Apply DepInfeR to the GDSC drug screen datasets
Junyan Lu
2021-10-05
Last updated: 2022-01-11
Checks: 6 1
Knit directory: DepInfeR/analysis/
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Load packages
Packages
library(DepInfeR)
library(RColorBrewer)
library(pheatmap)
library(ggbeeswarm)
library(ggrepel)
library(tidyverse)
source("../code/utils.R")
knitr::opts_chunk$set(dev = c("png","pdf"))Load pre-processed datasets
load("../output/inputs_GDSC.RData")Dimensions of input matrices
Drug-target
dim(tarMat_GDSC)[1] 66 118Drug-sample (viability matrix)
dim(viabMat_GDSC)[1] 66 126Multivariant model for protein dependence prediction
Perform multivariant LASSO regression based on a drug-protein affinity matrix and a drug response matrix.
This chunk can take a long time to run. Therefore we will save the result for later use to save time.
set.seed(333)
result <- runLASSOregression(TargetMatrix = tarMat_GDSC, ResponseMatrix = viabMat_GDSC)
#remove targets that were never selected
useTar <- rowSums(result$coefMat) != 0
result$coefMat <- result$coefMat[useTar,]
#save intermediate results
save(result, file = "../output/GDSC_result.RData")Load the saved result
load("../output/GDSC_result.RData")Number of selected targets
nrow(result$coefMat)[1] 9Heatmap plots of protein dependence matrix
The protein dependence matrix can be nicely visualized in a heatmap. High positive coefficients imply strong reliance of a certain sample on this protein for survival. Proteins with coefficients close to zero are less essential for the cell’s survival. Negative coefficients indicate that the viability phenotype benefits from inhibition of the protein.
Heatmap of protein dependence coefficients
annoColor <- list(H2O2 = c(`-1` = "red", `0` = "black", `1` = "green"),
IL.1 = c(`-1` = "red", `0` = "black", `1` = "green"),
JAK.STAT = c(`-1` = "red", `0` = "black", `1` = "green"),
MAPK.only = c(`-1` = "red", `0` = "black", `1` = "green"),
MAPK.PI3K = c(`-1` = "red", `0` = "black"),
TLR = c( `-1` = "red", `0` = "black", `1` = "green"),
Wnt = c(`-1` = "red", `0` = "black", `1` = "green"),
VEGF = c(`-1` = "red", `0` = "black", `1` = "green"),
PI3K.only = c(`-1` = "red", `0` = "black", `1` = "green"),
TCGA.classification = c(ALL="#BC3C29FF",AML="#E18727FF",DLBC="#20854EFF","BRCAHer-"="#0072B5FF",'BRCAHer+'="#7876B1FF"),
ARID1A_mut = c(`1` = "black",`0` = "grey80"),
EP300_mut = c(`1` = "black",`0` = "grey80"),
PTEN_mut = c(`1` = "black",`0` = "grey80"),
TP53_mut = c(`1` = "black",`0` = "grey80"),
PIK3CA_mut = c(`1` = "black",`0` = "grey80"),
BRCA2_mut = c(`1` = "black",`0` = "grey80"),
BRCA1_mut = c(`1` = "black",`0` = "grey80"),
CDH1_mut = c(`1` = "black",`0` = "grey80"),
FBXW7_mut = c(`1` = "black",`0` = "grey80"),
NRAS_mut = c(`1` = "black",`0` = "grey80"),
ASXL1_mut = c(`1` = "black",`0` = "grey80"),
MLL2_mut = c(`1` = "black",`0` = "grey80"),
ABL1_trans = c(`1` = "black",`0` = "grey80"),
missing_value_perc = c(`0` = "white",`25` = "red")
)
plotTab <- result$coefMat
#Row normalization while keeping sign
plotTab_scaled <- scale(t(plotTab), center = FALSE, scale= TRUE)
plotTab <- t(plotTab_scaled)
levels(mutation_GDSC$TCGA.classification) <- c(levels(mutation_GDSC$TCGA.classification), "BRCAHer-")
mutation_GDSC$TCGA.classification[mutation_GDSC$TCGA.classification=="BRCA"] <- "BRCAHer-"
mutation_GDSC$TCGA.classification <- factor(mutation_GDSC$TCGA.classification, levels = c("ALL", "AML", "DLBC", "BRCAHer-", "BRCAHer+"))
mutation_GDSC$missing_value_perc <- NULL
pheatmap(plotTab,
color=colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")), bias= 1.8)(100),
annotation_col = mutation_GDSC,
annotation_colors = annoColor,
clustering_method = "ward.D2", scale = "none",
show_colnames = TRUE, main = "", fontsize = 9, fontsize_row = 10, fontsize_col = 7)
Differential dependence on proteins associated with cancer types and genotypes
Prepare genomic background table
cell_anno_final <- mutation_GDSC %>%
#dplyr::select(-missing_value_perc) %>%
dplyr::rename(cancer_type = TCGA.classification) %>%
dplyr::filter(rownames(mutation_GDSC) %in% colnames(result$coefMat))
colnames(cell_anno_final) <- str_remove_all(colnames(cell_anno_final),"_mut")
colnames(cell_anno_final) <- str_replace_all(colnames(cell_anno_final),"_trans","_translocation")Association test between protein dependence and cancer type or mutational background
testRes <- diffImportance(result$coefMat, cell_anno_final)Visualize protein associations with cancer type
CancerType <- testRes %>% dplyr::filter(mutName == "cancer_type") %>% dplyr::filter(p.adj < 0.05, FC > 0.1)
plotTab <- t(scale(t(result$coefMat))) %>% data.frame() %>%
rownames_to_column("target") %>% gather(key = "CellLine", value = "coef",-target) %>% mutate(Cancer_Type = mutation_GDSC[CellLine,]$TCGA.classification) %>%
group_by(target, Cancer_Type) %>% mutate(meanCoef = mean(coef)) %>% arrange(meanCoef) %>% ungroup() %>%
mutate(target = factor(target, levels = unique(target)))
plotTab <- plotTab %>% dplyr::filter(target %in% CancerType$targetName)
plotTab$Cancer_Type <- factor(plotTab$Cancer_Type, levels = c("ALL", "AML","DLBC", "BRCAHer-", "BRCAHer+"))
ggplot(plotTab, aes(x = target, y = coef, group=Cancer_Type)) +
geom_jitter(
aes(color = Cancer_Type),
position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
size = 1.2
) +
stat_summary(
fun= mean, fun.min=mean, fun.max=mean, colour="grey25",
geom = "crossbar", size = 0.8,
position = position_dodge(0.8)
) +
scale_color_manual(values= c("#BC3C29FF","#E18727FF","#20854EFF","#0072B5FF","#7876B1FF"),
guide = guide_legend(override.aes = list(size = 3) )) +
ggtitle("Protein dependence associated with cancer type") + ylab("Protein dependence coefficient") + xlab("Protein") + theme_bw() +
theme_custom + geom_vline(xintercept =seq(from = 1.5, to = 8.5, by = 1), color="darkgrey") + labs(color = "Cancer Type")
Radar plot visualization
starMatrix <- plotTab %>% dplyr::select(target, Cancer_Type, meanCoef) %>% distinct()
starMatrix <- starMatrix %>% pivot_wider(names_from = target, values_from = meanCoef)
starMatrix <- starMatrix %>% mutate(Cancer_Type = factor(Cancer_Type, levels = c("ALL", "AML","DLBC", "BRCAHer-", "BRCAHer+"))) %>% arrange(Cancer_Type)
starMatrix <- starMatrix %>% column_to_rownames("Cancer_Type")
starMatrix_norm <- (as.matrix(starMatrix) + abs(min(starMatrix)))
zeroValue <-abs(min(starMatrix)) / max(starMatrix_norm)
starMatrix_norm <- starMatrix_norm/ max(starMatrix_norm)
#function for a single star plot
starPlot <- function(dataIn, sampleName,zeroVal, color='red') {
stopifnot(is.matrix(dataIn) & nrow(dataIn) == 1)
#data for outer ring
outer <- dataIn
outer[!is.na(outer)] <- 1
#data for inner ring
inner <- dataIn
inner[!is.na(inner)] <- zeroVal
#plotting
stars(outer, draw.segments = FALSE, scale=FALSE, full=TRUE, locations=c(1,1), mar = c(4,4,4,4), main=sampleName, cex=0.5) #plot the outter ring
stars(inner, draw.segments = FALSE, scale=FALSE, full=TRUE, locations=c(1,1), lty =2 ,add=TRUE) #plot the inner ring
stars(dataIn, col.stars=color, draw.segments = FALSE, scale=FALSE, full=TRUE, key.loc = c(1,1), key.labels = colnames(dataIn), location=c(1,1), add=TRUE,cex=1) #plot the actual data
}
starColor <- c("#BC3C29FF","#E18727FF","#20854EFF","#0072B5FF","#7876B1FF") #define color scheme
par(mfrow=c(2,3)) #layout in 3 X 3 format
for (i in seq(1,nrow(starMatrix_norm))) {
dataIn <- starMatrix_norm[i, ,drop=FALSE]
sampleName <- rownames(starMatrix_norm)[i]
starCol <- starColor[i]
starPlot(dataIn,sampleName,zeroValue, starCol)
} 
Visualize significant associations between protein dependence and mutational background
colList2 <- c(`not significant` = "grey80", mutated = "#BC3C29FF", unmutated = "#0072B5FF")
pos = position_jitter(width = 0.15, seed = 10)
plotTab <- testRes %>% dplyr::filter(mutName != "cancer_type") %>% mutate(type = ifelse(p.adj > 0.1, "not significant",
ifelse(FC >0, "mutated","unmutated"))) %>%
mutate(varName = ifelse(type == "not significant","",targetName)) %>%
mutate(p.adj = ifelse(p.adj <1e-5, 1e-5,p.adj))
#subset for mutation with at least one significant associations
plotMut <- unique(filter(testRes, p.adj <= 0.1)$mutName)
plotTab <- plotTab %>% dplyr::filter(mutName %in% plotMut)
plotTab$type <- factor(plotTab$type, levels = c("mutated", "unmutated", "not significant"))
p <- ggplot(data=plotTab, aes(x= mutName, y=-log10(p.adj),
col=type, label = varName))+
geom_text_repel(position = pos, color = "black", size= 6, force = 3) +
geom_hline(yintercept = -log10(0.1), linetype="dotted", color = "grey20") +
geom_point(size=3, position = pos) +
ylab(expression(-log[10]*'('*adjusted~italic("P")~value*')')) + xlab("Mutation") +
scale_color_manual(values = colList2) +
scale_y_continuous(trans = "exp", limits = c(0,2.5), breaks = c(0,1,1.5,2)) +
theme_custom +
#annotate(geom = "text", x = 0.5, y = -log10(0.1) - 0.25, label = "10% FDR", size=7, col = "grey20") +
coord_flip() + labs(col = "Higher dependence in") +
theme(legend.position = c(0.80,0.2),
legend.background = element_rect(fill = NA),
legend.text = element_text(size=14),
legend.title = element_text(size=16),
axis.title = element_text(size=18),
axis.text = element_text(size=18))
plot(p)
#ggsave("test.pdf",height = 4, width = 8)Visualize significant associations using a heatmap
plotTab <- testRes %>% dplyr::filter(mutName != "cancer_type") %>%
mutate(starSign = ifelse(p.adj <=0.1, "*", ""),
pSign = -log10(p)*sign(FC))
#subset for mutation with at least one significant associations
plotTar <- unique(filter(plotTab, p.adj <= 0.1)$targetName)
plotMut <- unique(filter(plotTab, p.adj <= 0.1)$mutName)
plotTab <- plotTab %>% dplyr::filter( targetName %in% plotTar , mutName %in% plotMut)
p <- ggplot(data=plotTab, aes(y=mutName, x = targetName, fill=pSign)) +
geom_tile(col = "black") + geom_text(aes(label = starSign), size=5, vjust=0.5) +
scale_fill_gradient2(low = "#BC3C29FF", high = "#0072B5FF", name = bquote(-log[10]*italic("P"))) +
theme_minimal() +
theme(panel.grid.major = element_blank(),
legend.text = element_text(size=14),
legend.title = element_text(size=16),
axis.title = element_text(size=18),
axis.text = element_text(size=18)) +
ylab("Mutations") + xlab("Proteins")
p
Visualization of exemplary association between NRAS mutation status and MAP2K2 dependence visualized in a beeswarm plot
pList <- plotDiffBox(testRes, result$coefMat, cell_anno_final, 0.05)
pList$MAP2K2_NRAS
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[5] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 tidyverse_1.3.1
[9] ggrepel_0.9.1 ggbeeswarm_0.6.0 ggplot2_3.3.5 pheatmap_1.0.12
[13] RColorBrewer_1.1-2 DepInfeR_0.1.0
loaded via a namespace (and not attached):
[1] matrixStats_0.61.0 fs_1.5.2 lubridate_1.8.0 doParallel_1.0.16
[5] httr_1.4.2 rprojroot_2.0.2 tools_4.1.2 backports_1.4.1
[9] doRNG_1.8.2 bslib_0.3.1 utf8_1.2.2 R6_2.5.1
[13] vipor_0.4.5 DBI_1.1.2 colorspace_2.0-2 withr_2.4.3
[17] tidyselect_1.1.1 compiler_4.1.2 git2r_0.29.0 glmnet_4.1-3
[21] cli_3.1.0 rvest_1.0.2 xml2_1.3.3 labeling_0.4.2
[25] sass_0.4.0 scales_1.1.1 digest_0.6.29 rmarkdown_2.11
[29] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[33] fastmap_1.1.0 rlang_0.4.12 readxl_1.3.1 rstudioapi_0.13
[37] farver_2.1.0 shape_1.4.6 jquerylib_0.1.4 generics_0.1.1
[41] jsonlite_1.7.2 magrittr_2.0.1 rlist_0.4.6.2 Matrix_1.4-0
[45] Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0 lifecycle_1.0.1
[49] stringi_1.7.6 yaml_2.2.1 grid_4.1.2 parallel_4.1.2
[53] promises_1.2.0.1 crayon_1.4.2 lattice_0.20-45 haven_2.4.3
[57] splines_4.1.2 hms_1.1.1 knitr_1.37 pillar_1.6.4
[61] rngtools_1.5.2 codetools_0.2-18 reprex_2.0.1 glue_1.6.0
[65] evaluate_0.14 data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8
[69] tzdb_0.2.0 httpuv_1.6.4 foreach_1.5.1 cellranger_1.1.0
[73] gtable_0.3.0 assertthat_0.2.1 xfun_0.29 broom_0.7.10
[77] later_1.3.0 survival_3.2-13 iterators_1.0.13 beeswarm_0.4.0
[81] workflowr_1.7.0 ellipsis_0.3.2