Section 2: Identifying proteins associated disease drivers in CLL
Junyan Lu
2020-10-09
Last updated: 2021-03-15
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Knit directory: CLLproteomics_batch13/analysis/
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Load packages and datasets
library(limma)
library(DESeq2)
library(proDA)
library(IHW)
library(SummarizedExperiment)
library(tidyverse)
#load datasets
load("../data/patMeta_enc.RData")
load("../data/ddsrna_enc.RData")
load("../data/proteomic_explore_enc.RData")
source("../code/utils.R")
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,dev = c("png","pdf"))
#protCLL <- protCLL[,colnames(protCLL) %in% patMeta$Patient.ID]Preprocessing
Process proteomics data
#protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
protMat <- assays(protCLL)[["count"]] #without imputation
protMatLog <- assays(protCLL)[["log2Norm"]]Prepare genomic background
Get mutations with at least 5 cases
geneMat <- patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11q:U1) %>%
mutate_if(is.factor, as.character) %>% mutate(IGHV.status = ifelse(IGHV.status == "M", 1,0)) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #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" "ATM" "BRAF" "DDX3X" "EGR2"
[11] "MED12" "NOTCH1" "SF3B1" "TP53" Differential protein expression using proDA
For IGHV and trisomy12
Fit the probailistic dropout model
designMat <- geneMat[ ,c("IGHV.status","trisomy12")]
designMat[,"batch"] <- factor(protCLL[,rownames(designMat)]$batch)
fit <- proDA(protMat, design = ~ . ,
col_data = designMat)Limma for calculating log2 fold change
lmDesign <- model.matrix(~., designMat)
lmFit <- lmFit(protMatLog, design = lmDesign)
fit2 <- eBayes(lmFit)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val, n_obs) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
foldTab <- topTable(fit2, coef = n, number = "all") %>%
as_tibble(rownames = "id") %>% select(id, logFC) %>%
dplyr::rename(log2FC = logFC)
resTab <- left_join(resTab, foldTab, by = "id")
resTab
}) %>% 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[,"batch"] <- factor(protCLL[,rownames(designMat)]$batch)
designMat <- designMat[!is.na(designMat[[n]]),]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val, n_obs) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
#calculte log2 fold change
lmDesign <- model.matrix(~., designMat)
protMatTest <- protMatLog[,rownames(lmDesign)]
lmFit <- lmFit(protMatTest, design = lmDesign)
fit2 <- eBayes(lmFit)
foldTab <- topTable(fit2, coef = n, number = "all") %>%
as_tibble(rownames = "id") %>% select(id, logFC) %>%
dplyr::rename(log2FC = logFC)
resTab <- left_join(resTab, foldTab, by = "id")
resTab
}) %>% bind_rows()Combine the results
resList <- bind_rows(resList.ighvTri12, resList)
#Adjusting p values
#using BH
resList <- mutate(resList, adj.P.global = p.adjust(P.Value, method = "BH"))
#using IHW
ihwRes <- ihw(P.Value ~ factor(Gene), data= resList, alpha=0.1)
resList <- mutate(resList, adj.P.IHW = adj_pvalues(ihwRes))Calculate Q value for all tests
qobj <- qvalue(resList$P.Value)
resList$Q.Value.global <- qobj$qvaluesCalcualte Q values for test in individual genes
resListNew <- lapply(unique(resList$Gene), function(gn) {
eachTab <- filter(resList, Gene == gn)
qobj <- qvalue(eachTab$P.Value)
eachTab$Q.Value.local <- qobj$qvalues
eachTab
}) %>% bind_rows()
resList <- resListNewSave the results for re-using
save(resList, file = "../output/deResList.RData")Load the pre-calculated results (differential expression tests take long time.)
load("../output/deResList.RData")Summarise significant associations
Bar plot of number of significant associations (10% FDR)
fdrCut = 0.1
plotTab <- resList %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= fdrCut),
nFDR.global = sum(adj.P.global <= fdrCut),
nFDR.IHW = sum(adj.P.IHW <= fdrCut),
nFDR.Q.global = sum(Q.Value.global <=fdrCut),
nFDR.Q.local = sum(Q.Value.local < fdrCut),
nP = sum(P.Value < 0.05))P values adjusted for each variant
#local adjusted P-values
plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.local)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(10% FDR)") + xlab("")
P-values adjusted for all tests together
#Global adjusted P-values
plotTab <- arrange(plotTab, desc(nFDR.global)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.global)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.global)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(10% FDR)") + xlab("")
Q values for all test
#IHW adjusted p-values
plotTab <- arrange(plotTab, desc(nFDR.Q.global)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.Q.global)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.Q.global)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(10% FDR)") + xlab("")
Q values for individual genes
#IHW adjusted p-values
plotTab <- arrange(plotTab, desc(nFDR.Q.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.Q.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.Q.local)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(10% FDR)") + xlab("")
IHW adjusted p-values
#IHW adjusted p-values
plotTab <- arrange(plotTab, desc(nFDR.IHW)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.IHW)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.IHW)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(10% FDR)") + xlab("")
Bar plot of number of significant associations (5% FDR)
fdrCut = 0.05
plotTab <- resList %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= fdrCut),
nFDR.global = sum(adj.P.global <= fdrCut),
nFDR.IHW = sum(adj.P.IHW <= fdrCut),
nFDR.Q.global = sum(Q.Value.global <=fdrCut),
nFDR.Q.local = sum(Q.Value.local < fdrCut),
nP = sum(P.Value < 0.05))P values adjusted for each variant
#local adjusted P-values
plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.local)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
P-values adjusted for all tests together
#Global adjusted P-values
plotTab <- arrange(plotTab, desc(nFDR.global)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.global)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.global)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
Q values for all test
#IHW adjusted p-values
plotTab <- arrange(plotTab, desc(nFDR.Q.global)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.Q.global)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.Q.global)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
Q values for individual genes
#IHW adjusted p-values
plotTab <- arrange(plotTab, desc(nFDR.Q.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.Q.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.Q.local)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
IHW adjusted p-values
#IHW adjusted p-values
plotTab <- arrange(plotTab, desc(nFDR.IHW)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.IHW)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.IHW)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
Bar plot of number of raw P-value < 0.05
plotTab <- arrange(plotTab, desc(nP)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nP)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nP)),vjust=-1,col=colList[1]) + ylim(0,1500) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(nominal P-value < 0.05)") + xlab("")
Stratified P-value histogram
ggplot(resList, aes(x=P.Value)) + geom_histogram() +
facet_wrap(~Gene)
Differential protein expression using proDA (include WBC count as covariate)
For IGHV and trisomy12
Fit the probailistic dropout model
designMat <- geneMat[ ,c("IGHV.status","trisomy12")]
designMat[,"batch"] <- factor(protCLL[,rownames(designMat)]$batch)
designMat[,"LeukCount"] <- log10(sampleTab[match(rownames(designMat), sampleTab$encID),]$leukCount)
fit <- proDA(protMat, design = ~ . ,
col_data = designMat)Limma for calculating log2 fold change
lmDesign <- model.matrix(~., designMat)
lmFit <- lmFit(protMatLog, design = lmDesign)
fit2 <- eBayes(lmFit)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
contra <- n
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,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val, n_obs) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
foldTab <- topTable(fit2, coef = n, number = "all") %>%
as_tibble(rownames = "id") %>% select(id, logFC) %>%
dplyr::rename(log2FC = logFC)
resTab <- left_join(resTab, foldTab, by = "id")
resTab
}) %>% bind_rows()Test for other variantions (blocking for IGHV and trisomy12)
Fit the probailistic dropout model and test for differentially expressed proteins
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
designMat <- geneMat[,c("IGHV.status","trisomy12",n)]
designMat[,"batch"] <- factor(protCLL[,rownames(designMat)]$batch)
designMat[,"LeukCount"] <- log10(sampleTab[match(rownames(designMat), sampleTab$encID),]$leukCount)
designMat <- designMat[!is.na(designMat[[n]]),]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- n
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,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val, n_obs) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
#calculte log2 fold change
lmDesign <- model.matrix(~., designMat)
protMatTest <- protMatLog[,rownames(lmDesign)]
lmFit <- lmFit(protMatTest, design = lmDesign)
fit2 <- eBayes(lmFit)
foldTab <- topTable(fit2, coef = n, number = "all") %>%
as_tibble(rownames = "id") %>% select(id, logFC) %>%
dplyr::rename(log2FC = logFC)
resTab <- left_join(resTab, foldTab, by = "id")
resTab
}) %>% bind_rows()Combine the results
resList_WBC <- bind_rows(resList.ighvTri12, resList)Save the results for re-using
save(resList_WBC, file = "../output/deResList_WBC.RData")Load the pre-calculated results (differential expression tests take long time.)
load("../output/deResList_WBC.RData")
load("../output/deResList.RData")Compare results with and without blocking for WBC
tabNoBlock <- resList %>% mutate(ifSig = adj.P.Val <= 0.05) %>%
select(id, Gene, P.Value, ifSig)
tabBlock <- resList_WBC %>% mutate(ifSig.block = adj.P.Val < 0.05, P.Value.block = P.Value) %>%
select(id, Gene, P.Value.block, ifSig.block)
compareTab <- left_join(tabNoBlock, tabBlock, by = c("id","Gene")) %>%
mutate(group = case_when(
ifSig & ifSig.block ~ "significant in both",
ifSig & !ifSig.block ~ "significant only without blocking",
!ifSig & ifSig.block ~ "significant only with blocking",
TRUE ~ "not significant in both"
))
ggplot(compareTab, aes(x=-log10(P.Value), y=-log10(P.Value.block), col = group)) +
geom_point(alpha=0.5) +
xlab("-log10(P value) without blocking") + ylab("-log10 (P value) with blocking for WBC counts") +
scale_color_manual(values = c("grey80", colList[2], colList[1], colList[3]), name = "") +
geom_abline(slope = 1, linetype ="dashed") +
ggtitle("Associations with genomics") +
theme_full +
theme(legend.position = c(0.7,0.2))
Number of associations in each catagory
tt <- table(compareTab$group)
tt
not significant in both significant in both
49616 3106
significant only with blocking significant only without blocking
58 244 A percentage of consistent results
(tt[1] + tt[2])/sum(tt)*100not significant in both
99.43045 Associated the RNA expression with genomics using the same method
Prepare RNA seq data
Subset for samples with proteomics
ddsSub <- dds[,dds$PatID %in% colnames(protCLL)]
#how many samples?
ncol(ddsSub)[1] 82Subset for genes detected at protein level
ddsSub <- ddsSub[rownames(ddsSub) %in% rowData(protCLL)$ensembl_gene_id,]
#how many genes without any RNA expression detected?
table(rowSums(counts(ddsSub)) > 0)
FALSE TRUE
12 3291 ddsSub <- ddsSub[rowSums(counts(ddsSub)) > 0, ]12 genes were not detected at RNA level, they are removed from anaysis
How many gene and samples left?
dim(ddsSub)[1] 3291 82Differential RNA expression using DESeq
colData(ddsSub) <- cbind(colData(ddsSub), geneMat[colnames(ddsSub),])For IGHV and trisomy12
design(ddsSub) <- ~ IGHV.status + trisomy12
deRes <- DESeq(ddsSub)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
resTab <- results(deRes, name = n, tidy = TRUE) %>%
dplyr::rename(id = row, log2FC = log2FoldChange, t=stat,
P.Value = pvalue, adj.P.Val = padj) %>%
mutate(name = rowData(ddsSub[id,])$symbol) %>%
select(name, id, log2FC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
}) %>% bind_rows()Test for other variantions (blocking for IGHV and trisomy12)
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
ddsTest <- ddsSub[,!is.na(ddsSub[[n]])]
design(ddsTest) <- as.formula(paste0("~ IGHV.status + trisomy12 + ",n))
deRes <- DESeq(ddsTest)
resTab <- results(deRes, name = n, tidy = TRUE) %>%
dplyr::rename(id = row, log2FC = log2FoldChange, t=stat,
P.Value = pvalue, adj.P.Val = padj) %>%
mutate(name = rowData(ddsSub[id,])$symbol) %>%
select(name, id, log2FC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()Combine the results
resListRNA <- bind_rows(resList.ighvTri12, resList)Save the results for re-using
save(resListRNA, file = "../output/deResListRNA.RData")Load the pre-calculated results (differential expression tests take long time.)
load("../output/deResListRNA.RData")Bar plot of number of significant associations (5% FDR)
fdrCut = 0.05
plotTab <- resListRNA %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= fdrCut, na.rm=TRUE),
nP = sum(P.Value < 0.05))P values adjusted for each variant
#local adjusted P-values
plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.local)),vjust=-1,col=colList[1]) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] latex2exp_0.4.0 forcats_0.5.1
[3] stringr_1.4.0 dplyr_1.0.5
[5] purrr_0.3.4 readr_1.4.0
[7] tidyr_1.1.3 tibble_3.1.0
[9] ggplot2_3.3.3 tidyverse_1.3.0
[11] IHW_1.16.0 proDA_1.2.0
[13] DESeq2_1.28.1 SummarizedExperiment_1.18.2
[15] DelayedArray_0.14.1 matrixStats_0.58.0
[17] Biobase_2.48.0 GenomicRanges_1.40.0
[19] GenomeInfoDb_1.24.2 IRanges_2.22.2
[21] S4Vectors_0.26.1 BiocGenerics_0.34.0
[23] limma_3.44.3
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ellipsis_0.3.1 rprojroot_2.0.2
[4] XVector_0.28.0 fs_1.5.0 rstudioapi_0.13
[7] farver_2.1.0 bit64_4.0.5 AnnotationDbi_1.50.3
[10] fansi_0.4.2 lubridate_1.7.10 xml2_1.3.2
[13] splines_4.0.2 cachem_1.0.4 geneplotter_1.66.0
[16] knitr_1.31 jsonlite_1.7.2 workflowr_1.6.2
[19] broom_0.7.5 annotate_1.66.0 dbplyr_2.1.0
[22] compiler_4.0.2 httr_1.4.2 backports_1.2.1
[25] assertthat_0.2.1 Matrix_1.3-2 fastmap_1.1.0
[28] cli_2.3.1 later_1.1.0.1 htmltools_0.5.1.1
[31] tools_4.0.2 gtable_0.3.0 glue_1.4.2
[34] GenomeInfoDbData_1.2.3 Rcpp_1.0.6 slam_0.1-48
[37] cellranger_1.1.0 jquerylib_0.1.3 vctrs_0.3.6
[40] xfun_0.21 rvest_1.0.0 lifecycle_1.0.0
[43] XML_3.99-0.5 zlibbioc_1.34.0 scales_1.1.1
[46] hms_1.0.0 promises_1.2.0.1 RColorBrewer_1.1-2
[49] yaml_2.2.1 memoise_2.0.0 sass_0.3.1
[52] stringi_1.5.3 RSQLite_2.2.3 highr_0.8
[55] genefilter_1.70.0 BiocParallel_1.22.0 rlang_0.4.10
[58] pkgconfig_2.0.3 bitops_1.0-6 evaluate_0.14
[61] lattice_0.20-41 lpsymphony_1.16.0 labeling_0.4.2
[64] bit_4.0.4 tidyselect_1.1.0 magrittr_2.0.1
[67] R6_2.5.0 generics_0.1.0 DBI_1.1.1
[70] pillar_1.5.1 haven_2.3.1 withr_2.4.1
[73] survival_3.2-7 RCurl_1.98-1.2 modelr_0.1.8
[76] crayon_1.4.1 fdrtool_1.2.16 utf8_1.1.4
[79] rmarkdown_2.7 locfit_1.5-9.4 grid_4.0.2
[82] readxl_1.3.1 blob_1.2.1 git2r_0.28.0
[85] reprex_1.0.0 digest_0.6.27 xtable_1.8-4
[88] httpuv_1.5.5 munsell_0.5.0 bslib_0.2.4