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-leapR Paper Examples
- - - -examples.Rmd
-
-
-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
- -
-
-
-
-
- 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"
-)
