specific phosphorylation data to determine 
-# which kinases
-#     might be active by assessing the enrichment of the 
-# phosphorylation of their known substrates
-
-phosphodata.ksea.order <- leapR::leapR(
-  geneset = kinasesubstrates,
-  enrichment_method = "enrichment_in_order",
-  assay_name = "phosphoproteomics",
-  eset = phset,
-  method = 'ztest', 
-  primary_columns = "TCGA-13-1484")
-
-or <- order(phosphodata.ksea.order[, "pvalue"])
-rmarkdown::paged_table(phosphodata.ksea.order[or, cols_to_display])
      -
      - -
      -
      -
-
-# now do the same thing but use a threshold
-phosphodata.sets.order <- leapR::leapR(
-  geneset = kinasesubstrates,
-  enrichment_method = "enrichment_in_sets",
-  eset = phset,
-  assay_name = "phosphoproteomics",
-  threshold = 0.5,
-  primary_columns = "TCGA-13-1484"
-)
-or <- order(phosphodata.sets.order[, "pvalue"])
-rmarkdown::paged_table(phosphodata.sets.order[or, cols_to_display])
      -
      - -
      -
      -
      Visualizing kinase substrate enrichment -
      -
      -plot <- plot_leapr_bar(
-  phosphodata.sets.order,
-  title            = "Kinase Substrate Enrichment (Phosphoproteomics)",
-  top_n            = 15,
-  star_thresholds  = c(0.05, 0.01, 1e-3),
-  wrap             = 36,
-  fill_sig         = "#0F766E",  # dark teal for significant
-  fill_ns          = "#99F6E4",  # light teal for non-significant
-  outline          = "grey30",
-  axis_text_y_size = 7,
-  axis_text_x_size = 8
-)
-
-plot
      -

      -
      -
-# You can also modify the plot further using standard ggplot2 arguments
-plot + ggplot2::labs(
-  y = expression(-log[10]("adjusted p-value")),
-  caption = "* p<0.05, ** p<0.01, *** p<0.001"
-)
      -

      -

      Lastly we print out the session info!

      -
      -sessionInfo()
-#> R version 4.5.2 (2025-10-31)
-#> Platform: aarch64-apple-darwin20
-#> Running under: macOS Tahoe 26.2
-#> 
-#> Matrix products: default
-#> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
-#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
-#> 
-#> locale:
-#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
-#> 
-#> time zone: America/Los_Angeles
-#> tzcode source: internal
-#> 
-#> attached base packages:
-#> [1] stats     graphics  grDevices utils     datasets  methods   base     
-#> 
-#> other attached packages:
-#> [1] stringr_1.6.0    tibble_3.3.1     dplyr_1.1.4      ggplot2_4.0.1   
-#> [5] rmarkdown_2.30   gplots_3.3.0     leapR_0.99.7     BiocStyle_2.38.0
-#> 
-#> loaded via a namespace (and not attached):
-#>  [1] SummarizedExperiment_1.40.0 gtable_0.3.6               
-#>  [3] xfun_0.56                   bslib_0.10.0               
-#>  [5] htmlwidgets_1.6.4           caTools_1.18.3             
-#>  [7] Biobase_2.70.0              lattice_0.22-7             
-#>  [9] tzdb_0.5.0                  vctrs_0.7.1                
-#> [11] tools_4.5.2                 bitops_1.0-9               
-#> [13] generics_0.1.4              stats4_4.5.2               
-#> [15] pkgconfig_2.0.3             Matrix_1.7-4               
-#> [17] KernSmooth_2.23-26          RColorBrewer_1.1-3         
-#> [19] S7_0.2.1                    desc_1.4.3                 
-#> [21] S4Vectors_0.48.0            lifecycle_1.0.5            
-#> [23] compiler_4.5.2              farver_2.1.2               
-#> [25] textshaping_1.0.4           Seqinfo_1.0.0              
-#> [27] htmltools_0.5.9             sass_0.4.10                
-#> [29] yaml_2.3.12                 pkgdown_2.2.0              
-#> [31] pillar_1.11.1               jquerylib_0.1.4            
-#> [33] DelayedArray_0.36.0         cachem_1.1.0               
-#> [35] abind_1.4-8                 gtools_3.9.5               
-#> [37] tidyselect_1.2.1            digest_0.6.39              
-#> [39] stringi_1.8.7               bookdown_0.46              
-#> [41] labeling_0.4.3              fastmap_1.2.0              
-#> [43] grid_4.5.2                  cli_3.6.5                  
-#> [45] SparseArray_1.10.8          magrittr_2.0.4             
-#> [47] S4Arrays_1.10.1             readr_2.1.6                
-#> [49] withr_3.0.2                 scales_1.4.0               
-#> [51] XVector_0.50.0              matrixStats_1.5.0          
-#> [53] otel_0.2.0                  ragg_1.5.0                 
-#> [55] hms_1.1.4                   evaluate_1.0.5             
-#> [57] knitr_1.51                  GenomicRanges_1.62.1       
-#> [59] IRanges_2.44.0              rlang_1.1.7                
-#> [61] glue_1.8.0                  BiocManager_1.30.27        
-#> [63] BiocGenerics_0.56.0         rstudioapi_0.18.0          
-#> [65] jsonlite_2.0.0              R6_2.6.1                   
-#> [67] MatrixGenerics_1.22.0       systemfonts_1.3.1          
-#> [69] fs_1.6.6
      -
      -
      -
      -
      -
      - - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      -
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If isCallable(callback) is false, throw a TypeError exception. - // See: http://es5.github.com/#x9.11 - if (typeof callback !== "function") { - throw new TypeError(callback + ' is not a function'); - } - - // 5. If thisArg was supplied, let T be thisArg; else let - // T be undefined. - if (arguments.length > 1) { - T = thisArg; - } - - // 6. Let k be 0 - k = 0; - - // 7. Repeat, while k < len - while (k < len) { - - var kValue; - - // a. Let Pk be ToString(k). - // This is implicit for LHS operands of the in operator - // b. Let kPresent be the result of calling the HasProperty - // internal method of O with argument Pk. - // This step can be combined with c - // c. If kPresent is true, then - if (k in O) { - - // i. Let kValue be the result of calling the Get internal - // method of O with argument Pk. - kValue = O[k]; - - // ii. Call the Call internal method of callback with T as - // the this value and argument list containing kValue, k, and O. - callback.call(T, kValue, k, O); - } - // d. Increase k by 1. - k++; - } - // 8. return undefined - }; -} - -// Production steps of ECMA-262, Edition 5, 15.4.4.19 -// Reference: http://es5.github.io/#x15.4.4.19 -if (!Array.prototype.map) { - - Array.prototype.map = function(callback, thisArg) { - - var T, A, k; - - if (this == null) { - throw new TypeError(' this is null or not defined'); - } - - // 1. Let O be the result of calling ToObject passing the |this| - // value as the argument. - var O = Object(this); - - // 2. Let lenValue be the result of calling the Get internal - // method of O with the argument "length". - // 3. Let len be ToUint32(lenValue). - var len = O.length >>> 0; - - // 4. If IsCallable(callback) is false, throw a TypeError exception. - // See: http://es5.github.com/#x9.11 - if (typeof callback !== 'function') { - throw new TypeError(callback + ' is not a function'); - } - - // 5. If thisArg was supplied, let T be thisArg; else let T be undefined. - if (arguments.length > 1) { - T = thisArg; - } - - // 6. Let A be a new array created as if by the expression new Array(len) - // where Array is the standard built-in constructor with that name and - // len is the value of len. - A = new Array(len); - - // 7. Let k be 0 - k = 0; - - // 8. Repeat, while k < len - while (k < len) { - - var kValue, mappedValue; - - // a. Let Pk be ToString(k). - // This is implicit for LHS operands of the in operator - // b. Let kPresent be the result of calling the HasProperty internal - // method of O with argument Pk. - // This step can be combined with c - // c. If kPresent is true, then - if (k in O) { - - // i. Let kValue be the result of calling the Get internal - // method of O with argument Pk. - kValue = O[k]; - - // ii. Let mappedValue be the result of calling the Call internal - // method of callback with T as the this value and argument - // list containing kValue, k, and O. - mappedValue = callback.call(T, kValue, k, O); - - // iii. Call the DefineOwnProperty internal method of A with arguments - // Pk, Property Descriptor - // { Value: mappedValue, - // Writable: true, - // Enumerable: true, - // Configurable: true }, - // and false. - - // In browsers that support Object.defineProperty, use the following: - // Object.defineProperty(A, k, { - // value: mappedValue, - // writable: true, - // enumerable: true, - // configurable: true - // }); - - // For best browser support, use the following: - A[k] = mappedValue; - } - // d. Increase k by 1. - k++; - } - - // 9. return A - return A; - }; -} - -var PagedTable = function (pagedTable) { - var me = this; - - var source = function(pagedTable) { - var sourceElems = [].slice.call(pagedTable.children).filter(function(e) { - return e.hasAttribute("data-pagedtable-source"); - }); - - if (sourceElems === null || sourceElems.length !== 1) { - throw("A single data-pagedtable-source was not found"); - } - - return JSON.parse(sourceElems[0].innerHTML); - }(pagedTable); - - var options = function(source) { - var options = typeof(source.options) !== "undefined" && - source.options !== null ? source.options : {}; - - var columns = typeof(options.columns) !== "undefined" ? options.columns : {}; - var rows = typeof(options.rows) !== "undefined" ? options.rows : {}; - - var positiveIntOrNull = function(value) { - return parseInt(value) >= 0 ? parseInt(value) : null; - }; - - return { - pages: positiveIntOrNull(options.pages), - rows: { - min: positiveIntOrNull(rows.min), - max: positiveIntOrNull(rows.max), - total: positiveIntOrNull(rows.total) - }, - columns: { - min: positiveIntOrNull(columns.min), - max: positiveIntOrNull(columns.max), - total: positiveIntOrNull(columns.total) - } - }; - }(source); - - var Measurer = function() { - - // set some default initial values that will get adjusted in runtime - me.measures = { - padding: 12, - character: 8, - height: 15, - defaults: true - }; - - me.calculate = function(measuresCell) { - if (!me.measures.defaults) - return; - - var measuresCellStyle = window.getComputedStyle(measuresCell, null); - - var newPadding = parsePadding(measuresCellStyle.paddingLeft) + - parsePadding(measuresCellStyle.paddingRight); - - var sampleString = "ABCDEFGHIJ0123456789"; - var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length); - - if (newPadding <= 0 || newCharacter <= 0) - return; - - me.measures.padding = newPadding; - me.measures.character = newCharacter; - me.measures.height = measuresCell.clientHeight; - me.measures.defaults = false; - }; - - return me; - }; - - var Page = function(data, options) { - var me = this; - - var defaults = { - max: 7, - rows: 10 - }; - - var totalPages = function() { - return Math.ceil(data.length / me.rows); - }; - - me.number = 0; - me.max = options.pages !== null ? options.pages : defaults.max; - me.visible = me.max; - me.rows = options.rows.min !== null ? options.rows.min : defaults.rows; - me.total = totalPages(); - - me.setRows = function(newRows) { - me.rows = newRows; - me.total = totalPages(); - }; - - me.setPageNumber = function(newPageNumber) { - if (newPageNumber < 0) newPageNumber = 0; - if (newPageNumber >= me.total) newPageNumber = me.total - 1; - - me.number = newPageNumber; - }; - - me.setVisiblePages = function(visiblePages) { - me.visible = Math.min(me.max, visiblePages); - me.setPageNumber(me.number); - }; - - me.getVisiblePageRange = function() { - var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0); - var end = me.number + Math.floor(me.visible / 2) + 1; - var pageCount = me.total; - - if (start < 0) { - var diffToStart = 0 - start; - start += diffToStart; - end += diffToStart; - } - - if (end > pageCount) { - var diffToEnd = end - pageCount; - start -= diffToEnd; - end -= diffToEnd; - } - - start = start < 0 ? 0 : start; - end = end >= pageCount ? pageCount : end; - - var first = false; - var last = false; - - if (start > 0 && me.visible > 1) { - start = start + 1; - first = true; - } - - if (end < pageCount && me.visible > 2) { - end = end - 1; - last = true; - } - - return { - first: first, - start: start, - end: end, - last: last - }; - }; - - me.getRowStart = function() { - var rowStart = page.number * page.rows; - if (rowStart < 0) - rowStart = 0; - - return rowStart; - }; - - me.getRowEnd = function() { - var rowStart = me.getRowStart(); - return Math.min(rowStart + me.rows, data.length); - }; - - me.getPaddingRows = function() { - var rowStart = me.getRowStart(); - var rowEnd = me.getRowEnd(); - return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0; - }; - }; - - var Columns = function(data, columns, options) { - var me = this; - - me.defaults = { - min: 5 - }; - - me.number = 0; - me.visible = 0; - me.total = columns.length; - me.subset = []; - me.padding = 0; - me.min = options.columns.min !== null ? options.columns.min : me.defaults.min; - me.max = options.columns.max !== null ? options.columns.max : null; - me.widths = {}; - - var widthsLookAhead = Math.max(100, options.rows.min); - var paddingColChars = 10; - - me.emptyNames = function() { - columns.forEach(function(column) { - if (columns.label !== null && columns.label !== "") - return false; - }); - - return true; - }; - - var parsePadding = function(value) { - return parseInt(value) >= 0 ? parseInt(value) : 0; - }; - - me.calculateWidths = function(measures) { - columns.forEach(function(column) { - var maxChars = Math.max( - column.label.toString().length, - column.type.toString().length - ); - - for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) { - maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length); - } - - me.widths[column.name] = { - // width in characters - chars: maxChars, - // width for the inner html columns - inner: maxChars * measures.character, - // width adding outer styles like padding - outer: maxChars * measures.character + measures.padding - }; - }); - }; - - me.getWidth = function() { - var widthOuter = 0; - for (var idxCol = 0; idxCol < me.subset.length; idxCol++) { - var columnName = me.subset[idxCol].name; - widthOuter = widthOuter + me.widths[columnName].outer; - } - - widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character; - - if (me.hasMoreLeftColumns()) { - widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; - } - - if (me.hasMoreRightColumns()) { - widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; - } - - return widthOuter; - }; - - me.updateSlice = function() { - if (me.number + me.visible >= me.total) - me.number = me.total - me.visible; - - if (me.number < 0) me.number = 0; - - me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total)); - - me.subset = me.subset.map(function(column) { - Object.keys(column).forEach(function(colKey) { - column[colKey] = column[colKey] === null ? "" : column[colKey].toString(); - }); - - column.width = null; - return column; - }); - }; - - me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) { - me.number = columnNumber; - me.visible = newVisibleColumns; - me.padding = paddingCount; - - me.updateSlice(); - }; - - me.incColumnNumber = function(increment) { - me.number = me.number + increment; - }; - - me.setColumnNumber = function(newNumber) { - me.number = newNumber; - }; - - me.setPaddingCount = function(newPadding) { - me.padding = newPadding; - }; - - me.getPaddingCount = function() { - return me.padding; - }; - - me.hasMoreLeftColumns = function() { - return me.number > 0; - }; - - me.hasMoreRightColumns = function() { - return me.number + me.visible < me.total; - }; - - me.updateSlice(0); - return me; - }; - - var data = source.data; - var page = new Page(data, options); - var measurer = new Measurer(data, options); - var columns = new Columns(data, source.columns, options); - - var table = null; - var tableDiv = null; - var header = null; - var footer = null; - var tbody = null; - - // Caches pagedTable.clientWidth, specially for webkit - var cachedPagedTableClientWidth = null; - - var onChangeCallbacks = []; - - var clearSelection = function() { - if(document.selection && document.selection.empty) { - document.selection.empty(); - } else if(window.getSelection) { - var sel = window.getSelection(); - sel.removeAllRanges(); - } - }; - - var columnNavigationWidthPX = 5; - - var renderColumnNavigation = function(increment, backwards) { - var arrow = document.createElement("div"); - arrow.setAttribute("style", - "border-top: " + columnNavigationWidthPX + "px solid transparent;" + - "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" + - "border-" + (backwards ? 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parseInt(value) : 0; - }; - - me.fixedHeight = function() { - return options.rows.max != null; - } - - me.fitRows = function() { - if (me.fixedHeight()) - return; - - measurer.calculate(measuresCell); - - var rows = options.rows.min !== null ? options.rows.min : 0; - var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0; - var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0; - - if (pagedTable.offsetHeight > 0) { - var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight; - rows = Math.floor((availableHeight) / measurer.measures.height); - } - - rows = options.rows.min !== null ? 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      Authors and Citation

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      Authors

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        Sara Gosline. Author, maintainer. -

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        Jason McDermott. Author. -

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        Jeremy Jacobson. Author. -

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        Vincent Danna. Contributor. -

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        National Institutes of Health. Funder. -

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      Citation

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      Gosline S, McDermott J, Jacobson J (2026). -leapR: Layered enrichment analysis of pathways R. -R package version 0.99.7. -

      - 
      @Manual{,
-  title = {leapR: Layered enrichment analysis of pathways R},
-  author = {Sara Gosline and Jason McDermott and Jeremy Jacobson},
-  year = {2026},
-  note = {R package version 0.99.7},
-}
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      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
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      Site built with pkgdown 2.2.0.

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      - - - - - - - diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index 72b8497..0000000 --- a/docs/index.html +++ /dev/null @@ -1,185 +0,0 @@ - - - - - - - -Layered enrichment analysis of pathways R • leapR - - - - - - - - - - Skip to contents - - -
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      leapR -

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      Layered Enrichment Analysis of Pathways in R (leapR) a tool that carries out statistical enrichment analysis on single- or multi-omics data.

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      Install -

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      leapR is available through Bioconductor repository here

      -
      if (!require("BiocManager", quietly = TRUE))
-    install.packages("BiocManager")
-BiocManager::install(version = "3.21")
-BiocManager::install('BiocStyle')
-BiocManager::install('leapR')
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      Once you have successfully installed the package you can load the vignette to read examples using the vignette('leapR') command.

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      Basic Usage -

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      The primary function of the leapR package is the leapR function itself. This function serves a wrapper to run different styles of enrichment functions on the data. The package contains other functions to support pathway information and multi-omics datasets.

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      Enrichment calls -

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      Here is a list of enrichment arguments that can be called with the leapR command.

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      ArgumentDescription
      enrichment_in_setsCalculates enrichment in pathway membership in a list (e.g. highly differential proteins) relative to background using Fisher’s exact test.
      enrichment_in_orderCalculates enrichment of pathways based on a ranked list using the Kolmogorov-Smirnov test
      enrichment_comparisonCompares the distribution of abundances between two sets of conditions for each pathway using a t test
      enrichment_in_pathwaysCompares the distribution of abundances in a pathway with the background distribution of abundances using a t test
      correlation_enrichmentCalculates the enrichment of a pathway based on correlation between pathway members across conditions versus correlation between members not in the pathway
      enrichment_in_relationshipsCalculates the enrichment of a pathway in specified interactions relative to non-pathway members
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      Data examples -

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      We included examples of including proteomics data and transcriptomics data from 169 high-grade serous ovarian cancer (HGSOC) tumors previously studied and lists of the short- and long- surviving patients from that cohort.

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      Gene pathway examples -

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      We included two different gene pathways. An NCI pathway database (Pathway Information Database; PID) of signaling pathways and the MSIGDB set of gene collections from various sources.

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      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
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      Site built with pkgdown 2.2.0.

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      - -
      -
      - - - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad827..0000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/pkgdown.js b/docs/pkgdown.js deleted file mode 100644 index 0a5573a..0000000 --- a/docs/pkgdown.js +++ /dev/null @@ -1,162 +0,0 @@ -/* http://gregfranko.com/blog/jquery-best-practices/ */ -(function ($) { - $(function () { - - $('nav.navbar').headroom(); - - Toc.init({ - $nav: $("#toc"), - $scope: $("main h2, main h3, main h4, main h5, main h6") - }); - - if ($('#toc').length) { - $('body').scrollspy({ - target: '#toc', - offset: $("nav.navbar").outerHeight() + 1 - }); - } - - // Activate popovers - $('[data-bs-toggle="popover"]').popover({ - container: 'body', - html: true, - trigger: 'focus', - placement: "top", - sanitize: false, - }); - - $('[data-bs-toggle="tooltip"]').tooltip(); - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle = element.getAttribute('data-bs-original-title'); - element.setAttribute('data-bs-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-bs-original-title', tooltipOriginalTitle); - } - - if (ClipboardJS.isSupported()) { - $(document).ready(function () { - var copyButton = ""; - - $("div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({ container: 'body' }); - - // Initialize clipboard: - var clipboard = new ClipboardJS('[data-clipboard-copy]', { - text: function (trigger) { - return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); - } - }); - - clipboard.on('success', function (e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboard.on('error', function (e) { - changeTooltipMessage(e.trigger, 'Press Ctrl+C or Command+C to copy'); - }); - - }); - } - - /* Search marking --------------------------*/ - var url = new URL(window.location.href); - var toMark = url.searchParams.get("q"); - var mark = new Mark("main#main"); - if (toMark) { - mark.mark(toMark, { - accuracy: { - value: "complementary", - limiters: [",", ".", ":", "/"], - } - }); - } - - /* Search --------------------------*/ - /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ - // Initialise search index on focus - var fuse; - $("#search-input").focus(async function (e) { - if (fuse) { - return; - } - - $(e.target).addClass("loading"); - var response = await fetch($("#search-input").data("search-index")); - var data = await response.json(); - - var options = { - keys: ["what", "text", "code"], - ignoreLocation: true, - threshold: 0.1, - includeMatches: true, - includeScore: true, - }; - fuse = new Fuse(data, options); - - $(e.target).removeClass("loading"); - }); - - // Use algolia autocomplete - var options = { - autoselect: true, - debug: true, - hint: false, - minLength: 2, - }; - var q; - async function searchFuse(query, callback) { - await fuse; - - var items; - if (!fuse) { - items = []; - } else { - q = query; - var results = fuse.search(query, { limit: 20 }); - items = results - .filter((x) => x.score <= 0.75) - .map((x) => x.item); - if (items.length === 0) { - items = [{ dir: "Sorry 😿", previous_headings: "", title: "No results found.", what: "No results found.", path: window.location.href }]; - } - } - callback(items); - } - $("#search-input").autocomplete(options, [ - { - name: "content", - source: searchFuse, - templates: { - suggestion: (s) => { - if (s.title == s.what) { - return `${s.dir} >
      ${s.title}
      `; - } else if (s.previous_headings == "") { - return `${s.dir} >
      ${s.title}
      > ${s.what}`; - } else { - return `${s.dir} >
      ${s.title}
      > ${s.previous_headings} > ${s.what}`; - } - }, - }, - }, - ]).on('autocomplete:selected', function (event, s) { - window.location.href = s.path + "?q=" + q + "#" + s.id; - }); - }); -})(window.jQuery || window.$) - -document.addEventListener('keydown', function (event) { - // Check if the pressed key is '/' - if (event.key === '/') { - event.preventDefault(); // Prevent any default action associated with the '/' key - document.getElementById('search-input').focus(); // Set focus to the search input - } -}); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index 78646a0..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,11 +0,0 @@ -pandoc: 3.7.0.2 -pkgdown: 2.2.0 -pkgdown_sha: ~ -articles: - examples: examples.html - leapR: leapR.html - order-enrichment: order-enrichment.html -last_built: 2026-02-11T18:27Z -urls: - reference: https://pnnl-github.io/leapR/reference - article: https://pnnl-github.io/leapR/articles diff --git a/docs/reference/calcTTest.html b/docs/reference/calcTTest.html deleted file mode 100644 index 6028732..0000000 --- a/docs/reference/calcTTest.html +++ /dev/null @@ -1,127 +0,0 @@ - -calcTTest — calcTTest • leapR - Skip to contents - - -
      -
      -
      - -

      calcTTest

      - -
      calcTTest.Rd
      -
      - -
      -

      calculates a t-test for two distributions of data on a per-gene basis -append results to ExpressionSet with two extra columns: `pvalue` and -`difference` for each feature

      -
      - -
      -

      Usage

      - 
      calcTTest(eset, assay_name, group1, group2)
      -
      - -
      -

      Arguments

      - - -
      eset
      -

      SummarizedExperiment

      - - -
      assay_name
      -

      name of assay

      - - -
      group1
      -

      List of samples comprising group 1

      - - -
      group2
      -

      List of samples comprising group 2

      - -
      -
      -

      Value

      -

      An Expression set with two columns added to the featureData -slot: pvalue, and estimate

      -
      - -
      -

      Examples

      - 
      
-        library(leapR)
-        url <- "https://api.figshare.com/v2/file/download/56536214"
-        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
-        load('transData.rda')
-        p <- file.remove("transData.rda")
-
-        # read in the pathways
-        data("ncipid")
-
-        # read in the patient groups
-        data("shortlist")
-        data("longlist")
-        calcTTest(tset, 'transcriptomics', shortlist, longlist)
-#> class: SummarizedExperiment 
-#> dim: 1999 174 
-#> metadata(0):
-#> assays(1): transcriptomics
-#> rownames(1999): NOC2L ISG15 ... ARL6 MINA
-#> rowData names(2): pvalue difference
-#> colnames(174): TCGA-13-1484 TCGA-13-1495 ... TCGA-61-1995 TCGA-61-2008
-#> colData names(0):
-
      -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/cluster_enrichment.html b/docs/reference/cluster_enrichment.html deleted file mode 100644 index 2df9b6d..0000000 --- a/docs/reference/cluster_enrichment.html +++ /dev/null @@ -1,144 +0,0 @@ - -cluster_enrichment — cluster_enrichment • leapR - Skip to contents - - -
      -
      -
      - -

      cluster_enrichment

      - -
      cluster_enrichment.Rd
      -
      - -
      -

      Cluster enrichment Run enrichment (Fisher's exact) on clusters (lists of -identifier groups)

      -
      - -
      -

      Usage

      - 
      cluster_enrichment(eset, assay_name, geneset, clusters, sigfilter = 0.05)
      -
      - -
      -

      Arguments

      - - -
      eset
      -

      is an SummarizedExperiment containing data that is clustered

      - - -
      assay_name
      -

      is the name of the assay

      - - -
      geneset
      -

      is a GeneSet object for pathway annotation

      - - -
      clusters
      -

      is a list of clusters (gene lists) to calculate enrichment -on, generally the result of the `cutree` function

      - - -
      sigfilter
      -

      minimum significance threshold default is .05

      - -
      -
      -

      Value

      -

      data frame with enrichment results

      -
      -
      -

      Details

      -

      This function will calculate enrichment (Fisher's exact test for - membership overlap) on

      -

      a series of lists of genes, such as from a set of clusters. The -results are returned as

      -

      a list of results matrices in the order of the input clusters.

      -
      - -
      -

      Examples

      - 
              library(leapR)
-
-        # read in the example transcriptomic data
-        url <- "https://api.figshare.com/v2/file/download/56536214"
-        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
-        load('transData.rda')
-        p <- file.remove("transData.rda")
-
-        # read in the pathways
-        data("ncipid")
-
-        # for the example we will limit the number of transcripts considered
-        #- arbitrarily in this case
-        transdata <- SummarizedExperiment::assay(tset,'transcriptomics')
-        transdata[which(is.na(transdata),arr.ind=TRUE)]<-0.0
-        # perform heirarchical clustering on the  data
-        transdata.hc <- hclust(dist(transdata), method="ward.D2")
-
-        transdata.hc.clusters <- cutree(transdata.hc, k=5)
-        clust.list <- lapply(seq_len(5), function(x) {
-           return(names(which(transdata.hc.clusters==x)))})
-        #calculates enrichment for each of the clusters individually a
-        #and returns a list of enrichment results
-        transdata.hc.enrichment <- leapR::cluster_enrichment(eset=tset,
-                assay_name='transcriptomics',
-                geneset=ncipid,
-                clusters=clust.list)
-
-
-
-
      -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/combine_omics.html b/docs/reference/combine_omics.html deleted file mode 100644 index dd852c2..0000000 --- a/docs/reference/combine_omics.html +++ /dev/null @@ -1,130 +0,0 @@ - -combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids. — combine_omics • leapR - Skip to contents - - -
      -
      -
      - -

      combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

      - -
      combine_omics.Rd
      -
      - -
      -

      combine_omics -Combine two or more omics matrices into one multi-omics matrix with -'tagged' ids.

      -
      - -
      -

      Usage

      - 
      combine_omics(omics_list, id_list = rep(NA, length(omics_list)))
      -
      - -
      -

      Arguments

      - - -
      omics_list
      -

      Is a list of SummarizedExperiment each with -one assay

      - - -
      id_list
      -

      List of identifiers to use, in the same order as the -omics_list elements. If an element -is `NA`, then rownames are used.

      - -
      -
      -

      Value

      -

      SummarizedExperiment with an additional assay called - `combined`

      -
      -
      -

      Details

      -

      This combines matrices of different omics types together and -adds prefix tags to the ids.

      -
      - -
      -

      Examples

      - 
              library(leapR)
-        url <- 'https://api.figshare.com/v2/file/download/56536217'
-
-        pdata <- download.file(url,method='libcurl',destfile='protData.rda')
-        load('protData.rda')
-        p <- file.remove("protData.rda")
-
-        url <- "https://api.figshare.com/v2/file/download/56536214"
-        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
-        load('transData.rda')
-        p <- file.remove("transData.rda")
-
-        url <- 'https://api.figshare.com/v2/file/download/56536211'
-        phdata<-download.file(url,method='libcurl',destfile = 'phosData.rda')
-        #phosphodata<-read.csv("phdata",check.names=FALSE,row.names=1)
-        load('phosData.rda')
-        p <- file.remove('phosData.rda')# read in the example protein data
-
-
-        # merge the three datasets by rows and add prefix tags for
-        # different omics types
-        multi_omics <- combine_omics(list(pset, tset, phset),
-                    list(NA,NA,'hgnc_id'))
-
-
-
      -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/correlation_comparison_enrichment.html b/docs/reference/correlation_comparison_enrichment.html deleted file mode 100644 index a873852..0000000 --- a/docs/reference/correlation_comparison_enrichment.html +++ /dev/null @@ -1,115 +0,0 @@ - -correlation_comparison_enrichment — correlation_comparison_enrichment • leapR - Skip to contents - - -
      -
      -
      - -

      correlation_comparison_enrichment

      - -
      correlation_comparison_enrichment.Rd
      -
      - -
      -

      # internal function to calculate enrichment in differences in correlation -# between two groups -# access through the leapr wrapper

      -
      - -
      -

      Usage

      - 
      correlation_comparison_enrichment(
-  geneset,
-  eset,
-  assay_name,
-  set1,
-  set2,
-  mapping_column = NA
-)
      -
      - -
      -

      Arguments

      - - -
      geneset
      -

      pathway to use for enrichment

      - - -
      eset
      -

      SummarizedExperiment with abundance matrix

      - - -
      assay_name
      -

      name of assay

      - - -
      set1
      -

      first set to use

      - - -
      set2
      -

      second set to use

      - - -
      mapping_column
      -

      Column to use for id mapping within rowData

      - -
      -
      -

      Value

      -

      data frame with enrichment results

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/correlation_enrichment.html b/docs/reference/correlation_enrichment.html deleted file mode 100644 index 7d70347..0000000 --- a/docs/reference/correlation_enrichment.html +++ /dev/null @@ -1,97 +0,0 @@ - -correlation_enrichment — correlation_enrichment • leapR - Skip to contents - - -
      -
      -
      - -

      correlation_enrichment

      - -
      correlation_enrichment.Rd
      -
      - -
      -

      # calculate enrichment in correlation between pathway members -# access through leapr wrapper

      -
      - -
      -

      Usage

      - 
      correlation_enrichment(geneset, eset, assay_name, mapping_column = NA)
      -
      - -
      -

      Arguments

      - - -
      geneset
      -

      Geneset list

      - - -
      eset
      -

      a SummarizedExperiment object

      - - -
      assay_name
      -

      name of assay

      - - -
      mapping_column
      -

      Column to use to map identifiers, if not rownames

      - -
      -
      -

      Value

      -

      list of enrichment statistic table and correlation matrix

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/enrichment_in_abundance.html b/docs/reference/enrichment_in_abundance.html deleted file mode 100644 index f77a424..0000000 --- a/docs/reference/enrichment_in_abundance.html +++ /dev/null @@ -1,138 +0,0 @@ - -enrichment_in_abundance — enrichment_in_abundance • leapR - Skip to contents - - -
      -
      -
      - -

      enrichment_in_abundance

      - -
      enrichment_in_abundance.Rd
      -
      - -
      -

      Enrichment in abundance calculates enrichment in pathways by the difference -in abundance of the pathway members.

      -
      - -
      -

      Usage

      - 
      enrichment_in_abundance(
-  geneset,
-  eset,
-  assay_name,
-  mapping_column = NULL,
-  abundance_column = NULL,
-  fdr = 0,
-  matchset = NULL,
-  sample_comparison = NULL,
-  min_p_threshold = NULL,
-  sample_n = NULL,
-  silence_try_errors = TRUE
-)
      -
      - -
      -

      Arguments

      - - -
      geneset
      -

      Gene set to calculate enrichment

      - - -
      eset
      -

      Molecular abundance data in `SummarizedExperiment` format

      - - -
      assay_name
      -

      Name of assay to compare

      - - -
      mapping_column
      -

      Column to use to map identifiers

      - - -
      abundance_column
      -

      Columns to use to quantify abundance

      - - -
      fdr
      -

      number of times to sample for FDR value

      - - -
      matchset
      -

      Name of a set to use for enrichment

      - - -
      sample_comparison
      -

      list of samples to use as comparison. if missing -background (eset) is used

      - - -
      min_p_threshold
      -

      Only include p-values lower than this

      - - -
      sample_n
      -

      size of sample to use

      - - -
      silence_try_errors
      -

      set to true to silence try errors

      - -
      -
      -

      Value

      -

      data frame of enrichment result

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/enrichment_in_groups.html b/docs/reference/enrichment_in_groups.html deleted file mode 100644 index 1d7ccff..0000000 --- a/docs/reference/enrichment_in_groups.html +++ /dev/null @@ -1,139 +0,0 @@ - -enrichment_in_groups — enrichment_in_groups • leapR - Skip to contents - - -
      -
      -
      - -

      enrichment_in_groups

      - -
      enrichment_in_groups.Rd
      -
      - -
      -

      Calculate the enrichment in pathways using Fisher's exact or -Kolmogorov-Smirnov test, using either the abundance column to identify -feature or the targets list. access through leapr wrapper

      -
      - -
      -

      Usage

      - 
      enrichment_in_groups(
-  geneset,
-  targets = c(),
-  background = NULL,
-  assay_name = NULL,
-  method = "fishers",
-  minsize = 5,
-  mapping_column = NULL,
-  abundance_column = NULL,
-  randomize = FALSE,
-  silence_try_errors = TRUE
-)
      -
      - -
      -

      Arguments

      - - -
      geneset
      -

      geneset to use for enrichment

      - - -
      targets
      -

      targets to use for enrichment

      - - -
      background
      -

      `SummarizedExperiment` describing background to use

      - - -
      assay_name
      -

      is the name of the assay to use from the background

      - - -
      method
      -

      method to use for statistical test, options are -'fishers', 'ks', 'ztest', or 'chisq'. Remember that KS test assumes normality, so it would be good -to log your data before calling. NOTE: if you do not call `suppressWarnings` then -the KS test will warn you about ties.

      - - -
      minsize
      -

      minimum size of set

      - - -
      mapping_column
      -

      column name of mapping identifiers

      - - -
      abundance_column
      -

      columns mapping abundance, either in the `assay` -matrix or `rowData`

      - - -
      randomize
      -

      true/false whether to randomize

      - - -
      silence_try_errors
      -

      true/false to silence errors

      - -
      -
      -

      Value

      -

      data frame with enrichment results

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/enrichment_in_relationships.html b/docs/reference/enrichment_in_relationships.html deleted file mode 100644 index 72d95d7..0000000 --- a/docs/reference/enrichment_in_relationships.html +++ /dev/null @@ -1,109 +0,0 @@ - -enrichment_in_relationships — enrichment_in_relationships • leapR - Skip to contents - - -
      -
      -
      - -

      enrichment_in_relationships

      - -
      enrichment_in_relationships.Rd
      -
      - -
      -

      enrichment_in_relationships function description is a general way to -determine if a pathway -is enriched in relationships (interactions, correlation) between its members -# access through leapr wrapper

      -
      - -
      -

      Usage

      - 
      enrichment_in_relationships(
-  geneset,
-  relationships,
-  idmap = NA,
-  silence_try_errors = TRUE
-)
      -
      - -
      -

      Arguments

      - - -
      geneset
      -

      List of pathways in gmt format

      - - -
      relationships
      -

      table of relationship information, e.g. correlation

      - - -
      idmap
      -

      list of identifiers to use for mapping, the names of the items -should agree with names of features in matrix

      - - -
      silence_try_errors
      -

      boolean to silence errors

      - -
      -
      -

      Value

      -

      table of enrichment statistics

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/get_pathway_information.html b/docs/reference/get_pathway_information.html deleted file mode 100644 index 23fa80f..0000000 --- a/docs/reference/get_pathway_information.html +++ /dev/null @@ -1,106 +0,0 @@ - -get_pathway_information — get_pathway_information • leapR - Skip to contents - - -
      -
      -
      - -

      get_pathway_information

      - -
      get_pathway_information.Rd
      -
      - -
      -

      get_pathway_information extracts information about a pathway from a -GeneSet object

      -
      - -
      -

      Usage

      - 
      get_pathway_information(geneset, path, remove.tags = FALSE)
      -
      - -
      -

      Arguments

      - - -
      geneset
      -

      is a GeneSet object for pathway annotation

      - - -
      path
      -

      is the name of the gene set pathway to be return

      - - -
      remove.tags
      -

      boolean indicating whether to remove tags

      - -
      -
      -

      Value

      -

      list of pathway information

      -
      - -
      -

      Examples

      - 
      
-     library(leapR)
-
-     # load example gene set
-     data("ncipid")
-
-     tnfpathway = get_pathway_information(ncipid, "tnfpathway")
-
-
-
      -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100644 index 1578a2b..0000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,186 +0,0 @@ - -Package index • leapR - Skip to contents - - -
      -
      -
      - -

      Package index

      -
      - -
      -

      All functions

      - - - -
      - - - - -
      - - calcTTest() - -
      -
      calcTTest
      - -
      - - cluster_enrichment() - -
      -
      cluster_enrichment
      - -
      - - combine_omics() - -
      -
      combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.
      - -
      - - correlation_comparison_enrichment() - -
      -
      correlation_comparison_enrichment
      - -
      - - correlation_enrichment() - -
      -
      correlation_enrichment
      - -
      - - enrichment_in_abundance() - -
      -
      enrichment_in_abundance
      - -
      - - enrichment_in_groups() - -
      -
      enrichment_in_groups
      - -
      - - enrichment_in_relationships() - -
      -
      enrichment_in_relationships
      - -
      - - get_pathway_information() - -
      -
      get_pathway_information
      - -
      - - kinasesubstrates - -
      -
      Kinase substrate lists
      - -
      - - krbpaths - -
      -
      KEGG, Reactome, BioCarta Pathways
      - -
      - - leapR() - -
      -
      leapR
      - -
      - - longlist - -
      -
      Long list of patient samples
      - -
      - - ncipid - -
      -
      NCI Gene lists
      - -
      - - plot_leapr_bar() - -
      -
      Plot leapR pathway bars (single panel)
      - -
      - - read_gene_sets() - -
      -
      read_gene_sets
      - -
      - - shortlist - -
      -
      A list of pathways and genes that comprise these pathways from msigdb
      -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/kinasesubstrates.html b/docs/reference/kinasesubstrates.html deleted file mode 100644 index e88b2c8..0000000 --- a/docs/reference/kinasesubstrates.html +++ /dev/null @@ -1,90 +0,0 @@ - -Kinase substrate lists — kinasesubstrates • leapR - Skip to contents - - -
      -
      -
      - -

      Kinase substrate lists

      - -
      kinasesubstrates.Rd
      -
      - -
      -

      Kinase substrate lists

      -
      - -
      -

      Usage

      - 
      kinasesubstrates
      -
      - -
      -

      Format

      -

      A list with 4 items

      names
      -

      The names of the kinases

      - -
      desc
      -

      Short description of the kinase

      - -
      sizes
      -

      Length of the substrate list

      - -
      matrix
      -

      Substrate list for the kinase

      - - -
      -
      -

      Source

      -

      PhosphositePlus

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/krbpaths.html b/docs/reference/krbpaths.html deleted file mode 100644 index dab0ecd..0000000 --- a/docs/reference/krbpaths.html +++ /dev/null @@ -1,90 +0,0 @@ - -KEGG, Reactome, BioCarta Pathways — krbpaths • leapR - Skip to contents - - -
      -
      -
      - -

      KEGG, Reactome, BioCarta Pathways

      - -
      krbpaths.Rd
      -
      - -
      -

      KEGG, Reactome, BioCarta Pathways

      -
      - -
      -

      Usage

      - 
      krbpaths
      -
      - -
      -

      Format

      -

      A list with 4 items

      names
      -

      The names of the pathways

      - -
      desc
      -

      Short description of the pathways

      - -
      sizes
      -

      Number of genes in the signaling pathways

      - -
      matrix
      -

      Matrix containing the genes in the pathways

      - - -
      -
      -

      Source

      -

      https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/leapR-package.html b/docs/reference/leapR-package.html deleted file mode 100644 index 46b684b..0000000 --- a/docs/reference/leapR-package.html +++ /dev/null @@ -1,72 +0,0 @@ - -leapR: Layered enrichment analysis of pathways R — leapR-package • leapR - Skip to contents - - -
      -
      -
      - -

      leapR: Layered enrichment analysis of pathways R

      - -
      leapR-package.Rd
      -
      - -
      -

      leapR is a package that identifies pathways that are enriched across diverse 'omics experiments. It leverages any tabular expression data (proteomics, transcriptomics) using the `SummarizedExperiment` object. It works with any pathway in the .gct file format.

      -
      - - -
      -

      Author

      -

      Maintainer: Sara Gosline sara.gosline@pnnl.gov (ORCID)

      -

      Authors:

      Other contributors:

      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/leapR.html b/docs/reference/leapR.html deleted file mode 100644 index 250cc59..0000000 --- a/docs/reference/leapR.html +++ /dev/null @@ -1,317 +0,0 @@ - -leapR — leapR • leapR - Skip to contents - - -
      -
      -
      - -

      leapR

      - -
      leapR.Rd
      -
      - -
      -

      leapR is a wrapper function that consolidates multiple enrichment methods.

      -
      - -
      -

      Usage

      - 
      leapR(geneset, enrichment_method, eset, assay_name, ...)
      -
      - -
      -

      Arguments

      - - -
      geneset
      -

      is a list of four vectors, gene names, gene descriptions, gene -sizes and a matrix of genes. It represents .gmt format pathway files.

      - - -
      enrichment_method
      -

      is a character string specifying the method of -enrichment to be performed, one of: "enrichment_comparison", -"enrichment_in_order", "enrichment_in_sets", "enrichment_in_pathway", -"correlation_enrichment".

      - - -
      eset
      -

      is a `SummarizedExperiment` object containing expression data, -with features as rows and n sample/conditions as columns.

      - - -
      assay_name
      -

      is the assay to be analyzed within the `eset`. -Recommended to describe the data type (e.g. transcriptomics, proteomics) -so that it can be integrated in `combine_omics`

      - - -
      ...
      -

      further arguments

      - -
      -
      -

      Value

      -

      data frame with results

      -
      -
      -

      Details

      -

      Further arguments and enrichment method optional argument information:

      id_columnIs a character string, present in the rowData slot, -that is used to specify a column for identifiers to map to enrichment -libraries. -If missing, the rownames of the SummarizedExperiment assay will be used.
      primary_columnsIs a character vector composed of column names from -eset (either in the `assay` or in the `rowData`), -that specifies a set of primary columns to calculate enrichment on. -The meaning of this varies according to the enrichment method used - see -the descriptions for each method below. -This is an optional argument used with 'enrichment_in_order', -'enrichment_in_sets', and 'enrichment_comparison' methods.
      secondary_columns
      Is a character vector of column names for comparison, -pulled from the `assay` of the SummarizedExperiment. This is an -optional argument used with 'enrichment_comparison' methods.
      thresholdIs a numeric value, an optional argument used with -'enrichment_in sets' method which filters out abundance values or p-values -(depending on what `primary_columns` is used) -either above or below it.
      greaterthan
      Is a logical value that defaults to TRUE, it's used with -'enrichment_in_sets' method. -When set to TRUE, genes with `primary_columns` value above the -threshold argument are kept. -When set to FALSE genes with `primary_columns` value below the -threshold argument are kept. -This is an optional argument used with 'enrichment_in_sets' method.
      minsizeIs a numeric value, an optional argument used with -'enrichment_in_sets' and 'enrichment_in_order".
      fdr
      A numerical value which specifies how many times to randomly -sample genes to calculate an empirical false discovery rate, is an optional -argument used with 'enrichment_comparison' method.
      min_p_thresholdIs a numeric value, a lower p-value threshold and is an -optional argument used with 'enrichment_comparison' method.
      sample_n
      Is a way to subsample the number of components considered for -each calculation randomly. This is an optional argument used with -'enrichment_comparison' method.

      Enrichment Methods: -

      -enrichment_comparison -
      -Compares the distribution of abundances between two sets of -conditions for each pathway using a t test. For each pathway in -geneset uses a t test to compare the distribution of abundance -values/numbers in eset primary_columns with those in -eset secondary_columns. Lower p-values for pathways indicate -that the expression of the pathway is significantly different between the -set of conditions in primary_columns and the set of conditions in -secondary_columns. -Optionally, users can specify fdr which will calculate an empirical -p-value by randomizing abundances fdr number of times. If the -min_p_threshold is specified the method will only return pathways -with an adjusted p-value lower than the specified threshold. If -sample_n is specified the method will subsample the -pathway members to the specified number of components. -

      -enrichment_in_order -
      -Calculates enrichment of pathways based on a ranked list using the -Kolmogorov-Smirnov test. For each pathway in geneset uses a -Kolmogorov-Smirnov test for rank order to test if the distribution of ranked -abundance values in the eset primary_columns is significant -relative to a random distribution. Note that currently -primary_columns only accepts a single column for this method. -

      -enrichment_in_sets -
      -Calculates enrichment in pathway membership in a list (e.g. highly -differential proteins) relative to background using Fisher's exact test. For -each pathway in geneset uses a Fisher's exact test over- or under- -representation of a list of components specified. If targets are -specified this must be a vector of identifiers to serve as the target list -for comparison. If eset and primary_columns are specified then -threshold specifies a threshold value for determining the target list -of components to test. Specifying greaterthan to be False -will result in components with values lower than the specified -threshold. If eset is a data frame or matrix, the background -used for calculation will be taken as the rownames of eset -

      -enrichment_in_pathway -
      -Compares the distribution of abundances in a pathway with the background -distribution of abundances using a t test. For each pathway in -geneset calculates the significance of the difference between the -abundances from pathway members versus abundance of non-pathway members in -the set of conditions specified by primary_columns. Optionally, users -can specify fdr which will calculate an empirical p-value by -randomizing abundances fdr number of times. If the -min_p_threshold is specified the method will only return pathways -with an adjusted p-value lower than the specified threshold. If -sample_n is specified the method will subsample the -pathway members to the specified number of components. -

      -correlation_enrichment -
      -Calculates the enrichment of a pathway based on correlation between pathway -members across conditions versus correlation between members not in the -pathway. For each pathway in geneset calculates the pairwise -correlation between all pathway members and non-pathway members -across the specified primary_columns conditions in eset. Note -that for large matrices this can take a long time. A p-value is calculated -based on comparing the correlation within the members of a pathway with the -correlation values between members of the pathway and non-members of the -pathway. -

      -
      - -
      -

      Examples

      - 
              library(leapR)
-
- # read in the example abundance data
- # read in the example transcriptomic data
- tdata <- download.file("https://api.figshare.com/v2/file/download/56536214",
-      method='libcurl',destfile='transData.rda')
- load('transData.rda')
- p <- file.remove("transData.rda")
-
- # read in the pathways
- data("ncipid")
-
- # read in the patient groups
- data("shortlist")
- data("longlist")
-
- # use enrichment_comparison to calculate enrichment in one set of
- # conditions (shortlist) and another (longlist)
- short_v_long = leapR(geneset=ncipid, assay_name='transcriptomics',
-              enrichment_method='enrichment_comparison',
-              eset=tset, primary_columns=shortlist,
-               secondary_columns=longlist)
-
- # use enrichment_in_sets to calculate the most enriched pathways
- # from the highest abundance proteins
- #     from one condition
- onept_sets = leapR(geneset=ncipid, assay_name='transcriptomics',
-               enrichment_method='enrichment_in_sets',
-               eset=tset, primary_columns="TCGA-13-1484", threshold=1.5)
-
- # use enrichment_in_order to calculate the most enriched pathways from the
- # same condition
- # Note: that this uses the entire set of abundance values and their order -
- # whereas the previous example uses a hard threshold to get a short list of
- # most abundant proteins and calculates enrichment based on set overlap.
- # The results are likely to be similar - but with some notable differences.
- onept_order = leapR(geneset=ncipid, assay_name='transcriptomics',
-               enrichment_method='enrichment_in_order',
-               eset=tset, primary_columns="TCGA-13-1484")
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-#> Warning: p-value will be approximate in the presence of ties
-
- # use enrichment_in_pathway to calculate the most enriched pathways in a
- # set of conditions based on abundance in the pathway members versus
- # abundance in non-pathway members
- short_pathways = leapR(geneset=ncipid, assay_name='transcriptomics',
-               enrichment_method='enrichment_in_pathway',
-               eset=tset, primary_columns=shortlist)
-
- # use correlation_enrichment to calculate the most enriched pathways in
- # correlation across the shortlist conditions
- short_correlation_pathways = leapR(geneset=ncipid,
-                assay_name='transcriptomics',
-                enrichment_method='correlation_enrichment',
-                eset=tset, primary_columns=shortlist)
-
-
-
      -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/longlist.html b/docs/reference/longlist.html deleted file mode 100644 index a6ec82d..0000000 --- a/docs/reference/longlist.html +++ /dev/null @@ -1,74 +0,0 @@ - -Long list of patient samples — longlist • leapR - Skip to contents - - -
      -
      -
      - -

      Long list of patient samples

      - -
      longlist.Rd
      -
      - -
      -

      Long list of patient samples

      -
      - -
      -

      Usage

      - 
      longlist
      -
      - -
      -

      Format

      -

      An object of class character of length 37.

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/ncipid.html b/docs/reference/ncipid.html deleted file mode 100644 index fceb6c8..0000000 --- a/docs/reference/ncipid.html +++ /dev/null @@ -1,90 +0,0 @@ - -NCI Gene lists — ncipid • leapR - Skip to contents - - -
      -
      -
      - -

      NCI Gene lists

      - -
      ncipid.Rd
      -
      - -
      -

      A list of pathways and the genes that comprise these pathways

      -
      - -
      -

      Usage

      - 
      ncipid
      -
      - -
      -

      Format

      -

      A list with 4 items

      names
      -

      The names of the signaling pathways

      - -
      desc
      -

      Short description of the pathways

      - -
      sizes
      -

      Number of genes in the signaling pathways

      - -
      matrix
      -

      Matrix containing the genes in the pathways

      - - -
      -
      -

      Source

      -

      NCIPID

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/plot_leapr_bar.html b/docs/reference/plot_leapr_bar.html deleted file mode 100644 index e1688a5..0000000 --- a/docs/reference/plot_leapr_bar.html +++ /dev/null @@ -1,137 +0,0 @@ - -Plot leapR pathway bars (single panel) — plot_leapr_bar • leapR - Skip to contents - - -
      -
      -
      - -

      Plot leapR pathway bars (single panel)

      - -
      plot_leapr_bar.Rd
      -
      - -
      -

      This plotting helper expects leapR generated results to plot. -It will use BH_pvalue if present, otherwise pvalue.

      -
      - -
      -

      Usage

      - 
      plot_leapr_bar(
-  res_df,
-  title = NULL,
-  top_n = 15,
-  star_thresholds = c(0.05, 0.01, 0.001),
-  wrap = 42,
-  max_stars = 5L,
-  fill_sig = "#2C7BB6",
-  fill_ns = "#BFD7FF",
-  outline = NA,
-  axis_text_y_size = 8,
-  axis_text_x_size = 9
-)
      -
      - -
      -

      Arguments

      - - -
      res_df
      -

      A leapR df containing BH_pvalue (or pvalue) and a pathway/term label column.

      - - -
      title
      -

      Plot title.

      - - -
      top_n
      -

      Number of top pathways/genes to display.

      - - -
      star_thresholds
      -

      list of numeric significance thresholds for star annotations.

      - - -
      wrap
      -

      Wrap width for pathway labels (helps formatting).

      - - -
      max_stars
      -

      Maximum number of stars to draw per bar (default 5).

      - - -
      fill_sig
      -

      Fill color for significant bars

      - - -
      fill_ns
      -

      Fill color for non-significant bars.

      - - -
      outline
      -

      Bar border color.

      - - -
      axis_text_y_size
      -

      Font size for y-axis (category) labels.

      - - -
      axis_text_x_size
      -

      Font size for x-axis (numeric) labels.

      - -
      -
      -

      Value

      -

      A ggplot2 object (or NULL if nothing to plot).

      -
      - -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/read_gene_sets.html b/docs/reference/read_gene_sets.html deleted file mode 100644 index b15d70c..0000000 --- a/docs/reference/read_gene_sets.html +++ /dev/null @@ -1,112 +0,0 @@ - -read_gene_sets — read_gene_sets • leapR - Skip to contents - - -
      -
      -
      - -

      read_gene_sets

      - -
      read_gene_sets.Rd
      -
      - -
      -

      read_gene_sets is a function to import external pathway -database files in .gmt format

      -
      - -
      -

      Usage

      - 
      read_gene_sets(
-  gsfile,
-  gene.labels = NA,
-  gs.size.threshold.min = 5,
-  gs.size.threshold.max = 15000
-)
      -
      - -
      -

      Arguments

      - - -
      gsfile
      -

      is a gene set file, for example a .gmt file -(gene matrix transposed file format)

      - - -
      gene.labels
      -

      defaults to NA

      - - -
      gs.size.threshold.min
      -

      defaults to 5

      - - -
      gs.size.threshold.max
      -

      defaults to 15000

      - -
      -
      -

      Value

      -

      gene set object

      -

      geneset list

      -
      - -
      -

      Examples

      - 
      gfile <- system.file('extdata','h.all.v2024.1.Hs.symbols.gmt',
-          package='leapR')
-glist <- read_gene_sets(gfile)
-
-
      -
      -
      - - -
      -

      Developed by Sara Gosline, Jason McDermott, Jeremy Jacobson, National Institutes of Health.

      -
      - -
      -

      Site built with pkgdown 2.2.0.

      -
      - -
      - - - - - - - diff --git a/docs/reference/shortlist.html b/docs/reference/shortlist.html deleted file mode 100644 index aa77ef6..0000000 --- a/docs/reference/shortlist.html +++ /dev/null @@ -1,75 +0,0 @@ - -A list of pathways and genes that comprise these pathways from msigdb — shortlist • leapR - Skip to contents - - -
      -
      -
      - -

      A list of pathways and genes that comprise these pathways from msigdb

      - -
      shortlist.Rd
      -
      - -
      -

      A list of pathways and genes that comprise these pathways from msigdb

      -
      - -
      -

      Usage

      - 
      shortlist
      -
      - -
      -

      Format

      -

      a list with 4 items

      -

      Short list of patient samples

      -
      - -
      - - -
      - - - - - - -