Adding 5 individual figure scripts by CamiloPosso · Pull Request #44 · PNNL-CompBio/amlMultidrugResistance

CamiloPosso · 2026-05-04T18:10:33Z

Figure 0 - Introductory panels for the two datasets, clinical + exvivo
Figure 1 - Applying existing signatures in clinical dataset
Figure 2 - Deriving exvivo signature of resistance and applying to clinical data
Figure 3 - Deriving clinical signature of resistance and applying to exvivo data
Figure 4 - Markers from Cox Hazard with survival time and also with remission time
Figure 5 - Applying signatures to BeatAML and correlating with drug AUC.
scripts - Necessary helper scripts, loading data, extracting signatures, applying them, etc.

sgosline · 2026-05-04T22:14:12Z

+library(ggpubr)
+library(patchwork)
+
+syn <- synapseLogin()


@CamiloPosso where is this function defined? My running fails on this

CamiloPosso · 2026-05-05T18:24:18Z

Just added source(../../synapseUtil.R) to each script, so now synapseLogin should work when running loading_data.R

sgosline

i had some issues. I'm wondering if it's my setup so i'm including my sessionInfo() below:

R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.4.1

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] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] survminer_0.5.2     survival_3.8-6      GSVA_2.4.8          msigdbr_26.1.0      glmnet_4.1-10      
 [6] Matrix_1.7-4        data.table_1.18.2.1 patchwork_1.3.2     ggpubr_0.6.3        ggplot2_4.0.2      
[11] MSnSet.utils_0.2.0  MSnbase_2.36.0      ProtGenerics_1.42.0 S4Vectors_0.48.0    mzR_2.44.0         
[16] Rcpp_1.1.1          Biobase_2.70.0      BiocGenerics_0.56.0 generics_0.1.4      dplyr_1.2.0        
[21] reticulate_1.45.0  

loaded via a namespace (and not attached):
  [1] fs_1.6.7                    matrixStats_1.5.0           bitops_1.0-9                httr_1.4.8                 
  [5] RColorBrewer_1.1-3          doParallel_1.0.17           dynamicTreeCut_1.63-1       tools_4.5.2                
  [9] backports_1.5.0             R6_2.6.1                    HDF5Array_1.38.0            lazyeval_0.2.2             
 [13] mgcv_1.9-4                  rhdf5filters_1.22.0         GetoptLong_1.1.0            withr_3.0.2                
 [17] gridExtra_2.3               preprocessCore_1.72.0       WGCNA_1.74                  cli_3.6.5                  
 [21] labeling_0.4.3              S7_0.2.1                    randomForest_4.7-1.2        genefilter_1.92.0          
 [25] askpass_1.2.1               yulab.utils_0.2.4           foreign_0.8-91              DOSE_4.4.0                 
 [29] R.utils_2.13.0              MetaboCoreUtils_1.18.1      invgamma_1.2                limma_3.66.0               
 [33] rstudioapi_0.18.0           impute_1.84.0               RSQLite_2.4.6               shape_1.4.6.1              
 [37] gtools_3.9.5                car_3.1-5                   zip_2.3.3                   GO.db_3.22.0               
 [41] MALDIquant_1.22.3           abind_1.4-8                 R.methodsS3_1.8.2           lifecycle_1.0.5            
 [45] yaml_2.3.12                 edgeR_4.8.2                 carData_3.0-6               SummarizedExperiment_1.40.0
 [49] rhdf5_2.54.1                gplots_3.3.0                qvalue_2.42.0               SparseArray_1.10.9         
 [53] BiocFileCache_3.0.0         grid_4.5.2                  blob_1.3.0                  crayon_1.5.3               
 [57] PSMatch_1.14.0              lattice_0.22-9              beachmat_2.26.0             cowplot_1.2.0              
 [61] annotate_1.88.0             KEGGREST_1.50.0             magick_2.9.1                pillar_1.11.1              
 [65] knitr_1.51                  ComplexHeatmap_2.26.1       fgsea_1.36.2                GenomicRanges_1.62.1       
 [69] rjson_0.2.23                boot_1.3-32                 codetools_0.2-20            fastmatch_1.1-8            
 [73] glue_1.8.0                  outliers_0.15               memuse_4.2-3                pcaMethods_2.2.0           
 [77] MultiAssayExperiment_1.36.1 vctrs_0.7.1                 png_0.1-8                   Rdpack_2.6.6               
 [81] gtable_0.3.6                assertthat_0.2.1            cachem_1.1.0                xfun_0.56                  
 [85] openxlsx_4.2.8.1            rbibutils_2.4.1             S4Arrays_1.10.1             Seqinfo_1.0.0              
 [89] reformulas_0.4.4            ncdf4_1.24                  SingleCellExperiment_1.32.0 iterators_1.0.14           
 [93] statmod_1.5.1               ROCR_1.0-12                 nlme_3.1-168                bit64_4.6.0-1              
 [97] filelock_1.0.3              R.cache_0.17.0              irlba_2.3.7                 affyio_1.80.0              
[101] KernSmooth_2.23-26          otel_0.2.0                  rpart_4.1.24                colorspace_2.1-2           
[105] DBI_1.3.0                   Hmisc_5.2-5                 nnet_7.3-20                 tidyselect_1.2.1           
[109] bit_4.6.0                   compiler_4.5.2              curl_7.0.0                  graph_1.88.1               
[113] httr2_1.2.2                 htmlTable_2.4.3             h5mread_1.2.1               DelayedArray_0.36.0        
[117] checkmate_2.3.4             scales_1.4.0                caTools_1.18.3              affy_1.88.0                
[121] varSelRF_0.7-9              rappdirs_0.3.4              SpatialExperiment_1.20.0    stringr_1.6.0              
[125] MSnID_1.44.2                digest_0.6.39               minqa_1.2.8                 rmarkdown_2.30             
[129] XVector_0.50.0              htmltools_0.5.9             pkgconfig_2.0.3             base64enc_0.1-6            
[133] lme4_2.0-1                  umap_0.2.10.0               sparseMatrixStats_1.22.0    MatrixGenerics_1.22.0      
[137] dbplyr_2.5.2                fastmap_1.2.0               rlang_1.1.7                 GlobalOptions_0.1.3        
[141] htmlwidgets_1.6.4           farver_2.1.2                jsonlite_2.0.0              BiocParallel_1.44.0        
[145] mzID_1.48.0                 GOSemSim_2.36.0             R.oo_1.27.1                 BiocSingular_1.26.0        
[149] magrittr_2.0.4              Formula_1.2-5               Rhdf5lib_1.32.0             babelgene_22.9             
[153] MsCoreUtils_1.22.1          vsn_3.78.1                  stringi_1.8.7               pROC_1.19.0.1              
[157] MASS_7.3-65                 AnnotationHub_4.0.0         plyr_1.8.9                  FField_0.1.0               
[161] parallel_4.5.2              ggrepel_0.9.7               Biostrings_2.78.0           splines_4.5.2              
[165] Spectra_1.20.1              circlize_0.4.17             locfit_1.5-9.12             Boruta_9.0.0               
[169] igraph_2.2.2                fastcluster_1.3.0           QFeatures_1.20.0            ggsignif_0.6.4             
[173] pkgload_1.5.0               ScaledMatrix_1.18.0         reshape2_1.4.5              Heatplus_3.18.0            
[177] BiocVersion_3.22.0          XML_3.99-0.22               evaluate_1.0.5              BiocManager_1.30.27        
[181] nloptr_2.2.1                foreach_1.5.2               tidyr_1.3.2                 openssl_2.3.5              
[185] purrr_1.2.1                 clue_0.3-67                 BiocBaseUtils_1.12.0        rsvd_1.0.5                 
[189] broom_1.0.12                xtable_1.8-8                AnnotationFilter_1.34.0     RSpectra_0.16-2            
[193] rstatix_0.7.3               tibble_3.3.1                memoise_2.0.1               AnnotationDbi_1.72.0       
[197] IRanges_2.44.0              cluster_2.1.8.2             sva_3.58.0                  GSEABase_1.72.0

sgosline · 2026-05-05T19:57:03Z

+df.u$subcohort = pData(m_exp28)[, "subcohort"]
+df.v = xx$data.v
+
+p <- ggplot(data = df.u, mapping = aes(x = df.u[, 1], y = df.u[, 2], 


This fails for me - it feels like df.u is trying to subset a plot object, since xx is actually a plot?

sgosline · 2026-05-05T20:03:45Z

   left_join(., p_data, by = "sample_name") %>% filter(sample_name %in% sampleNames(m_exp28))

 plots = list()
 for (cell_type in unique(maturation_df$X)){


Since you are doing very similar plots, it might be useful to make a function to compute protein scores and weights across the cohort. A For loop is fine, but if you end up using it later, a function outside would be optimal.

sgosline · 2026-05-05T20:05:29Z

+yy = plot_pca(m_, phenotype = "Treatment", output_type = "full")
+df.u = yy$data.u
+df.u$Treatment = pData(m_)[, "Treatment"]
+df.u = df.u %>%


df.u is not defined here, I"m guessing this is from Figure 0 code. However, it fails for the same reason - yy is a graphics object, not a PCA object.

sgosline · 2026-05-05T20:13:22Z

+```{r}
+
+
+diffexp = diffexp_helper(m_exp28_[complete_features, ], contrast_var = "refractory", 


m_exp28_ is not defined. I changed this to m_exp28 which is defined above, but then it can't find the refractory column of the pData.

sgosline · 2026-05-05T20:20:35Z

+   return(res_)
+}
+
+hazards_res = hazards_(msnset[features_50perc, ])


This line fails, I think it's the subsetting of msnset:

> msnset[features_50perc, ] Error in orig[[nm]][i, , ..., drop = drop] : subscript out of bounds

sgosline · 2026-05-05T20:22:12Z

+source("../scripts/predict_helper.R")
+source("../scripts/s2n_model.R")
+
+m_exp28 = readRDS("msnset_exp28_global.RDS")


cannot find this file - maybe download from synapse?

sgosline · 2026-05-05T20:22:28Z

+
+complete_features = featureNames(m_exp28)[rowSums(is.na(exprs(m_exp28))) == 0]
+m_210$new = paste(m_210$InitialAMLDiagnosis, m_210$PostChemotherapy)
+beataml_clinical = openxlsx::read.xlsx("../../PNNL_clinical_summary_12_08_2021_updated_OS_4patients_02_28_2022.xlsx")


same issue with this file

CamiloPosso · 2026-05-06T17:55:58Z

I don't think it was on your end, just some minor typos/bugs and a function (modification of plot_pca) I forgot to include in the commits. All the scripts should be working now (famous last words).

sgosline

getting farther!

sgosline · 2026-05-07T16:59:07Z

+PCA of SSGSEA
+
+```{r}
+treatment_colors = c("DV" = "#00798c", "UT" = "forestgreen")


I think it might be helpful early on to agree on nomenclature for this sample, perhaps calling then 'Untreated' and 'Ven + HMA treated' throughout would be easier.

sgosline · 2026-05-07T16:59:38Z

+Diffexp of SSGSEA
+
+```{r}
+diffexp_results = diffexp_helper(m_exp28, "subcohort", c("Refractory-Response_no_relapse", "Refractory-Relapse")) 


Same naming conventions go here - "Refractory", "Short response", "Long response" I think is what we agreed on.

sgosline · 2026-05-07T17:11:02Z

+Showing the signature in the exvivo data (training data)
+
+```{r}
+# model <- logistic_model(m_4pat[complete_DV_UT_features_, ], "Treatment", skip_loo = TRUE,


I had to uncomment this to run the next line.

sgosline · 2026-05-07T17:18:54Z

+
+
+```{r}
+


i'd try to comment each block to describe what you're doing, and also prose/plain text above.

sgosline · 2026-05-07T19:03:31Z

 p

 model_features = results_ttest %>% filter(pval_cutoff == 0.001) %>% pull(feature) %>% unique()
 plot_pca(m_exp28[model_features, ], phenotype = "subcohort") + ggtitle("PCA of 21 model features") +


This is not working here.

sgosline · 2026-05-07T20:46:01Z

+# feat2 = all_features_df_      # using the ven+hma model
+common = intersect(feat1$feature, feat2$feature)
+
+feat1 = left_join(feat1, diffexp_results_venhma %>% select(logFC, feature, t_test_pval), by = "feature")


this fails because feat1 is not defined. if i define it above, then it failsb ecause the diffexp_results_venhma is not defined.

sgosline · 2026-05-07T20:48:10Z

+complete_features = featureNames(m_exp28)[rowSums(is.na(exprs(m_exp28))) == 0]
+m_210$new = paste(m_210$InitialAMLDiagnosis, m_210$PostChemotherapy)
+beataml_clinical = openxlsx::read.xlsx(syn$get("syn74822940")$path)
+```


is the above section in every markdown? if so would it be helpful to have a 'load data' script?

sgosline · 2026-05-07T20:49:13Z

+refractory_markers = model$full_model$features %>% filter(group_ == "DV") %>% pull(feature)
+longresponse_markers = model$full_model$features %>% filter(group_ != "DV") %>% pull(feature)
+
+xx = predict_helper_(m_md_anderson, refractory_markers, longresponse_markers) %>%


is m_md_anderson defined? this fails for me

This script uses only the data from synapse, and generate all the panels for the exvivo DecVen and Ven signatures. It runs LOOCV to find the number of markers for each signature, and then applies the signature in the invivo data, showing boxplots of the results.

Added the stats table to synapse

CamiloPosso added 5 commits May 4, 2026 11:04

Trying to add necessary scripts

1d91015

Trying to add necessary scripts

a1333b3

Update s2n_model.R

24d979c

Update s2n_model.R

06da2b5

Update s2n_model.R

77e89a6

sgosline reviewed May 4, 2026

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sgosline requested changes May 4, 2026

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Added synapse fix

e33c8f1

CamiloPosso requested a review from sgosline May 5, 2026 18:24

sgosline requested changes May 5, 2026

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CamiloPosso added 2 commits May 6, 2026 10:41

Fixing bugs

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Fixing bugs

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CamiloPosso requested a review from sgosline May 7, 2026 13:31

sgosline requested changes May 7, 2026

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CamiloPosso added 2 commits August 10, 2026 07:37

Update exvivo_signature.Rmd

c247c40

Added the stats table to synapse

		```{r}


		diffexp = diffexp_helper(m_exp28_[complete_features, ], contrast_var = "refractory",

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CamiloPosso commented May 6, 2026

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