Analysis of the V1 V3 mTDCs proteomic data for the manuscript
Last updated: 2023-05-27
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Knit directory:
LungCancer_SotilloLab/analysis/
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Load packages and dataset
#package
library(DEP)
library(SummarizedExperiment)
library(MultiAssayExperiment)
library(proDA)
library(tidyverse)
source("../code/utils.R")
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
#dataset
load("../output/fpe_V1V2_DDA.RData")Figure 1: Differentially expressed proteins between V1 and V3 mTDCs
Data preprocesing
Subset dataset
fpeSub <- fpe[,fpe$treatment == "dmso"]
keepVal <- rowSums(is.na(assay(fpeSub)))/ncol(fpeSub) <= 0.5
fpeSub <- fpeSub[keepVal,]
assay(fpeSub) <- log2(assay(fpeSub))Imputation for PCA
imp <- DEP::impute(fpeSub, "bpca")
assays(fpeSub)[["imputed"]] <- assay(imp)PCA plot (Supplementary Figure 1D)
v3Color <- "#DC7970"
v1Color <- "#EAEAEA"
exprMat <- assays(fpeSub)[["imputed"]]
smpAnno <- colData(fpeSub) %>% as_tibble()
pcRes <- prcomp(t(exprMat))
varExp <- pcRes$sdev^2/sum(pcRes$sdev^2)*100
pcTab <- pcRes$x[,1:2] %>%
as_tibble(rownames = "sampleID") %>%
left_join(smpAnno)
ggplot(pcTab, aes(x=PC1, y=PC2)) +
geom_point(aes(fill = EA_variant), shape = 21, size=5) +
scale_fill_manual(values = c(V1 = v1Color, V3= v3Color), name = bquote(italic("EA")~"variant"))+
scale_x_continuous(limits = c(-18,18), breaks = seq(-15,15,5)) +
scale_y_continuous(limits = c(-18,18), breaks = seq(-15,15,5)) +
xlab(sprintf("PC1 (%1.1f%%)",varExp[1])) +
ylab(sprintf("PC2 (%1.1f%%)",varExp[2])) +
theme_bw() +
theme(panel.border = element_blank(),
legend.position = c(0.2,0.8),
legend.box.background = element_rect(),
axis.title = element_text(size=15),
axis.text = element_text(size=15))
ggsave("../docs/FigS1D_PCA_plot.pdf", height =4, width = 4)PDF file: FigS1D_PCA_plot.pdf
Differential expression analysis
Differential expression using proDA
exprMat <- assay(fpeSub)
designMat <- model.matrix(~EA_variant, colData(fpeSub))
fit <- proDA(exprMat, design = designMat)
resTab <- test_diff(fit, contrast = "EA_variantV3") %>%
arrange(pval) %>%
mutate(symbol = rowData(fpeSub[name,])$name) %>%
select(-name) %>%
dplyr::rename(log2FC = diff) %>%
select(symbol, pval, adj_pval, log2FC, t_statistic)
writexl::write_xlsx(resTab, "../docs/pTab_diffProt_V1V2.xlsx")Result table: pTab_diffProt_V1V2.xlsx
Note that the list is not filtered. You can filter the list in
excel based on your cut-off
Volcano plot
plotTab <- resTab %>%
mutate(ifSig = pval <= 0.05) %>%
mutate(group = case_when(ifSig & log2FC >0 ~ "Higher in V3",
ifSig & log2FC <0 ~ "Higher in V1",
TRUE ~ "n.s."))
pVol <- ggplot(plotTab, aes(x= log2FC, y=-log10(pval))) +
geom_point(aes(fill = group), shape =21, size=3) +
geom_hline(yintercept = -log10(0.05), linetype = "dotted") +
scale_fill_manual(values = c(`Higher in V1` = v1Color, `Higher in V3` = v3Color, n.s. = "grey30")) +
scale_y_continuous(limits = c(0,4), breaks = seq(0,4), expand = c(0,0)) +
xlim(-3,3) +
ylab(bquote(-Log[10]*italic(P)*"-value"))+
xlab(bquote(Log[2]*"LFQ intensity Fold Change"))+
theme_classic() +
theme(legend.position = "none",
axis.text = element_text(size=16),
axis.title = element_text(size=15))
gLegend <- ggplot(filter(plotTab, group != "n.s."),aes(x= log2FC, y=-log10(pval))) +
geom_point(aes(fill = group), shape =21, size=4) +
scale_fill_manual(values = c(`Higher in V1` = v1Color, `Higher in V3` = v3Color), name = NULL) +
theme_classic() +
theme(legend.position = "top",
legend.background = element_rect(color = "black", linewidth = 0.3),
legend.text = element_text(size=15))
pLegend <- cowplot::get_legend(gLegend)
pCom <- cowplot::plot_grid(pLegend, pVol, ncol=1, rel_heights = c(0.2,1))
pCom
ggsave("../docs/Fig1E_volcano.pdf", height = 5, width = 3.5)PDF file: Fig1E_volcano.pdf
Enrichment analysis (pre-ranked GSEA)
#function for running gsea
# Run and plot enrichment barplot
runGSEA <- function(resTab, gmts, pCutSet = 0.1, geneFdr =FALSE, setFdr = TRUE,
minSize = 15, maxSize = 400, nperm = 10000, collapsePathway = TRUE) {
resInput <- resTab
leadingEdgeTab <- tibble(Pathway=NULL, Gene = NULL, compare = NULL, setName = NULL)
plotOut <- lapply(names(gmts), function(pathName) {
print(paste0("Testing for: ", pathName))
enList <- lapply(unique(resInput$compare), function(eachPair) {
print(paste0("Condition: ", eachPair))
inputTab <- filter(resInput, compare == eachPair, !symbol%in% c("",NA)) %>%
arrange(pval) %>% distinct(symbol, .keep_all = TRUE) %>%
select(symbol, t_statistic)
geneList <- structure(inputTab$t_statistic, names = inputTab$symbol)
setList <- fgsea::gmtPathways(gmts[[pathName]])
fgRes <- fgsea::fgseaMultilevel(pathways = setList,
stats = geneList,
minSize=minSize, ## minimum gene set size
maxSize=maxSize) %>% arrange(pval)
if (collapsePathway) {
keepPath <- fgsea::collapsePathways(fgRes, setList, geneList)
fgRes <- fgRes[fgRes$pathway %in% keepPath$mainPathways,]
}
#get number of leading edge gene
fgRes$geneNum <- sapply(fgRes$leadingEdge, length)
#get leading edge gene table
eachLeadTab <- lapply(fgRes$pathway, function(eachPath) {
tibble(Pathway = eachPath,
Gene = fgRes[fgRes$pathway == eachPath,]$leadingEdge[[1]])
}) %>% bind_rows() %>%
mutate(compare = eachPair, setName = pathName)
fgRes <- fgRes %>% select(pathway, pval, padj, NES, geneNum) %>% as_tibble()
eachLeadTab <- filter(eachLeadTab, Pathway %in% fgRes$pathway)
leadingEdgeTab <<- bind_rows(leadingEdgeTab, eachLeadTab)
fgRes
})
names(enList) <- unique(resInput$compare)
enList
})
names(plotOut) <- names(gmts)
plotOut[["leadingEdgeGene"]] <- leadingEdgeTab
return(plotOut)
}Using Canonical pathway sets
resTab <- mutate(resTab, compare = "V3 versus V1")
gmts <- list(CanonicalPathway = "../data/gmts/m2.cp.v2022.1.Mm.symbols.gmt")
plotList <- runGSEA(resTab, gmts, pCutSet = 0.05, setFdr = FALSE)[1] "Testing for: CanonicalPathway"
[1] "Condition: V3 versus V1"Table of enriched pathways (p-value < 0.05)
writexl::write_xlsx(plotList$CanonicalPathway$`V3 versus V1`, "../docs/GSEA_pathway_V1V3.xlsx")Plot
plotTab <- plotList$CanonicalPathway$`V3 versus V1` %>%
arrange(NES) %>%
mutate(pathway = str_remove_all(pathway, "WP|REACTOME|HALLMARK")) %>%
mutate(pathway = str_replace_all(pathway, "_", " ")) %>%
mutate(pathway = factor(pathway, levels = pathway))
ggplot(plotTab, aes(x=NES, y=pathway)) +
geom_segment(aes(y=pathway, yend=pathway, x=0, xend=NES), color = "grey50")+
geom_point(aes(color = padj, size = geneNum)) +
scale_color_gradient(low = "navy", high = "red",
breaks = seq(0.05,0.2,0.05), limits=c(0.05, 0.22), name = "FDR") +
scale_size_continuous(name = "SIZE") +
ylab("") + xlab("Normalized enrichment score") +
theme_bw() +
theme(panel.border = element_blank(),
axis.text.x = element_text(size=10))
ggsave("../docs/Fig1G_GSEA.pdf", height = 4, width = 7)Positive enrichment score indicates upregulated in V3
PDF file: Fig1G_GSEA.pdf
Using Hallmark sets
resTab <- mutate(resTab, compare = "V3 versus V1")
gmts <- list(CanonicalPathway = "../data/gmts/mh.all.v2022.1.Mm.symbols.gmt")
plotList <- runGSEA(resTab, gmts, pCutSet = 0.05, setFdr = FALSE)[1] "Testing for: CanonicalPathway"
[1] "Condition: V3 versus V1"Table of enriched pathways (p-value < 0.05)
writexl::write_xlsx(plotList$CanonicalPathway$`V3 versus V1`, "../docs/GSEA_Hallmark_V1V3.xlsx")Plot
plotTab <- plotList$CanonicalPathway$`V3 versus V1` %>%
arrange(NES) %>%
mutate(pathway = str_remove_all(pathway, "WP|REACTOME|HALLMARK")) %>%
mutate(pathway = str_replace_all(pathway, "_", " ")) %>%
mutate(pathway = factor(pathway, levels = pathway))
ggplot(plotTab, aes(x=NES, y=pathway)) +
geom_segment(aes(y=pathway, yend=pathway, x=0, xend=NES), color = "grey50")+
geom_point(aes(color = padj, size = geneNum)) +
scale_color_gradient(low = "navy", high = "red",
name = "FDR") +
scale_size_continuous(name = "SIZE") +
ylab("") + xlab("Normalized enrichment score") +
theme_bw() +
theme(panel.border = element_blank(),
axis.text.x = element_text(size=10))
ggsave("../docs/Fig1G_GSEA_Hallmark.pdf", height = 4, width = 7)Positive enrichment score indicates upregulated in V3
PDF file: Fig1G_GSEA_Hallmark.pdf
For figure 5G: Brigatinib resistance cells versus sensitive cells (V3 only)
Preprocessing
Subsetting
fpeSub <- fpe[,fpe$treatment %in% c("dmso","brigatinib") & fpe$EA_variant == "V3"]
fpeSub$treatment <- factor(fpeSub$treatment, levels = c("dmso","brigatinib"))
fpeSub <- fpeSub[!rowData(fpeSub)$name %in% c(NA,""),]
keepVal <- rowSums(is.na(assay(fpeSub)))/ncol(fpeSub) <= 0.5
fpeSub <- fpeSub[keepVal,]
assay(fpeSub) <- log2(assay(fpeSub))PCA plot (for a supplementary figure?)
#imputation for PCA
imp <- DEP::impute(fpeSub, "bpca")
assays(fpeSub)[["imputed"]] <- assay(imp)
exprMat <- assays(fpeSub)[["imputed"]]
smpAnno <- colData(fpeSub) %>% as_tibble()
pcRes <- prcomp(t(exprMat))
varExp <- pcRes$sdev^2/sum(pcRes$sdev^2)*100
pcTab <- pcRes$x[,1:2] %>%
as_tibble(rownames = "sampleID") %>%
left_join(smpAnno)
ggplot(pcTab, aes(x=PC1, y=PC2)) +
geom_point(aes(fill = treatment), shape = 21, size=5) +
scale_fill_manual(values = c(dmso = v1Color, brigatinib= v3Color), name = "treatment")+
scale_x_continuous(limits = c(-20,20), breaks = seq(-20,20,5)) +
scale_y_continuous(limits = c(-20,20), breaks = seq(-20,20,5)) +
xlab(sprintf("PC1 (%1.1f%%)",varExp[1])) +
ylab(sprintf("PC2 (%1.1f%%)",varExp[2])) +
theme_bw() +
theme(panel.border = element_blank(),
legend.position = c(0.8,0.85),
legend.box.background = element_rect(),
axis.title = element_text(size=15),
axis.text = element_text(size=15))
ggsave("../docs/briga_DMSO_PCA_plot.pdf", height =4, width = 4)PDF file: briga_DMSO_PCA_plot.pdf
Differential expression test (briga versus dmso in V3)
exprMat <- assay(fpeSub)
designMat <- model.matrix(~treatment, colData(fpeSub))
fit <- proDA(exprMat, design = designMat)
resTab <- test_diff(fit, contrast = "treatmentbrigatinib") %>%
arrange(pval) %>%
mutate(symbol = rowData(fpeSub[name,])$name) %>%
select(-name) %>%
dplyr::rename(log2FC = diff) %>%
select(symbol, pval, adj_pval, log2FC, t_statistic)
writexl::write_xlsx(resTab, "../docs/pTab_diffProt_briga_dmso.xlsx")Result table: pTab_diffProt_briga_dmso.xlsx
Note that the list is not filtered. You can filter the list in
excel based on your cut-off
Positive logFC means proteins are up-regulated in brigatinib
treatment cells
Enrichment analysis (pre-ranked GSEA)
Using Canonical pathways
resTab <- mutate(resTab, compare = "brigatinib versus dmso")
gmts <- list(CanonicalPathway = "../data/gmts/m2.cp.v2022.1.Mm.symbols.gmt")
plotList <- runGSEA(resTab, gmts, pCutSet = 0.05, setFdr = FALSE)[1] "Testing for: CanonicalPathway"
[1] "Condition: brigatinib versus dmso"Table of enriched pathways (p-value < 0.05)
writexl::write_xlsx(plotList$CanonicalPathway$`brigatinib versus dmso`, "../docs/GSEA_pathway_brigaResistant.xlsx")Plot
plotTab <- plotList$CanonicalPathway$`brigatinib versus dmso` %>%
arrange(NES) %>%
mutate(pathway = str_remove_all(pathway, "WP|REACTOME")) %>%
mutate(pathway = str_replace_all(pathway, "_", " ")) %>%
mutate(pathway = factor(pathway, levels = pathway))
ggplot(plotTab, aes(x=NES, y=pathway)) +
geom_segment(aes(y=pathway, yend=pathway, x=0, xend=NES), color = "grey50")+
geom_point(aes(color = padj, size = geneNum)) +
scale_color_gradient(low = "navy", high = "red",
breaks = seq(0.00,0.2,0.05), limits=c(0.00, 0.22), name = "FDR") +
scale_size_continuous(name = "SIZE") +
ylab("") + xlab("Normalized enrichment score") +
theme_bw() +
theme(panel.border = element_blank(),
axis.text.x = element_text(size=10))
ggsave("../docs/BrigaResistant_GSEA.pdf", height = 5, width = 8)Positive enrichment score indicates upregulated in brigatinib resistant
PDF file: BrigaResistant_GSEA.pdf
Using Hallmark
resTab <- mutate(resTab, compare = "brigatinib versus dmso")
gmts <- list(CanonicalPathway = "../data/gmts/mh.all.v2022.1.Mm.symbols.gmt")
plotList <- runGSEA(resTab, gmts, pCutSet = 0.05, setFdr = FALSE)[1] "Testing for: CanonicalPathway"
[1] "Condition: brigatinib versus dmso"Table of enriched pathways (p-value < 0.05)
writexl::write_xlsx(plotList$CanonicalPathway$`brigatinib versus dmso`, "../docs/GSEA_Hallmark_brigaResistant.xlsx")Plot
plotTab <- plotList$CanonicalPathway$`brigatinib versus dmso` %>%
arrange(NES) %>%
mutate(pathway = str_remove_all(pathway, "WP|REACTOME|HALLMARK")) %>%
mutate(pathway = str_replace_all(pathway, "_", " ")) %>%
mutate(pathway = factor(pathway, levels = pathway))
ggplot(plotTab, aes(x=NES, y=pathway)) +
geom_segment(aes(y=pathway, yend=pathway, x=0, xend=NES), color = "grey50")+
geom_point(aes(color = padj, size = geneNum)) +
scale_color_gradient(low = "navy", high = "red",
name = "FDR") +
scale_size_continuous(name = "SIZE") +
ylab("") + xlab("Normalized enrichment score") +
theme_bw() +
theme(panel.border = element_blank(),
axis.text.x = element_text(size=10))
ggsave("../docs/BrigaResistant_GSEA_Hallmark.pdf", height = 5, width = 8)PDF file: BrigaResistant_GSEA_Hallmark.pdf
List of proteins up-regulated in brigatinib resistant cell lines and down-regulated in combo compared to brigatinib
Define a list of up-regulated proteins in resistant cell lines (using p-value < 0.05, log2FC > 0)
resTab.up <- resTab %>% filter(pval < 0.05, log2FC > 0) %>%
arrange(pval) %>% distinct(symbol, .keep_all = TRUE) %>%
dplyr::rename(`pval (briga resistace)` = pval, `log2FC (briga resistance)`= log2FC) %>%
select(-adj_pval, -t_statistic)DE list of combo versus brigatinib at 16 hours
load("../output/allResList_RUN5_timeBased.RData")
deTab.comboDown <- allResList$diffProt$time_16 %>% filter(compare == "combo_brigatinib") %>%
filter(pval < 0.05, diff <0) %>%
arrange(pval) %>% distinct(symbol, .keep_all = TRUE) %>%
dplyr::rename(`pval (combo vs briga)` = pval, `log2FC (combo vs briga)`= diff) %>%
select(-adj_pval, -t_statistic, -name, -compare)Table of common proteins
overlap <- intersect(resTab.up$symbol, deTab.comboDown$symbol)
comTab <- left_join(filter(resTab.up,symbol %in% overlap),
filter(deTab.comboDown, symbol %in% overlap), by = "symbol")
writexl::write_xlsx(comTab, "../docs/overlap_protein_list_briga_combo.xlsx")overlap_protein_list_briga_combo.xlsx
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.1
[3] dplyr_1.0.9 purrr_0.3.4
[5] readr_2.1.2 tidyr_1.2.0
[7] tibble_3.1.8 ggplot2_3.4.1
[9] tidyverse_1.3.2 proDA_1.10.0
[11] MultiAssayExperiment_1.22.0 SummarizedExperiment_1.26.1
[13] Biobase_2.56.0 GenomicRanges_1.48.0
[15] GenomeInfoDb_1.32.2 IRanges_2.30.0
[17] S4Vectors_0.34.0 BiocGenerics_0.42.0
[19] MatrixGenerics_1.8.1 matrixStats_0.62.0
[21] DEP_1.18.0
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1 circlize_0.4.15
[4] fastmatch_1.1-3 workflowr_1.7.0 systemfonts_1.0.4
[7] plyr_1.8.7 gmm_1.6-6 shinydashboard_0.7.2
[10] BiocParallel_1.30.3 digest_0.6.30 foreach_1.5.2
[13] htmltools_0.5.4 fansi_1.0.3 magrittr_2.0.3
[16] googlesheets4_1.0.0 cluster_2.1.3 doParallel_1.0.17
[19] tzdb_0.3.0 limma_3.52.2 ComplexHeatmap_2.12.0
[22] modelr_0.1.8 imputeLCMD_2.1 sandwich_3.0-2
[25] colorspace_2.0-3 rvest_1.0.2 textshaping_0.3.6
[28] haven_2.5.0 xfun_0.31 crayon_1.5.2
[31] RCurl_1.98-1.7 jsonlite_1.8.3 impute_1.70.0
[34] zoo_1.8-10 iterators_1.0.14 glue_1.6.2
[37] gtable_0.3.0 gargle_1.2.0 zlibbioc_1.42.0
[40] XVector_0.36.0 GetoptLong_1.0.5 DelayedArray_0.22.0
[43] shape_1.4.6 scales_1.2.0 vsn_3.64.0
[46] mvtnorm_1.1-3 DBI_1.1.3 Rcpp_1.0.9
[49] mzR_2.30.0 xtable_1.8-4 clue_0.3-61
[52] preprocessCore_1.58.0 MsCoreUtils_1.8.0 DT_0.23
[55] htmlwidgets_1.5.4 httr_1.4.3 fgsea_1.22.0
[58] RColorBrewer_1.1-3 ellipsis_0.3.2 farver_2.1.1
[61] pkgconfig_2.0.3 XML_3.99-0.10 sass_0.4.2
[64] dbplyr_2.2.1 utf8_1.2.2 labeling_0.4.2
[67] tidyselect_1.1.2 rlang_1.0.6 later_1.3.0
[70] munsell_0.5.0 cellranger_1.1.0 tools_4.2.0
[73] cachem_1.0.6 cli_3.4.1 generics_0.1.3
[76] broom_1.0.0 evaluate_0.15 fastmap_1.1.0
[79] ragg_1.2.2 mzID_1.34.0 yaml_2.3.5
[82] knitr_1.39 fs_1.5.2 ncdf4_1.19
[85] mime_0.12 xml2_1.3.3 compiler_4.2.0
[88] rstudioapi_0.13 png_0.1-7 affyio_1.66.0
[91] reprex_2.0.1 bslib_0.4.1 stringi_1.7.8
[94] highr_0.9 MSnbase_2.22.0 lattice_0.20-45
[97] ProtGenerics_1.28.0 Matrix_1.5-4 tmvtnorm_1.5
[100] vctrs_0.5.2 pillar_1.8.0 norm_1.0-10.0
[103] lifecycle_1.0.3 BiocManager_1.30.18 jquerylib_0.1.4
[106] MALDIquant_1.21 GlobalOptions_0.1.2 data.table_1.14.8
[109] cowplot_1.1.1 bitops_1.0-7 httpuv_1.6.6
[112] extraDistr_1.9.1 R6_2.5.1 pcaMethods_1.88.0
[115] affy_1.74.0 promises_1.2.0.1 gridExtra_2.3
[118] writexl_1.4.0 codetools_0.2-18 MASS_7.3-58
[121] assertthat_0.2.1 rprojroot_2.0.3 rjson_0.2.21
[124] withr_2.5.0 GenomeInfoDbData_1.2.8 parallel_4.2.0
[127] hms_1.1.1 grid_4.2.0 rmarkdown_2.14
[130] googledrive_2.0.0 git2r_0.30.1 shiny_1.7.4
[133] lubridate_1.8.0