Correlate proteomic profiling with genomic background (new timsTOF data)
Junyan Lu
2020-08-06
Last updated: 2020-08-06
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),]$trisomy12Prepare genomic background
Get Mutations with at least 5 cases
geneMat <- patMeta[match(colnames(protMat), patMeta$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))>=5]Mutations that will be tested
colnames(geneMat) [1] "IGHV.status" "del11q" "del13q" "del17p" "trisomy12"
[6] "trisomy19" "DDX3X" "EGR2" "NOTCH1" "SF3B1"
[11] "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$p.value)
}) %>% bind_rows()
}) %>% bind_rows() %>% arrange(p) %>%
filter(p <=0.05)
chiRes# A tibble: 7 x 3
geneA geneB p
<chr> <chr> <dbl>
1 DDX3X EGR2 0.0104
2 trisomy12 trisomy19 0.0107
3 del17p TP53 0.0156
4 del13q trisomy12 0.0216
5 IGHV.status trisomy19 0.0223
6 IGHV.status del11q 0.0232
7 IGHV.status DDX3X 0.0496For IGHV and trisomy12
Fit the probailistic dropout model
designMat <- geneMat[,c("IGHV.status","trisomy12")]
fit <- proDA(protMat, design = ~ .,
col_data = designMat)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
contra <- paste0(n,"1")
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
}) %>% bind_rows()Test for other variantions (blocking for IGHV and trisomy12)
Fit the probailistic dropout model and test for differentially expressed proteins
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
designMat <- geneMat[,c("IGHV.status","trisomy12",n)]
designMat <- designMat[!is.na(designMat[[n]]),]
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,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()Combine the results
resList <- bind_rows(resList.ighvTri12, resList)P-value histogram for each genomic feature
ggplot(resList, aes(x=P.Value)) + geom_histogram(fill = "green", alpha =0.5, bins=30, col = "grey50") + facet_wrap(~Gene, ncol=3, scales = "free") + xlim(0,1)Warning: Removed 22 rows containing missing values (geom_bar).
Number of significantly associated proteins at 10% FDR
proNumTab <- resList %>% group_by(Gene) %>%
summarise(number = sum(adj.P.Val < 0.1, na.rm=TRUE)) %>%
arrange(desc(number)) %>% mutate(Gene = factor(Gene, levels = Gene))`summarise()` ungrouping output (override with `.groups` argument)proNumTab# A tibble: 11 x 2
Gene number
<fct> <int>
1 trisomy12 1106
2 IGHV.status 514
3 SF3B1 28
4 del11q 18
5 trisomy19 8
6 del17p 5
7 DDX3X 0
8 del13q 0
9 EGR2 0
10 NOTCH1 0
11 TP53 0Based on the P-value histograms and numbers of significant associations, trisomy12 has the most impact on protein expression, followed by IGHV and del11q. Other genomic variations do not seem to have major impact on protein expression.
Visualize results
IGHV status
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "IGHV.status", adj.P.Val < 0.1) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()Volcano plot
plotVolcano <- function(pTab, fdrCut = 0.05, posCol = "red", negCol = "blue",
x_lab = "dm", plotTitle = "",ifLabel = FALSE,
colLabel = NULL) {
plotTab <- pTab %>% mutate(ifSig = ifelse(adj.P.Val > fdrCut, "n.s.",
ifelse(logFC > 0, "up","down"))) %>%
mutate(ifSig = factor(ifSig, levels = c("up","down","n.s.")))
pCut <- -log10((filter(plotTab, ifSig != "n.s.") %>% arrange(desc(P.Value)))$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)) %>%
distinct(name, .keep_all = TRUE) %>%
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,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
dplyr::select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$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,])$hgnc_symbol
colAnno <- geneMat[,c("trisomy19","IGHV.status","trisomy12")] %>%
data.frame()
colAnno$gender <- protCLL[,rownames(colAnno)]$gender
rowAnno <- rowData(protCLL)[proList, c("chromosome_name","hgnc_symbol"),drop=FALSE] %>% data.frame(stringsAsFactors = FALSE) %>%
mutate(onChr19 = ifelse(chromosome_name == "19","yes","no")) %>%
select(hgnc_symbol, onChr19) %>% data.frame() %>% column_to_rownames("hgnc_symbol")
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, annotation_row = rowAnno,
clustering_method = "ward.D2",
color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
Plot top 9 most differentially expressed proteins
plotTab <- filter(protTab, name %in% corRes.sig$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 10.15.6
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_0.5.0 stringr_1.4.0
[3] dplyr_1.0.0 purrr_0.3.4
[5] readr_1.3.1 tidyr_1.1.0
[7] tibble_3.0.3 ggplot2_3.3.2
[9] tidyverse_1.3.0 SummarizedExperiment_1.16.1
[11] DelayedArray_0.12.3 BiocParallel_1.20.1
[13] matrixStats_0.56.0 Biobase_2.46.0
[15] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[17] IRanges_2.20.2 S4Vectors_0.24.4
[19] BiocGenerics_0.32.0 jyluMisc_0.1.5
[21] pheatmap_1.0.12 piano_2.2.0
[23] proDA_1.1.2 cowplot_1.0.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.8 fastmatch_1.1-0
[4] drc_3.0-1 workflowr_1.6.2 igraph_1.2.5
[7] shinydashboard_0.7.1 splines_3.6.0 crosstalk_1.1.0.1
[10] TH.data_1.0-10 digest_0.6.25 htmltools_0.5.0
[13] fansi_0.4.1 gdata_2.18.0 magrittr_1.5
[16] cluster_2.1.0 openxlsx_4.1.5 limma_3.42.2
[19] modelr_0.1.8 sandwich_2.5-1 colorspace_1.4-1
[22] ggrepel_0.8.2 rvest_0.3.5 blob_1.2.1
[25] haven_2.3.1 xfun_0.15 crayon_1.3.4
[28] RCurl_1.98-1.2 jsonlite_1.7.0 survival_3.2-3
[31] zoo_1.8-8 glue_1.4.1 survminer_0.4.7
[34] gtable_0.3.0 zlibbioc_1.32.0 XVector_0.26.0
[37] car_3.0-8 abind_1.4-5 scales_1.1.1
[40] mvtnorm_1.1-1 DBI_1.1.0 relations_0.6-9
[43] rstatix_0.6.0 Rcpp_1.0.5 plotrix_3.7-8
[46] xtable_1.8-4 foreign_0.8-71 km.ci_0.5-2
[49] DT_0.14 htmlwidgets_1.5.1 httr_1.4.1
[52] fgsea_1.12.0 gplots_3.0.4 RColorBrewer_1.1-2
[55] ellipsis_0.3.1 farver_2.0.3 pkgconfig_2.0.3
[58] dbplyr_1.4.4 utf8_1.1.4 labeling_0.3
[61] tidyselect_1.1.0 rlang_0.4.7 later_1.1.0.1
[64] munsell_0.5.0 cellranger_1.1.0 tools_3.6.0
[67] visNetwork_2.0.9 cli_2.0.2 generics_0.0.2
[70] broom_0.7.0 evaluate_0.14 fastmap_1.0.1
[73] yaml_2.2.1 knitr_1.29 fs_1.4.2
[76] zip_2.0.4 survMisc_0.5.5 caTools_1.18.0
[79] mime_0.9 slam_0.1-47 xml2_1.3.2
[82] compiler_3.6.0 rstudioapi_0.11 curl_4.3
[85] ggsignif_0.6.0 marray_1.64.0 reprex_0.3.0
[88] stringi_1.4.6 lattice_0.20-41 Matrix_1.2-18
[91] shinyjs_1.1 KMsurv_0.1-5 vctrs_0.3.1
[94] pillar_1.4.6 lifecycle_0.2.0 data.table_1.12.8
[97] bitops_1.0-6 httpuv_1.5.4 R6_2.4.1
[100] promises_1.1.1 KernSmooth_2.23-17 gridExtra_2.3
[103] rio_0.5.16 codetools_0.2-16 MASS_7.3-51.6
[106] gtools_3.8.2 exactRankTests_0.8-31 assertthat_0.2.1
[109] rprojroot_1.3-2 withr_2.2.0 multcomp_1.4-13
[112] GenomeInfoDbData_1.2.2 hms_0.5.3 grid_3.6.0
[115] rmarkdown_2.3 carData_3.0-4 git2r_0.27.1
[118] maxstat_0.7-25 ggpubr_0.4.0 sets_1.0-18
[121] shiny_1.5.0 lubridate_1.7.9