Pre-processing EMBL2016 drug screen datasets
Junyan Lu
2021-10-05
Last updated: 2021-12-24
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Knit directory: DepInfeR/analysis/
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Introduction
This document shows the preprocessing of the EMBL2016 screening dataset to use with the target importance inference package (DepInfeR) with the kinobeads kinase inhibitor screen (Klaeger, 2017).
Load packages
Packages
library(depInfeR)
library(stringdist)
library(BloodCancerMultiOmics2017)
library(DESeq2)
library(igraph)
library(tidyverse)
source("../code/utils.R")
knitr::opts_chunk$set(dev = c("png","pdf"))Read data sets
Load pre-processed kinobead table table
tarList <- readRDS("../output/allTargets.rds")Read in EMBL2016 raw drug screen datasets
EMBLscreen <- readxl::read_xlsx("../data/EMBL2016/EMBL2016_screen.xlsx")
#sample annotation
patMeta <- readxl::read_xlsx("../data/EMBL2016/EMBL2016_patAnnotation.xlsx")Preprocess datasets
Find overlapping drugs between drug screen data and drug-target dataset
Get drug list from EMBL2016 screen
drugList <- EMBLscreen %>% dplyr::select(drugID, name, Synonyms) %>%
filter(!is.na(drugID), !duplicated(drugID)) %>% mutate(Drug = tolower(name)) %>%
mutate(Drug = gsub("[- ]","",Drug)) Find overlapped drugs by their names
overDrug <- intersect(tarList$Drug, drugList$Drug)Drugs that are not overlapped.
missDrug <- setdiff(drugList$Drug, tarList$Drug)Calculate hamming distance and consider synonyms
notFound <- setdiff(unique(tarList$Drug),overDrug)
stillNotFound <- filter(drugList, Drug %in% missDrug)
distTab <- lapply(seq(nrow(stillNotFound)), function(i) {
drug1 <- stillNotFound[i,]$Drug
synList <- strsplit(stillNotFound[i,]$Synonyms, split = ",")[[1]]
lapply(synList, function(syn) {
lapply(notFound, function(drug2) {
data.frame(drug1 = drug1, synonym = tolower(syn), drug2= drug2, dis = stringdist(tolower(syn), drug2), stringsAsFactors = FALSE)
}) %>% dplyr::bind_rows()
}) %>% dplyr::bind_rows()
}) %>% dplyr::bind_rows()
distTab <- arrange(distTab, dis)
head(distTab, n=10) drug1 synonym drug2 dis
1 roscovitine seliciclib seliciclib 0
2 flavopiridol alvocidib alvocidib 0
3 nvpaew541 aew541 aew541 0
4 azd9291 osimertinib osimertinib 0
5 afuresertib gsk2110183 gsk2110183 0
6 sns032 bms-387032 bms387032 1
7 mk8776 sch 900776 sch900776 1
8 bi6727 volasertib volasertib 1
9 roscovitine seliciclib milciclib 3
10 azd9291 osimertinib ulixertinib 3The first 8 drugs are the same drugs
Get drug mappings
drugMap <- distTab[1:8,]$drug1
names(drugMap) <- distTab[1:8,]$drug2Modify the name
tarList <- mutate(tarList, Drug = ifelse(Drug %in% names(drugMap), drugMap[Drug],Drug))Get the final overlapped drug list
finalList <- intersect(tarList$Drug, drugList$Drug)Combine the lists and match drug IDs
targets <- left_join(tarList, drugList, by = "Drug") %>%
dplyr::select(name, drugID, `Target Classification`, EC50,`Apparent Kd`, `Gene Name`) %>%
dplyr::filter(!is.na(name))How many drugs?
length(unique(targets$drugID))[1] 86Change names
colnames(targets) <- c("drugName","drugID","targetClassification","EC50","Kd","targetName","originalTarget","originalPathway")Remove targets that are not expressed in patient samples at all
Based on published RNAseq dataset
data("dds")
dds <- dds[,dds$PatID %in% EMBLscreen$patID]
colnames(dds) <- dds$PatIDGet count values from RNAseq data
#targets that are not in RNAseq dataset
#setdiff(unique(targets$targetName), rowData(dds)$symbol)
#actually four genes have different gene names used.
symbolMap <- c(ADCK3 ="COQ8A", ZAK = "MAP3K20",
KIAA0195 = "TMEM94", ADRBK1 = "GRK2")
#correct the name
targets <- mutate(targets, targetName = ifelse(targetName %in% names(symbolMap),
symbolMap[targetName],
targetName))
highTargets <- filter(targets, targetClassification == "High confidence")
#get count data
targetCount <- dds[rowData(dds)$symbol %in% targets$targetName,]Plot the expression values
#prepare plot tab
plotTab <- data.frame(counts(targetCount, normalized = FALSE)) %>%
rownames_to_column("ID") %>%
mutate(symbol = rowData(targetCount)$symbol) %>%
gather(key = "patID", value = "counts", -symbol, -ID)
#deal with one gene, multiple transcript problem
#only keep the most aboundant transcript
transTab <- group_by(plotTab, ID, symbol) %>% summarize(total = sum(counts)) %>%
ungroup() %>%
arrange(desc(total)) %>% distinct(symbol, .keep_all = TRUE)`summarise()` has grouped output by 'ID'. You can override using the `.groups` argument.plotTab <- filter(plotTab, ID %in% transTab$ID)
#get the 80% quantile expression value
exprMed <- group_by(plotTab, symbol) %>% summarise(avgCount = quantile(counts,0.8)) %>%
arrange(avgCount) %>% top_n(-50, avgCount)
#only plot the 50 lowest expressed genes
plotTab <- filter(plotTab, symbol %in% exprMed$symbol) %>%
mutate(symbol = factor(symbol, levels = exprMed$symbol))
ggplot(plotTab, aes(x= symbol, y = counts)) + geom_boxplot() +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust =1, vjust =.5)) 
Removed the targets that are not expressed
#80% quantile < 10
geneRemove <- filter(exprMed, rank(avgCount) / n() < 0.8)
geneRemove <- filter(exprMed,avgCount < 10)$symbol
targets <- filter(targets, !targetName %in% geneRemove)Turn target table into drug-target affinity matrix
tarMat_kd <- dplyr::filter(targets, targetClassification == "High confidence") %>%
dplyr::select(drugID, targetName, Kd) %>%
spread(key = "targetName", value = "Kd") %>%
remove_rownames() %>% column_to_rownames("drugID") %>% as.matrix()Plot target groups
#plot network
#Only plot for finnally selected targets
load("../output/EMBL_result.RData")
CancerxTargets<- rowSums(result$freqMat)
CancerxTargets <- names(CancerxTargets[CancerxTargets>0])
plotTarGroups(ProcessTargetResults, CancerxTargets)
Visualization of the whole drug-target network
plotTab <- dplyr::select(targets, drugName, targetName)
nodeAttr <- gather(plotTab, key = "type", value = "name", drugName, targetName) %>%
filter(!duplicated(name)) %>%
mutate(type = ifelse(type == "targetName", "target", "drug"))
g <- graph_from_edgelist(as.matrix(plotTab))
V(g)$nodeType <- nodeAttr[match(V(g)$name, nodeAttr$name),]$type
V(g)$shape <- ifelse(V(g)$nodeType == "drug", "circle","square")
V(g)$color <- ifelse(V(g)$nodeType == "drug", "skyblue","pink")
V(g)$size = 6
V(g)$label.cex = 0.7
plot(g, layout=layout_with_kk)
No obvious structure can be seen. Polypharmacology needs to be resolved.
Preparation of drug response matrix
Prepare response matrix using the z-score
In order to be consistent for all drugs, only the 9 lowest concentrations are regarded.
Use average of 9 concentrations
viabTab <- dplyr::filter(EMBLscreen,
concIndex %in% seq(1,9)) %>%
group_by(drugID, patID) %>%
summarise(viab = mean(normVal.sigm)) %>% ungroup() %>%
dplyr::rename(Drug = drugID, patientID = patID)`summarise()` has grouped output by 'drugID'. You can override using the `.groups` argument.viabMat <- spread(viabTab, patientID, viab) %>%
data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()Save pre-processed dataset
targetsEMBL <- targets
ProcessTargetResults_EMBL <- ProcessTargetResults
tarMat_EMBL <- ProcessTargetResults$targetMatrix
viabMat_EMBL <- viabMat[rownames(tarMat_EMBL),]
annotation_EMBL <- patMeta
save(tarMat_EMBL, viabMat_EMBL, annotation_EMBL, ProcessTargetResults_EMBL, targetsEMBL, file = "../output/inputs_EMBL.RData")
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0
[3] dplyr_1.0.7 purrr_0.3.4
[5] readr_2.1.1 tidyr_1.1.4
[7] tibble_3.1.6 ggplot2_3.3.5
[9] tidyverse_1.3.1 igraph_1.2.10
[11] DESeq2_1.34.0 SummarizedExperiment_1.24.0
[13] Biobase_2.54.0 MatrixGenerics_1.6.0
[15] matrixStats_0.61.0 GenomicRanges_1.46.1
[17] GenomeInfoDb_1.30.0 IRanges_2.28.0
[19] S4Vectors_0.32.3 BiocGenerics_0.40.0
[21] BloodCancerMultiOmics2017_1.14.0 stringdist_0.9.8
[23] depInfeR_0.1.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 tidyselect_1.1.1 RSQLite_2.2.9
[4] AnnotationDbi_1.56.2 htmlwidgets_1.5.4 grid_4.1.2
[7] BiocParallel_1.28.3 devtools_2.4.3 munsell_0.5.0
[10] codetools_0.2-18 withr_2.4.3 colorspace_2.0-2
[13] highr_0.9 knitr_1.36 rstudioapi_0.13
[16] labeling_0.4.2 git2r_0.29.0 GenomeInfoDbData_1.2.7
[19] mnormt_2.0.2 bit64_4.0.5 farver_2.1.0
[22] rprojroot_2.0.2 vctrs_0.3.8 generics_0.1.1
[25] xfun_0.29 rlist_0.4.6.2 R6_2.5.1
[28] doParallel_1.0.16 locfit_1.5-9.4 bitops_1.0-7
[31] cachem_1.0.6 DelayedArray_0.20.0 assertthat_0.2.1
[34] promises_1.2.0.1 scales_1.1.1 nnet_7.3-16
[37] beeswarm_0.4.0 gtable_0.3.0 processx_3.5.2
[40] workflowr_1.7.0 rlang_0.4.12 genefilter_1.76.0
[43] splines_4.1.2 broom_0.7.10 checkmate_2.0.0
[46] yaml_2.2.1 reshape2_1.4.4 abind_1.4-5
[49] modelr_0.1.8 backports_1.4.1 httpuv_1.6.4
[52] Hmisc_4.6-0 tools_4.1.2 usethis_2.1.5
[55] psych_2.1.9 lavaan_0.6-9 ellipsis_0.3.2
[58] jquerylib_0.1.4 RColorBrewer_1.1-2 ggdendro_0.1.22
[61] sessioninfo_1.2.2 Rcpp_1.0.7 plyr_1.8.6
[64] base64enc_0.1-3 zlibbioc_1.40.0 RCurl_1.98-1.5
[67] ps_1.6.0 prettyunits_1.1.1 rpart_4.1-15
[70] pbapply_1.5-0 qgraph_1.9 haven_2.4.3
[73] cluster_2.1.2 fs_1.5.2 magrittr_2.0.1
[76] data.table_1.14.2 reprex_2.0.1 tmvnsim_1.0-2
[79] pkgload_1.2.4 hms_1.1.1 evaluate_0.14
[82] xtable_1.8-4 XML_3.99-0.8 jpeg_0.1-9
[85] readxl_1.3.1 gridExtra_2.3 shape_1.4.6
[88] testthat_3.1.1 compiler_4.1.2 crayon_1.4.2
[91] htmltools_0.5.2 corpcor_1.6.10 later_1.3.0
[94] tzdb_0.2.0 Formula_1.2-4 geneplotter_1.72.0
[97] lubridate_1.8.0 DBI_1.1.1 dbplyr_2.1.1
[100] MASS_7.3-54 Matrix_1.4-0 cli_3.1.0
[103] parallel_4.1.2 pkgconfig_2.0.3 foreign_0.8-81
[106] xml2_1.3.3 foreach_1.5.1 pbivnorm_0.6.0
[109] annotate_1.72.0 bslib_0.3.1 ipflasso_1.1
[112] XVector_0.34.0 rvest_1.0.2 callr_3.7.0
[115] digest_0.6.29 Biostrings_2.62.0 rmarkdown_2.11
[118] cellranger_1.1.0 htmlTable_2.3.0 gtools_3.9.2
[121] lifecycle_1.0.1 nlme_3.1-153 glasso_1.11
[124] jsonlite_1.7.2 desc_1.4.0 fansi_0.5.0
[127] pillar_1.6.4 ggsci_2.9 lattice_0.20-45
[130] KEGGREST_1.34.0 fastmap_1.1.0 httr_1.4.2
[133] pkgbuild_1.3.1 survival_3.2-13 glue_1.5.1
[136] remotes_2.4.2 fdrtool_1.2.17 png_0.1-7
[139] iterators_1.0.13 glmnet_4.1-3 bit_4.0.4
[142] stringi_1.7.6 sass_0.4.0 blob_1.2.2
[145] latticeExtra_0.6-29 memoise_2.0.1