Identify drug responses associated with genomics in CLL with IC50 screen data
Junyan Lu
9 June 2022
Last updated: 2022-06-09
Checks: 6 1
Knit directory: EMBL2016/analysis/
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Load libraries and dataset
Load datasets
Preprocessing IC50 screen data
Select CLL samples and use AUC as measures of drug effect
screenData <- ic50 %>%
dplyr::rename(viab = normVal, viab.auc = normVal_auc, conc = Concentration) %>%
filter(!Drug%in% c("PBS","DMSO"))
#for the drugs that are also in EMBL2016 screen, use the same name as in EMBL2016 screen
screenData <- mutate(screenData, emblName = targetAnno[match(Drug, targetAnno$nameIC50),]$nameEMBL2016) %>%
mutate(Drug = ifelse(is.na(emblName), Drug, emblName)) %>%
select(-emblName)Overview of associations between drug responses and genomics in CLL
Using AUC of trepazoidal rule
Without blocking for IGHV
Only mutations occcured at least 5 times will be included in the test
Perform test
Adjust p-value use IHW, using standard deviation as covariate
meanSdTab <- tibble(name = rownames(viabMat),
meanVal = rowMeans(viabMat, na.rm = TRUE),
sdVal = genefilter::rowSds(viabMat, na.rm=TRUE))
ihwTab <- tibble(pval = pTab$p, name = pTab$drug) %>%
left_join(meanSdTab)
ihwRes <- ihw(pval ~ sdVal, data = ihwTab, alpha = 0.1)
pTab$p.adj.ihw <- adj_pvalues(ihwRes)
#plot(ihwRes)Write out test result table
write_csv2(pTab,"../docs/p_table_noBlock_IC50.csv")Number of significant associations per gene (10% FDR)
Number of significant associations per gene (10% FDR), adjusted by IHW
Seems to help a little, especially with some each associations.
Use the result from p values adjustment by IHW
pTab <- mutate(pTab, p.adj = p.adj.ihw)P value scatter plot (only show drugs with assocations)
Associations pass 10% FDR are colored by genes.
Only a few associations pass the 10% FDR threshold, although many
associations pass raw p-value 0.01 threshold. This could be due to the
multiple hypothesis testing problem. We have more drugs in EMLB2016
screen than other screens. (I already pre-filtered the drugs that show
very little variance across samples.)
PDF version: pScatter-1.pdf
Volcano plots
#filter genes with significant assocaitions
useGene <- unique(filter(pTab, p.adj <=0.1)$gene)
#get top 10 most up and down regulated genes
upDrug <- lapply(unique(pTab$gene), function(n) {
dplyr::filter(pTab, gene ==n, logFC >0) %>% top_n(10, -log10(p))
}) %>% bind_rows()
downDrug <- lapply(unique(pTab$gene), function(n) {
dplyr::filter(pTab, gene == n, logFC < 0) %>% top_n(10, -log10(p))
}) %>% bind_rows()
drugLab <- bind_rows(upDrug, downDrug) %>%
filter(p.adj <0.1) %>%
mutate(drugLabel = drug) %>% select(drug, gene, drugLabel)
plotList <- lapply(useGene, function(n) {
eachTab <- filter(pTab, gene %in% n, !is.na(p)) %>%
mutate(direction = ifelse(p.adj > 0.1, "n.s.",
ifelse(logFC>0, "resistant","sensitive"))) %>%
left_join(drugLab, by = c("drug", "gene"))
#pCut <- -log10(max(filter(eachTab, p.adj <=0.1)$p))
pCut <- -log10(0.1)
ggplot(eachTab, aes(x=logFC, y = -log10(p.adj))) +
geom_point(aes(col = direction)) +
ggrepel::geom_text_repel(aes(label = drugLabel), max.overlaps = 100) +
scale_color_manual(values = c("n.s."="grey60","resistant" = "red","sensitive" = "blue")) +
geom_hline(yintercept = pCut, linetype = "dashed", color = "orange") +
ylab("-log10(adjusted P value)") + xlab("log2(fold change)") +
ggtitle(sprintf("%s (mutated vs unmutated)", n)) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, size=15, face ="bold"),
legend.position = "bottom",
axis.text = element_text(size=14),
axis.title = element_text(size=14))
})
plot_grid(plotlist = plotList, ncol=2)
makepdf(plotList, "../docs/volcano_noBlocking_IC50.pdf", ncol=2, nrow=2, height = 10, width = 9)PDF version: volcano_noBlocking_IC50.pdf
A table of significant associations
filter(pTab, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Blocking for IGHV
Number of significant associations per gene (10% FDR)
Associations pass 10% FDR are colored by genes.
Number of significant associations per gene (10% FDR), adjusted by IHW
Number of significant associations per gene, blocking for non-IGHV features
P value scatter plot
PDF version: pScatter_aov-1.pdf
Volcano plots
#filter genes with significant assocaitions
useGene <- unique(filter(pTab.block, p.adj <=0.1)$gene)
#get top 10 most up and down regulated genes
upDrug <- lapply(unique(pTab.block$gene), function(n) {
filter(pTab.block, gene ==n, logFC >0) %>% top_n(10, -log10(p))
}) %>% bind_rows()
downDrug <- lapply(unique(pTab.block$gene), function(n) {
filter(pTab.block, gene == n, logFC < 0) %>% top_n(10, -log10(p))
}) %>% bind_rows()
drugLab <- bind_rows(upDrug, downDrug) %>%
filter(p.adj <0.1) %>%
mutate(drugLabel = drug) %>% select(drug, gene, drugLabel)
plotList <- lapply(useGene, function(n) {
eachTab <- filter(pTab.block, gene %in% n, !is.na(p)) %>%
mutate(direction = ifelse(p.adj > 0.1, "n.s.",
ifelse(logFC>0, "resistant","sensitive"))) %>%
left_join(drugLab, by = c("drug", "gene"))
#pCut <- -log10(max(filter(eachTab, p.adj <=0.1)$p))
pCut <- -log10(0.1)
ggplot(eachTab, aes(x=logFC, y = -log10(p.adj))) +
geom_point(aes(col = direction)) +
ggrepel::geom_text_repel(aes(label = drugLabel), max.overlaps = 100) +
scale_color_manual(values = c("n.s."="grey60","resistant" = "red","sensitive" = "blue")) +
geom_hline(yintercept = pCut, linetype = "dashed", color = "orange") +
ylab("-log10(adjusted P value)") + xlab("log2(fold change)") +
ggtitle(sprintf("%s (mutated vs unmutated)", n)) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, size=15, face ="bold"),
legend.position = "bottom",
axis.text = element_text(size=14),
axis.title = element_text(size=14))
})
plot_grid(plotlist = plotList, ncol=2)
makepdf(plotList, "../docs/volcano_withBlocking_IC50.pdf", ncol=2, nrow=2, height = 10, width = 9)PDF version: volcano_withBlocking_IC50.pdf
A table of significant associations
filter(pTab.block, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Within M-CLL only
Only mutations occcured at least 3 times will be included in the test
Number of significant associations per gene (10% FDR)
Volcano plots
#filter genes with significant assocaitions
useGene <- unique(filter(pTab, p.adj <=0.1)$gene)
plotList <- lapply(useGene, function(n) {
eachTab <- filter(pTab, gene %in% n, !is.na(p)) %>%
mutate(direction = ifelse(p.adj > 0.1, "n.s.",
ifelse(logFC>0, "resistant","sensitive"))) %>%
mutate(drugLabel = ifelse(direction == "n.s.","",drug))
pCut <- -log10(max(filter(eachTab, p.adj <=0.1)$p))
ggplot(eachTab, aes(x=logFC, y = -log10(p))) +
geom_point(aes(col = direction)) +
ggrepel::geom_text_repel(aes(label = drugLabel), max.overlaps = 100) +
scale_color_manual(values = c("n.s."="grey60","resistant" = "red","sensitive" = "blue")) +
geom_hline(yintercept = pCut, linetype = "dashed", color = "orange") +
ylab("-log10 (P value)") + xlab("log2 Fold Change") +
ggtitle(sprintf("%s (mutated vs unmutated)", n)) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, size=15, face ="bold"),
legend.position = "bottom")
})
plot_grid(plotlist = plotList, ncol=2)
makepdf(plotList, "../docs/volcano_M_CLL_IC50.pdf", ncol=1, nrow=1, height = 12, width = 12)PDF version: volcano_M_CLL_IC50.pdf
A table of significant associations
filter(pTab, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Within U-CLL only
Only mutations occcured at least 3 times will be included in the test
Number of significant associations per gene (10% FDR)
Volcano plots
#filter genes with significant assocaitions
useGene <- unique(filter(pTab, p.adj <=0.1)$gene)
plotList <- lapply(useGene, function(n) {
eachTab <- filter(pTab, gene %in% n, !is.na(p)) %>%
mutate(direction = ifelse(p.adj > 0.1, "n.s.",
ifelse(logFC>0, "resistant","sensitive"))) %>%
mutate(drugLabel = ifelse(direction == "n.s.","",drug))
pCut <- -log10(max(filter(eachTab, p.adj <=0.1)$p))
ggplot(eachTab, aes(x=logFC, y = -log10(p))) +
geom_point(aes(col = direction)) +
ggrepel::geom_text_repel(aes(label = drugLabel), max.overlaps = 100) +
scale_color_manual(values = c("n.s."="grey60","resistant" = "red","sensitive" = "blue")) +
geom_hline(yintercept = pCut, linetype = "dashed", color = "orange") +
ylab("-log10 (P value)") + xlab("log2 Fold Change") +
ggtitle(sprintf("%s (mutated vs unmutated)", n)) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, size=15, face ="bold"),
legend.position = "bottom")
})
plot_grid(plotlist = plotList, ncol=2)
makepdf(plotList, "../docs/volcano_U_CLL_IC50.pdf", ncol=1, nrow=1, height = 12, width = 12)PDF version: volcano_U_CLL_IC50.pdf
A table of significant associations
filter(pTab, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Using individual concentrations
Without blocking for IGHV
p_table_noBlock_allConc_IC50.csv
Number of significant associations per gene (10%
FDR)
P value heatmap
Only drugs show at least one significant association under 10% FDR
pTab.sig <- filter(pTab, p.adj <= 0.1)
plotTab <- filter(pTab, gene %in% pTab.sig$gene) %>%
filter(Drug %in% pTab.sig$Drug) %>%
mutate(sign = ifelse(p.adj <= 0.1, "*",""),
pSign = -log10(p)) %>%
mutate(pSign = ifelse(pSign > 12, 12, pSign)) %>%
mutate(pSign = pSign * sign(logFC),
Drug = sprintf("%s (%s)",Drug, targetFamily))
pMat <- mutate(plotTab, geneConc = paste0(gene,"_", concIndex)) %>%
select(Drug, geneConc, pSign) %>%
pivot_wider(names_from = geneConc, values_from = pSign) %>%
data.frame() %>% column_to_rownames("Drug")
hc <- hclust(dist(pMat))
drugOrder <- rownames(pMat)[hc$order]
plotTab <- mutate(plotTab, Drug = factor(Drug, levels = drugOrder),
gene = factor(gene, levels = levels(sumTab$gene)))
ggplot(plotTab, aes(x=concIndex, y = Drug, fill = pSign)) +
geom_tile() + geom_text(aes(label=sign), nudge_y = -0.25) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", name ="-log10(P-value)") +
facet_wrap(~gene, ncol =12) +
xlab("concentration index")
* indicates assocations passed 10% FDR control
A table of significant associations
filter(pTab, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
left_join(select(targetAnno, drugName, target, pathway), by = c(Drug = "drugName")) %>%
DT::datatable()Blocking for IGHV
p_table_ighvBlock_allConc_IC50.csv
Number of significant associations per gene (10%
FDR)
P value heatmap
Only drugs show at least one significant association under 10% FDR
pTab.sig <- filter(pTab, p.adj <= 0.1)
plotTab <- filter(pTab, gene %in% pTab.sig$gene) %>%
filter(Drug %in% pTab.sig$Drug) %>%
mutate(sign = ifelse(p.adj <= 0.1, "*",""),
pSign = -log10(p)) %>%
mutate(pSign = ifelse(pSign > 12, 12, pSign)) %>%
mutate(pSign = pSign * sign(logFC),
Drug = sprintf("%s (%s)",Drug, targetFamily))
pMat <- mutate(plotTab, geneConc = paste0(gene,"_", concIndex)) %>%
select(Drug, geneConc, pSign) %>%
pivot_wider(names_from = geneConc, values_from = pSign) %>%
data.frame() %>% column_to_rownames("Drug")
hc <- hclust(dist(pMat))
drugOrder <- rownames(pMat)[hc$order]
plotTab <- mutate(plotTab, Drug = factor(Drug, levels = drugOrder),
gene = factor(gene, levels = levels(sumTab$gene)))
ggplot(plotTab, aes(x=concIndex, y = Drug, fill = pSign)) +
geom_tile() + geom_text(aes(label=sign), nudge_y = -0.25) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", name = "-log10(P-value)") +
facet_wrap(~gene, ncol =12) +
xlab("concentration index")
* indicates associations passed 10% FDR control
A table of significant associations
targetAnno <- read_csv2("../data/targetAnnotation_all.csv") %>%
mutate(drugName = nameEMBL2016)
filter(pTab, p.adj <=0.1) %>% mutate_if(is.numeric, formatC, digits=2) %>%
left_join(select(targetAnno, drugName, target, pathway), by = c(Drug = "drugName")) %>%
DT::datatable()Drug responses associated with IGHV
Volcano plot
Drugs colored by blue are more effective in U-CLL samples. The names of
the drugs that show significant associations and effect size above 10%
in at least 3 concentrations are labeled. Dashed line indicates 5%
FDR
As expected, M-CLL samples show increased resistance to a lot of drugs.
Drug responses associated with Trisomy12
For all CLLs
How many triosmy12 samples?
tri12Tab <- distinct(viabTab, patientID, .keep_all = TRUE)
tri12Tab %>% filter(trisomy12 == 1) %>% nrow()[1] 27Volcano plot (10% FDR cut-off) for combined
concentrations
Drugs colored by blue are more effective in samples with trisomy12. The
names of the drugs that show significant associations in at least 2
concentrations are labeled. Dashed line indicates 10% FDR.
Volcano plots for individual concentrations
Beeswarm plots for all drug at all concentrations
For M CLLs only
Volcano plot (combined concentrations)
Volcation plots for individual concentrations
Beeswarm plots for all drug at all concentrations
For U CLLs
Volcano plot (combined concentrations)
Volcation plots for individual concentrations
Beeswarm plots for all drug at all concentrations
Drug_VS_trisomy12_allConc_U-CLL_IC50.pdf
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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_1.0.9
[4] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[7] tibble_3.1.7 tidyverse_1.3.1 limma_3.52.1
[10] IHW_1.24.0 readxl_1.4.0 gtable_0.3.0
[13] ggbeeswarm_0.6.0 jyluMisc_0.1.5 colorspace_2.0-3
[16] RColorBrewer_1.1-3 ggrepel_0.9.1 ggplot2_3.3.6
[19] cowplot_1.1.1 genefilter_1.78.0 pheatmap_1.0.12
[22] reshape2_1.4.4 gridExtra_2.3 Biobase_2.56.0
[25] BiocGenerics_0.42.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2
[3] tidyselect_1.1.2 RSQLite_2.2.14
[5] AnnotationDbi_1.58.0 htmlwidgets_1.5.4
[7] grid_4.2.0 BiocParallel_1.30.2
[9] maxstat_0.7-25 munsell_0.5.0
[11] codetools_0.2-18 DT_0.23
[13] withr_2.5.0 highr_0.9
[15] knitr_1.39 rstudioapi_0.13
[17] stats4_4.2.0 ggsignif_0.6.3
[19] MatrixGenerics_1.8.0 labeling_0.4.2
[21] git2r_0.30.1 slam_0.1-50
[23] GenomeInfoDbData_1.2.8 lpsymphony_1.24.0
[25] KMsurv_0.1-5 bit64_4.0.5
[27] farver_2.1.0 rprojroot_2.0.3
[29] vctrs_0.4.1 generics_0.1.2
[31] TH.data_1.1-1 xfun_0.31
[33] sets_1.0-21 R6_2.5.1
[35] GenomeInfoDb_1.32.2 bitops_1.0-7
[37] cachem_1.0.6 fgsea_1.22.0
[39] DelayedArray_0.22.0 assertthat_0.2.1
[41] promises_1.2.0.1 scales_1.2.0
[43] vroom_1.5.7 multcomp_1.4-19
[45] beeswarm_0.4.0 sandwich_3.0-1
[47] workflowr_1.7.0 rlang_1.0.2
[49] splines_4.2.0 rstatix_0.7.0
[51] broom_0.8.0 BiocManager_1.30.18
[53] yaml_2.3.5 abind_1.4-5
[55] modelr_0.1.8 crosstalk_1.2.0
[57] backports_1.4.1 httpuv_1.6.5
[59] tools_4.2.0 relations_0.6-12
[61] ellipsis_0.3.2 gplots_3.1.3
[63] jquerylib_0.1.4 Rcpp_1.0.8.3
[65] plyr_1.8.7 visNetwork_2.1.0
[67] zlibbioc_1.42.0 RCurl_1.98-1.6
[69] ggpubr_0.4.0 S4Vectors_0.34.0
[71] zoo_1.8-10 SummarizedExperiment_1.26.1
[73] haven_2.5.0 cluster_2.1.3
[75] exactRankTests_0.8-35 fs_1.5.2
[77] magrittr_2.0.3 data.table_1.14.2
[79] reprex_2.0.1 survminer_0.4.9
[81] mvtnorm_1.1-3 matrixStats_0.62.0
[83] hms_1.1.1 shinyjs_2.1.0
[85] mime_0.12 evaluate_0.15
[87] xtable_1.8-4 XML_3.99-0.9
[89] IRanges_2.30.0 compiler_4.2.0
[91] KernSmooth_2.23-20 crayon_1.5.1
[93] htmltools_0.5.2 later_1.3.0
[95] tzdb_0.3.0 lubridate_1.8.0
[97] DBI_1.1.2 dbplyr_2.1.1
[99] MASS_7.3-57 BiocStyle_2.24.0
[101] Matrix_1.4-1 car_3.0-13
[103] cli_3.3.0 marray_1.74.0
[105] parallel_4.2.0 igraph_1.3.1
[107] GenomicRanges_1.48.0 pkgconfig_2.0.3
[109] km.ci_0.5-6 piano_2.12.0
[111] xml2_1.3.3 annotate_1.74.0
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[121] cellranger_1.1.0 fastmatch_1.1-3
[123] survMisc_0.5.6 shiny_1.7.1
[125] gtools_3.9.2 lifecycle_1.0.1
[127] jsonlite_1.8.0 carData_3.0-5
[129] fansi_1.0.3 pillar_1.7.0
[131] lattice_0.20-45 KEGGREST_1.36.0
[133] fastmap_1.1.0 httr_1.4.3
[135] plotrix_3.8-2 survival_3.3-1
[137] glue_1.6.2 fdrtool_1.2.17
[139] png_0.1-7 bit_4.0.4
[141] stringi_1.7.6 sass_0.4.1
[143] blob_1.2.3 caTools_1.18.2
[145] memoise_2.0.1