Last updated: 2020-08-06
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Knit directory: Proteomics/analysis/
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protMat <- assays(protCLL)[["count"]] #without imputation
protCLL$trisomy12 <- patMeta[match(colnames(protCLL),patMeta$Patient.ID),]$trisomy12
geneMat <- patMeta[match(colnames(protMat), patMeta$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"
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$p.value)
}) %>% bind_rows()
}) %>% bind_rows() %>% arrange(p) %>%
filter(p <=0.05)
chiRes
# A tibble: 7 x 3
geneA geneB p
<chr> <chr> <dbl>
1 DDX3X EGR2 0.0104
2 trisomy12 trisomy19 0.0107
3 del17p TP53 0.0156
4 del13q trisomy12 0.0216
5 IGHV.status trisomy19 0.0223
6 IGHV.status del11q 0.0232
7 IGHV.status DDX3X 0.0496
Fit the probailistic dropout model
designMat <- geneMat[,c("IGHV.status","trisomy12")]
fit <- proDA(protMat, design = ~ .,
col_data = designMat)
Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
contra <- paste0(n,"1")
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
}) %>% bind_rows()
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,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()
Combine the results
resList <- bind_rows(resList.ighvTri12, resList)
ggplot(resList, aes(x=P.Value)) + geom_histogram(fill = "green", alpha =0.5, bins=30, col = "grey50") + facet_wrap(~Gene, ncol=3, scales = "free") + xlim(0,1)
Warning: Removed 22 rows containing missing values (geom_bar).
Number of significantly associated proteins at 10% FDR
proNumTab <- resList %>% group_by(Gene) %>%
summarise(number = sum(adj.P.Val < 0.1, na.rm=TRUE)) %>%
arrange(desc(number)) %>% mutate(Gene = factor(Gene, levels = Gene))
`summarise()` ungrouping output (override with `.groups` argument)
proNumTab
# A tibble: 11 x 2
Gene number
<fct> <int>
1 trisomy12 849
2 IGHV.status 388
3 del11q 8
4 SF3B1 3
5 DDX3X 0
6 del13q 0
7 del17p 0
8 EGR2 0
9 NOTCH1 0
10 TP53 0
11 trisomy19 0
Based on the P-value histograms and numbers of significant associations, trisomy12 has the most impact on protein expression, followed by IGHV and del11q. Other genomic variations do not seem to have major impact on protein expression.
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "IGHV.status", adj.P.Val < 0.1) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()
Volcano plot
plotVolcano <- function(pTab, fdrCut = 0.05, posCol = "red", negCol = "blue",
x_lab = "dm", plotTitle = "",ifLabel = FALSE,
colLabel = NULL) {
plotTab <- pTab %>% mutate(ifSig = ifelse(adj.P.Val > fdrCut, "n.s.",
ifelse(logFC > 0, "up","down"))) %>%
mutate(ifSig = factor(ifSig, levels = c("up","down","n.s.")))
pCut <- -log10((filter(plotTab, ifSig != "n.s.") %>% arrange(desc(P.Value)))$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)
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$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)
Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.
#pdf("tri12Enrich.pdf", height = 15, width = 6)
plot(p)
#dev.off()
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "SF3B1", adj.P.Val < 0.1) %>%
select(-Gene)
corRes.sig %>% mutate_if(is.numeric, formatC, digits=2, format="e") %>% DT::datatable()
Volcano plot
plotVolcano(filter(resList, Gene == "SF3B1"), fdrCut =0.1, x_lab="log2FoldChange",
plotTitle = "SF3B1", ifLabel = TRUE)
Heatmap of differentially expressed proteins
proList <- corRes.sig$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$KEGG, "page")
p <- plotEnrichmentBar(enRes, pCut =0.05, ifFDR= FALSE)
Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.
plot(p)
List of significant proteins (10% FDR)
corRes.sig <- resList %>% filter(Gene == "del11q", adj.P.Val < 0.1) %>%
mutate(chromosome = rowData(protCLL[id,])$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()
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")
Warning: Removed 1 rows containing missing values (geom_hline).
Warning: Removed 1 rows containing missing values (geom_text).
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)
#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