Correlate proteomic profiling with genomic background (without blocking for IGHV and trisomy12)
Junyan Lu
2020-03-07
Last updated: 2020-04-24
Checks: 5 2
Knit directory: Proteomics/analysis/
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Preprocessing
Process proteomics data
protMat <- assays(protCLL)[["count"]] #without imputation
protCLL$trisomy12 <- patMeta[match(colnames(protCLL),patMeta$Patient.ID),]$trisomy12Prepare genomic background
Get Mutations with at least 3 cases
geneMat <- patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11p:U1) %>%
mutate_if(is.factor, as.character) %>% mutate(IGHV.status = ifelse(IGHV.status == "M", 1,0)) %>%
mutate_at(vars(-Patient.ID), as.factor) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")
geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=3]Mutations that will be tested
colnames(geneMat) [1] "IGHV.status" "del11q" "del13q" "del14q" "del17p"
[6] "del5IgH" "IgH_break" "trisomy12" "trisomy19" "ATM"
[11] "BRAF" "DDX3X" "EGR2" "FBXW7" "HIST1H1E"
[16] "KRAS" "NOTCH1" "SF3B1" "TP53" Test if there’s interaction between gene mutations (potential confounders)
chiRes <- lapply(seq(1,ncol(geneMat)-1), function(i) {
lapply(seq(i+1, ncol(geneMat)), function(j) {
geneA <- colnames(geneMat)[i]
geneB <- colnames(geneMat)[j]
#res <- chisq.test(geneMat[,i],geneMat[,j])
res <- fisher.test(table(geneMat[,i], geneMat[,j]))
tibble(geneA = geneA, geneB=geneB, p = res$p.value)
}) %>% bind_rows()
}) %>% bind_rows() %>% arrange(p) %>%
filter(p <=0.05)
chiRes# A tibble: 10 x 3
geneA geneB p
<chr> <chr> <dbl>
1 del5IgH IgH_break 0.00226
2 del17p TP53 0.00710
3 IGHV.status del11q 0.0106
4 DDX3X EGR2 0.0111
5 trisomy12 trisomy19 0.0119
6 IGHV.status DDX3X 0.0219
7 del13q trisomy12 0.0222
8 DDX3X FBXW7 0.0265
9 IGHV.status TP53 0.0488
10 IGHV.status trisomy19 0.0496 Heatmap of mutations
plotMat <- apply(geneMat,2,as.numeric)
rownames(plotMat) <- rownames(geneMat)
plotMat <- data.frame(plotMat)
#A recursive function to sort table
sortTab <- function(sumTab) {
i <- ncol(sumTab)
#print(i)
if (i == 1) {
#print(rownames(sumTab)[order(sumTab[,i])])
return(rownames(sumTab)[order(sumTab[,i])])
}
orderRow <- c(sortTab(sumTab[sumTab[,i] %in% c(0,NA), seq(1,i-1), drop = FALSE]), sortTab(sumTab[sumTab[,i] %in% 1, seq(1,i-1), drop = FALSE]))
return(orderRow)
}
#order columns based on number of samples
plotMat <- plotMat[,order(colSums(plotMat, na.rm = TRUE))]
#sort rows based on completeness
sampleOrder <- sortTab(plotMat)
plotMat <- plotMat[rev(sampleOrder),]
pheatmap(t(plotMat), cluster_rows = FALSE, cluster_cols = FALSE)
It seems most del11q, NOTCH1, TP53, DDX3X are U-CLLs while most trisomy19 are M-CLL samples.
Test for all variantions (no blocking)
Fit the probailistic dropout model and test for differentially expressed proteins
resList <- lapply(colnames(geneMat), function(n) {
designMat <- geneMat[,n, drop =FALSE]
designMat <- designMat[!is.na(designMat[[n]]),,drop=FALSE]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- paste0(n,"1")
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()P-value histogram for each genomic feature
ggplot(resList, aes(x=P.Value)) + geom_histogram(fill = "green", alpha =0.5, bins=30, col = "grey50") + facet_wrap(~Gene, ncol=4, scales = "fixed") + xlim(0,1)Warning: Removed 38 rows containing missing values (geom_bar).
Number of significantly associated proteins at 10% FDR
proNumTab <- resList %>% group_by(Gene) %>%
summarise(number = sum(adj.P.Val < 0.1, na.rm=TRUE)) %>%
arrange(desc(number)) %>% mutate(Gene = factor(Gene, levels = Gene))
proNumTab# A tibble: 19 x 2
Gene number
<fct> <int>
1 trisomy12 1111
2 IGHV.status 336
3 trisomy19 260
4 del11q 30
5 SF3B1 20
6 KRAS 13
7 del14q 12
8 ATM 9
9 del17p 2
10 EGR2 1
11 BRAF 0
12 DDX3X 0
13 del13q 0
14 del5IgH 0
15 FBXW7 0
16 HIST1H1E 0
17 IgH_break 0
18 NOTCH1 0
19 TP53 0Based on the P-value histograms and numbers of significant associations, trisomy12 has the most impact on protein expression, followed by IGHV and del11q. Other genomic variations do not seem to have major impact on protein expression.
Visualize results
IGHV status
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "IGHV.status", adj.P.Val < 0.1) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()Volcano plot
plotVolcano <- function(pTab, fdrCut = 0.05, posCol = "red", negCol = "blue",
x_lab = "dm", plotTitle = "",ifLabel = FALSE,
colLabel = NULL) {
plotTab <- pTab %>% mutate(ifSig = ifelse(adj.P.Val > fdrCut, "n.s.",
ifelse(logFC > 0, "up","down"))) %>%
mutate(ifSig = factor(ifSig, levels = c("up","down","n.s.")))
pCut <- -log10((filter(plotTab, ifSig != "n.s.") %>% arrange(desc(P.Value)))$P.Value[1])
g <- ggplot(plotTab, aes(x=logFC, y=-log10(P.Value), label = name)) +
geom_point(shape = 21, aes(fill = ifSig),size=3) +
geom_hline(yintercept = pCut, linetype = "dashed") +
annotate("text", x = -Inf, y = pCut, label = paste0(fdrCut*100,"% FDR"),
size = 5, vjust = -1.2, hjust=-0.1) +
scale_fill_manual(values = c(n.s. = "grey70",
up = posCol, down = negCol)) +
theme( legend.position = "bottom",
legend.text = element_text(size = 15)) +
ylab(expression(-log[10]*'('*italic(P)~value*')')) +
xlab(x_lab) + ggtitle(plotTitle)
if (ifLabel & is.null(colLabel))
g <- g + ggrepel::geom_text_repel(data = filter(plotTab, ifSig != "n.s."),
size=5, force = 2)
else if (ifLabel & !is.null(colLabel)) {
g <- g+ggrepel::geom_text_repel(data = filter(plotTab, ifSig != "n.s."),
aes_string(col = colLabel),
size=5, force = 2) +
scale_color_manual(values = c(yes = "red",no = "black"))
}
return(g)
}
plotVolcano(filter(resList, Gene == "IGHV.status"), fdrCut =0.01, x_lab="log2FoldChange",
plotTitle = "IGHV.status", ifLabel = TRUE)
Heatmap of differentially expressed proteins
proList <- corRes.sig$id
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- colData(protCLL)[,c("trisomy12","IGHV.status")] %>%
data.frame()
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
Plot top 9 most differentially expressed proteins
protTab <- sumToTiday(protCLL,"patID") %>% mutate(name = hgnc_symbol)
plotTab <- filter(protTab, name %in% corRes.sig$name[1:9]) %>% dplyr::rename(expression = QRILC)
ggplot(plotTab, aes(x=IGHV.status, y = expression)) + geom_boxplot(aes(fill = IGHV.status)) + geom_point() +
facet_wrap(~name, scale = "free")
Enrichment analysis
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG= "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")
inputTab <- resList %>% filter(P.Value <0.05, Gene == "IGHV.status") %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
dplyr::select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$H, "page")
enRes[["KEGG"]] <- runGSEA(inputTab, gmts$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.plot(p)
Trisomy12
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "trisomy12", adj.P.Val < 0.1) %>%
mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()Volcano plot (0.1% FDR)
plotTab <- filter(resList, Gene == "trisomy12") %>%
mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
mutate(onChr12 = ifelse(chromosome == "12","yes","no"))
plotVolcano(plotTab, fdrCut =0.001, x_lab="log2FoldChange",
plotTitle = "trisomy12", ifLabel = TRUE, colLabel = "onChr12")
Labels colored by red indicates the gene is on chromosome 12
Heatmap of differentially expressed proteins (0.1%)
proList <- filter(corRes.sig, !is.na(name),adj.P.Val < 0.001) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- colData(protCLL)[,c("gender","trisomy12","IGHV.status")] %>%
data.frame()
rowAnno <- rowData(protCLL)[proList,c("chromosome_name","hgnc_symbol"),drop=FALSE] %>%
data.frame(stringsAsFactors = FALSE) %>%
mutate(onChr12 = ifelse(chromosome_name == "12","yes","no")) %>%
select(hgnc_symbol, onChr12) %>% data.frame() %>% column_to_rownames("hgnc_symbol")
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
annotation_row = rowAnno,
color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
Plot top 9 most differentially expressed proteins
plotTab <- filter(protTab, name %in% corRes.sig$name[1:9]) %>% dplyr::rename(expression = QRILC) %>%
filter(!is.na(trisomy12))
ggplot(plotTab, aes(x=trisomy12, y = expression)) + geom_boxplot(aes(fill = trisomy12)) + geom_point() +
facet_wrap(~name, scale = "free")
Enrichment analysis
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")
inputTab <- resList %>% filter(P.Value <0.05, Gene == "trisomy12") %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$H, "page")
enRes[["KEGG"]] <- runGSEA(inputTab, gmts$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.#pdf("tri12Enrich.pdf", height = 15, width = 6)
plot(p)
#dev.off()SF3B1
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "SF3B1", adj.P.Val < 0.1) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()Volcano plot
plotVolcano(filter(resList, Gene == "SF3B1"), fdrCut =0.1, x_lab="log2FoldChange",
plotTitle = "SF3B1", ifLabel = TRUE)
Heatmap of differentially expressed proteins
proList <- corRes.sig$id
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- geneMat[,c("trisomy12","IGHV.status","SF3B1")] %>%
data.frame()
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
Plot top 9 most differentially expressed proteins
protTab <- sumToTiday(protCLL,"patID") %>% mutate(name = hgnc_symbol)
plotTab <- filter(protTab, name %in% corRes.sig$name[1:9]) %>% dplyr::rename(expression = QRILC) %>%
mutate(SF3B1 = patMeta[match(colID, patMeta$Patient.ID),]$SF3B1)
ggplot(plotTab, aes(x=SF3B1, y = expression)) + geom_boxplot(aes(fill = SF3B1)) + geom_point() +
facet_wrap(~name, scale = "free")
Enrichment analysis
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG= "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")
inputTab <- resList %>% filter(P.Value <0.05, Gene == "SF3B1") %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
dplyr::select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$H, "page")
enRes[["KEGG"]] <- runGSEA(inputTab, gmts$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.plot(p)
del11q
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "del11q", adj.P.Val < 0.1) %>%
mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()Volcano plot
plotTab <- plotTab <- filter(resList, Gene == "del11q") %>%
mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
mutate(onChr11 = ifelse(chromosome == "11","yes","no"))
plotVolcano(plotTab, fdrCut =0.1, x_lab="log2FoldChange",
plotTitle = "del11q", ifLabel = TRUE, colLabel = "onChr11")
Labels colored by red indicates the gene is on chromosome 11
Heatmap of differentially expressed proteins
proList <- filter(corRes.sig, !is.na(name)) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- geneMat[,c("del11q","IGHV.status")] %>%
data.frame()
colAnno$gender <- protCLL[,rownames(colAnno)]$gender
rowAnno <- rowData(protCLL)[proList, c("chromosome_name","hgnc_symbol"),drop=FALSE] %>% data.frame(stringsAsFactors = FALSE) %>%
mutate(onChr11 = ifelse(chromosome_name == "11","yes","no")) %>%
select(hgnc_symbol, onChr11) %>% data.frame() %>% column_to_rownames("hgnc_symbol")
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, annotation_row = rowAnno,
clustering_method = "ward.D2",
color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
Plot top 9 most differentially expressed proteins
plotTab <- filter(protTab, name %in% corRes.sig$name[1:9]) %>% dplyr::rename(expression = QRILC) %>%
mutate(del11q = patMeta[match(colID, patMeta$Patient.ID),]$del11q) %>%
filter(!is.na(del11q))
ggplot(plotTab, aes(x=del11q, y = expression)) + geom_boxplot(aes(fill = del11q)) + geom_point() +
facet_wrap(~name, scale = "free")
Enrichment analysis
inputTab <- resList %>% filter(P.Value <0.05, Gene == "del11q") %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$H, "page")
enRes[["KEGG"]] <- runGSEA(inputTab, gmts$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.#pdf("tri12Enrich.pdf", height = 15, width = 6)
plot(p)
#dev.off()trisomy19
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "trisomy19", adj.P.Val < 0.1) %>%
mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()Volcano plot
plotTab <- plotTab <- filter(resList, Gene == "trisomy19") %>%
mutate(chromosome = rowData(protCLL[id,])$chromosome_name) %>%
mutate(onChr19 = ifelse(chromosome == "19","yes","no"))
plotVolcano(plotTab, fdrCut =0.1, x_lab="log2FoldChange",
plotTitle = "trisomy19", ifLabel = TRUE, colLabel = "onChr19")
Labels colored by red indicates the gene is on chromosome 19
Heatmap of differentially expressed proteins
proList <- filter(corRes.sig, !is.na(name)) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- geneMat[,c("trisomy19","IGHV.status","trisomy12")] %>%
data.frame()
rowAnno <- rowData(protCLL)[proList, c("chromosome_name","hgnc_symbol"),drop=FALSE] %>% data.frame(stringsAsFactors = FALSE) %>%
mutate(onChr19 = ifelse(chromosome_name == "19","yes","no")) %>%
select(hgnc_symbol, onChr19) %>% data.frame() %>% column_to_rownames("hgnc_symbol")
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, annotation_row = rowAnno,
clustering_method = "ward.D2",
color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
Plot top 9 most differentially expressed proteins
plotTab <- filter(protTab, name %in% corRes.sig$name[1:9]) %>% dplyr::rename(expression = QRILC) %>%
mutate(trisomy19 = patMeta[match(colID, patMeta$Patient.ID),]$trisomy19) %>%
filter(!is.na(trisomy19))
ggplot(plotTab, aes(x=trisomy19, y = expression)) + geom_boxplot(aes(fill = trisomy19)) + geom_point() +
facet_wrap(~name, scale = "free")
Enrichment analysis
inputTab <- resList %>% filter(P.Value <0.05, Gene == "trisomy19") %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$H, "page")
enRes[["KEGG"]] <- runGSEA(inputTab, gmts$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.#pdf("tri12Enrich.pdf", height = 15, width = 6)
plot(p)
#dev.off()
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] forcats_0.4.0 stringr_1.4.0
[3] dplyr_0.8.5 purrr_0.3.3
[5] readr_1.3.1 tidyr_1.0.0
[7] tibble_3.0.0 tidyverse_1.3.0
[9] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[11] BiocParallel_1.18.0 matrixStats_0.54.0
[13] Biobase_2.44.0 GenomicRanges_1.36.0
[15] GenomeInfoDb_1.20.0 IRanges_2.18.1
[17] S4Vectors_0.22.0 BiocGenerics_0.30.0
[19] jyluMisc_0.1.5 pheatmap_1.0.12
[21] piano_2.0.2 proDA_1.1.2
[23] cowplot_0.9.4 ggplot2_3.3.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.4 fastmatch_1.1-0
[4] drc_3.0-1 workflowr_1.6.0 igraph_1.2.4.1
[7] shinydashboard_0.7.1 splines_3.6.0 crosstalk_1.0.0
[10] TH.data_1.0-10 digest_0.6.19 htmltools_0.4.0
[13] fansi_0.4.0 gdata_2.18.0 magrittr_1.5
[16] cluster_2.1.0 openxlsx_4.1.0.1 limma_3.40.2
[19] modelr_0.1.5 sandwich_2.5-1 colorspace_1.4-1
[22] ggrepel_0.8.1 rvest_0.3.5 haven_2.2.0
[25] xfun_0.8 crayon_1.3.4 RCurl_1.95-4.12
[28] jsonlite_1.6 survival_2.44-1.1 zoo_1.8-6
[31] glue_1.3.2 survminer_0.4.4 gtable_0.3.0
[34] zlibbioc_1.30.0 XVector_0.24.0 car_3.0-3
[37] abind_1.4-5 scales_1.1.0 mvtnorm_1.0-11
[40] DBI_1.0.0 relations_0.6-8 Rcpp_1.0.1
[43] plotrix_3.7-6 xtable_1.8-4 cmprsk_2.2-8
[46] foreign_0.8-71 km.ci_0.5-2 DT_0.7
[49] htmlwidgets_1.3 httr_1.4.1 fgsea_1.10.0
[52] gplots_3.0.1.1 RColorBrewer_1.1-2 ellipsis_0.2.0
[55] farver_2.0.3 pkgconfig_2.0.2 dbplyr_1.4.2
[58] utf8_1.1.4 labeling_0.3 tidyselect_1.0.0
[61] rlang_0.4.5 later_0.8.0 munsell_0.5.0
[64] cellranger_1.1.0 tools_3.6.0 visNetwork_2.0.7
[67] cli_1.1.0 generics_0.0.2 broom_0.5.2
[70] evaluate_0.14 yaml_2.2.0 knitr_1.23
[73] fs_1.4.0 zip_2.0.2 survMisc_0.5.5
[76] caTools_1.17.1.2 nlme_3.1-140 mime_0.7
[79] slam_0.1-45 xml2_1.2.2 rstudioapi_0.10
[82] compiler_3.6.0 curl_3.3 ggsignif_0.5.0
[85] marray_1.62.0 reprex_0.3.0 stringi_1.4.3
[88] lattice_0.20-38 Matrix_1.2-17 shinyjs_1.0
[91] KMsurv_0.1-5 vctrs_0.2.4 pillar_1.4.3
[94] lifecycle_0.2.0 data.table_1.12.2 bitops_1.0-6
[97] httpuv_1.5.1 R6_2.4.0 promises_1.0.1
[100] KernSmooth_2.23-15 gridExtra_2.3 rio_0.5.16
[103] codetools_0.2-16 MASS_7.3-51.4 gtools_3.8.1
[106] exactRankTests_0.8-30 assertthat_0.2.1 rprojroot_1.3-2
[109] withr_2.1.2 multcomp_1.4-10 GenomeInfoDbData_1.2.1
[112] hms_0.5.2 grid_3.6.0 rmarkdown_1.13
[115] carData_3.0-2 git2r_0.26.1 maxstat_0.7-25
[118] ggpubr_0.2.1 sets_1.0-18 shiny_1.3.2
[121] lubridate_1.7.4