Protein markers for drug responses (IC50 screen)
Junyan Lu
2020-06-08
Last updated: 2020-06-09
Checks: 5 2
Knit directory: Proteomics/analysis/
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Correlations between protein abundance and drug response
Preprocess datasets
viabMat.auc <- ic50 %>% filter(! Drug %in% c("DMSO","PBS"), patientID %in% colnames(protCLL)) %>%
group_by(patientID, Drug) %>% summarise(viab = mean(normVal_auc)) %>%
spread(key = patientID, value = viab) %>%
data.frame(stringsAsFactors = FALSE) %>% column_to_rownames("Drug") %>%
as.matrix()
viabMat <- ic50 %>% filter(! Drug %in% c("DMSO","PBS"), patientID %in% colnames(protCLL)) %>%
group_by(patientID, Drug, concIndex) %>% summarise(viab = mean(normVal)) %>% ungroup() %>%
spread(key = patientID, value = viab) %>%
mutate(drugConc = paste0(Drug, "_",concIndex)) %>% select(-Drug, -concIndex) %>%
data.frame(stringsAsFactors = FALSE) %>% column_to_rownames("drugConc") %>%
as.matrix()Proteomics data
proMat <- assays(protCLL)[["count"]]
proMat <- proMat[,colnames(viabMat)]Remove proteins without much variance (to lower multi-testing burden)
sds <- genefilter::rowSds(proMat,na.rm=TRUE)
proMat <- proMat[sds > genefilter::shorth(sds),]Remove drugs without much variance (only for individual concentrations)
#individual concentrations
sds <- genefilter::rowSds(viabMat)
viabMat <- viabMat[sds > genefilter::shorth(sds),]How many samples have both proteomics data and ic50 screen data
ncol(proMat)[1] 32Association test without any blocking
For individual concentrations
Association test
resTab <- lapply(rownames(viabMat),function(drugName) {
viab <- viabMat[drugName, ]
designMat <- model.matrix(~1+viab)
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "viab") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol, drugConc = drugName)
}) %>% bind_rows() %>% separate(drugConc, c("Drug","concIndex"),"_",remove = FALSE) %>%
mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>% arrange(P.Value)Number of associations (25% FDR)
Here I use an FDR cut-off of 25%, because if I use 10%, there will be only 2 associations.
Select significant associations
resTab.sig <- filter(resTab, adj.P.Val < 0.25) %>%
select(Drug, symbol, id,logFC, P.Value, adj.P.Val, concIndex)plotTab <- resTab.sig %>% group_by(Drug, concIndex) %>%
summarise(n = length(id)) %>% ungroup()
ordTab <- group_by(plotTab, Drug) %>% summarise(total = sum(n)) %>%
arrange(desc(total))
plotTab <- mutate(plotTab, Drug = factor(Drug, levels = ordTab$Drug)) %>%
filter(n>0)
ggplot(plotTab, aes(x=Drug,y=n,fill = concIndex)) + geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust=1,vjust=0.5)) +
ylab("Number of associations") + xlab("")
Table of significant associations
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Plot top 9 drug-protein associations
plotList <- lapply(seq(9), function(i) {
drugConc <- paste0(resTab.sig$Drug[i],"_",resTab.sig$concIndex[i])
proteinName <- resTab.sig$symbol[i]
id <- resTab.sig$id[i]
plotTab <- tibble(patID = colnames(viabMat),
viab = viabMat[drugConc,],
expr = proMat[id,]) %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12)
ggplot(plotTab, aes(x=viab, y=expr)) + geom_point(aes(col = trisomy12, shape = IGHV)) +
scale_shape_manual(values = c(M = 19, U = 1)) + geom_smooth(method = "lm", se=FALSE) +
ggtitle(sprintf("%s ~ %s", drugConc, proteinName)) +
ylab("Protein expression") + xlab("Viability after treatment") +
theme(legend.position = "bottom")
})
plot_grid(plotlist = plotList, ncol =3)
From those plots, it can be seen that those associations are potentially confounded by IGHV status and/or trisomy12.
For averaged five concentrations (AUC)
Association test
resTab.auc <- lapply(rownames(viabMat.auc),function(drugName) {
viab <- viabMat.auc[drugName, ]
designMat <- model.matrix(~1+viab)
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "viab") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol, Drug = drugName)
}) %>% bind_rows() %>% mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>% arrange(P.Value)Number of associations (25% FDR)
Select significant associations (25% FDR)
resTab.sig <- filter(resTab.auc, adj.P.Val < 0.25) %>%
select(Drug, symbol, id,logFC, P.Value, adj.P.Val)plotTab <- resTab.sig %>% group_by(Drug) %>%
summarise(n = length(id)) %>% ungroup()
ordTab <- group_by(plotTab, Drug) %>% summarise(total = sum(n)) %>%
arrange(desc(total))
plotTab <- mutate(plotTab, Drug = factor(Drug, levels = ordTab$Drug)) %>%
filter(n>0)
ggplot(plotTab, aes(x=Drug,y=n)) + geom_bar(stat = "identity", fill = "lightblue") +
theme(axis.text.x = element_text(angle = 90,hjust=1,vjust=0.5)) +
ylab("Number of associations") + xlab("")
Table of significant associations
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Plot top 9 drug-protein associations
plotList <- lapply(seq(9), function(i) {
drugConc <- resTab.sig$Drug[i]
proteinName <- resTab.sig$symbol[i]
id <- resTab.sig$id[i]
plotTab <- tibble(patID = colnames(viabMat.auc),
viab = viabMat.auc[drugConc,],
expr = proMat[id,]) %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12)
ggplot(plotTab, aes(x=viab, y=expr)) + geom_point(aes(col = trisomy12, shape = IGHV)) +
scale_shape_manual(values = c(M = 19, U = 1)) + geom_smooth(method = "lm", se=FALSE) +
ggtitle(sprintf("%s ~ %s", drugConc, proteinName)) +
ylab("Protein expression") + xlab("Viability after treatment") +
theme(legend.position = "bottom")
})
plot_grid(plotlist = plotList, ncol =3)
Association test with blocking for IGHV and trisomy12
For individual concentrations
Association test
testList <- filter(resTab, adj.P.Val < 0.25)
resTab.block <- lapply(seq(nrow(testList)),function(i) {
pair <- testList[i,]
expr <- proMat[pair$id,]
viab <- viabMat[pair$drugConc, ]
ighv <- protCLL[,colnames(viabMat)]$IGHV.status
tri12 <- protCLL[,colnames(viabMat)]$trisomy12
res <- anova(lm(viab~ighv+tri12+expr))
data.frame(id = pair$id, P.Value = res["expr",]$`Pr(>F)`, symbol = pair$symbol,
drugConc = pair$drugConc, Drug = pair$Drug, concIndex = pair$concIndex,
P.Value.IGHV = res["ighv",]$`Pr(>F)`,P.Value.trisomy12 = res["tri12",]$`Pr(>F)`,
P.Value.noBlock = pair$P.Value,
stringsAsFactors = FALSE)
}) %>% bind_rows() %>% mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>% arrange(P.Value)Number of associations (10% FDR)
Select significant associations
resTab.sig <- filter(resTab.block, adj.P.Val < 0.1) %>%
select(Drug, symbol, id, P.Value, adj.P.Val, P.Value.trisomy12, P.Value.IGHV, concIndex)plotTab <- resTab.sig %>% group_by(Drug, concIndex) %>%
summarise(n = length(id)) %>% ungroup()
ordTab <- group_by(plotTab, Drug) %>% summarise(total = sum(n)) %>%
arrange(desc(total))
plotTab <- mutate(plotTab, Drug = factor(Drug, levels = ordTab$Drug)) %>%
filter(n>0)
ggplot(plotTab, aes(x=Drug,y=n,fill = concIndex)) + geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust=1,vjust=0.5)) +
ylab("Number of associations") + xlab("")
Table of significant associations
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Plot top 9 drug-protein associations
plotList <- lapply(seq(9), function(i) {
drugConc <- paste0(resTab.sig$Drug[i],"_",resTab.sig$concIndex[i])
proteinName <- resTab.sig$symbol[i]
id <- resTab.sig$id[i]
plotTab <- tibble(patID = colnames(viabMat),
viab = viabMat[drugConc,],
expr = proMat[id,]) %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12)
ggplot(plotTab, aes(x=viab, y=expr,col = trisomy12, shape = IGHV, linetype = IGHV)) + geom_point() +
scale_shape_manual(values = c(M = 19, U = 1)) + geom_smooth(method = "lm", se=FALSE) +
ggtitle(sprintf("%s ~ %s", drugConc, proteinName)) +
ylab("Protein expression") + xlab("Viability after treatment") +
theme(legend.position = "bottom")
})
plot_grid(plotlist = plotList, ncol =3)
For averaged five concentrations (AUC)
Assocation test
testList <- filter(resTab.auc, adj.P.Val < 0.25)
resTab.auc.block <- lapply(seq(nrow(testList)),function(i) {
pair <- testList[i,]
expr <- proMat[pair$id,]
viab <- viabMat.auc[pair$Drug, ]
ighv <- protCLL[,colnames(viabMat.auc)]$IGHV.status
tri12 <- protCLL[,colnames(viabMat.auc)]$trisomy12
res <- anova(lm(viab~ighv+tri12+expr))
data.frame(id = pair$id, P.Value = res["expr",]$`Pr(>F)`, symbol = pair$symbol,
Drug = pair$Drug,
P.Value.IGHV = res["ighv",]$`Pr(>F)`,P.Value.trisomy12 = res["tri12",]$`Pr(>F)`,
P.Value.noBlock = pair$P.Value,
stringsAsFactors = FALSE)
}) %>% bind_rows() %>% mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>% arrange(P.Value)Number of associations (10% FDR)
Select significant associations
resTab.sig <- filter(resTab.auc.block, adj.P.Val < 0.1) %>%
select(Drug, symbol, id, P.Value, adj.P.Val, P.Value.trisomy12, P.Value.IGHV)plotTab <- resTab.sig %>% group_by(Drug) %>%
summarise(n = length(id)) %>% ungroup()
ordTab <- group_by(plotTab, Drug) %>% summarise(total = sum(n)) %>%
arrange(desc(total))
plotTab <- mutate(plotTab, Drug = factor(Drug, levels = ordTab$Drug)) %>%
filter(n>0)
ggplot(plotTab, aes(x=Drug,y=n)) + geom_bar(stat = "identity", fill = "lightblue") +
theme(axis.text.x = element_text(angle = 90,hjust=1,vjust=0.5)) +
ylab("Number of associations") + xlab("")
Table of significant associations (10% FDR)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Plot top 9 drug-protein associations
plotList <- lapply(seq(9), function(i) {
drugConc <- resTab.sig$Drug[i]
proteinName <- resTab.sig$symbol[i]
id <- resTab.sig$id[i]
plotTab <- tibble(patID = colnames(viabMat.auc),
viab = viabMat.auc[drugConc,],
expr = proMat[id,]) %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12)
ggplot(plotTab, aes(x=viab, y=expr,col = trisomy12, shape = IGHV, linetype = IGHV)) + geom_point(aes()) +
scale_shape_manual(values = c(M = 19, U = 1)) + geom_smooth(method = "lm", se=FALSE) +
ggtitle(sprintf("%s ~ %s", drugConc, proteinName)) +
ylab("Protein expression") + xlab("Viability after treatment") +
theme(legend.position = "bottom")
})
plot_grid(plotlist = plotList, ncol =3)
Compare the ability to explain drug response among genomic, RNA and protein data
Prepare data
Proteomics data
proMat <- assays(protCLL)[["QRILC"]]
proMat <- proMat[,colnames(viabMat.auc)]
sds <- genefilter::rowSds(proMat,na.rm=TRUE)
proMat <- proMat[sds > genefilter::shorth(sds),]removeCorrelated <- function(x, cutoff = 0.8, distance = "cosine", cluster_method = "ward.D2") {
# calculate distiance matrix
if (distance == "binary") {
#maybe also usefull is the input is a sparse matrix
distMat <- dist(t(x), method = "binary")
} else if (distance == "pearson") {
#otherwise, using pearson correlation
distMat <- as.dist(1-cor(x))
} else if (distance == "euclidean") {
distMat <- dist(t(x), method = "euclidean")
} else if (distance == "cosine") {
# cosine similarity maybe prefered for sparse matrix
cosineSimi <- function(x){
x%*%t(x)/(sqrt(rowSums(x^2) %*% t(rowSums(x^2))))
}
distMat <- as.dist(1-cosineSimi(t(x)))
} else if (distance == "canberra") {
distMat <- as.dist(as.matrix(dist(t(x), method = "canberra"))/nrow(x))
}
#hierarchical clustering
hc <- hclust(distMat, method = cluster_method)
clusters <- cutree(hc, h = 1-cutoff)
x.re <- x[,!duplicated(clusters)]
#record the removed features
mapList <- lapply(colnames(x.re), function(i) {
members <- names(clusters[clusters == clusters[i]])
members[members != i]
})
names(mapList) <- colnames(x.re)
return(list(reduced = x.re,
mapReduce = mapList))
}Remove highly correlated proteins
proReduced <- removeCorrelated(t(proMat), cutoff = 0.9, distance = "pearson")
proMat.re <- t(proReduced$reduced)Subset samples
overSample <- intersect(colnames(proMat.re), colnames(dds))
proMat.glm <- proMat.re[,overSample]
viabMat.glm <- viabMat.auc[,overSample]
dds.glm <- dds[,overSample]Prepare expression data
ddsSub.glm <- dds.glm[rowSums(counts(dds.glm)) > 100,]
ddsSub.glm <- varianceStabilizingTransformation(ddsSub.glm)
exprMat <- assay(ddsSub.glm)
sds <- genefilter::rowSds(exprMat)
exprMat <- exprMat[order(sds, decreasing=T)[1:5000],]
reduceRes <- removeCorrelated(t(exprMat), cutoff = 0.9, distance = "pearson")
exprMat.glm <- t(reduceRes$reduced)Prepare genomic data
geneMat <- patMeta[match(overSample, patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11p:U1) %>%
mutate_if(is.factor, as.character) %>% mutate(IGHV.status = ifelse(IGHV.status == "M", 1,0)) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")
geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=3]
geneMat[is.na(geneMat)] <- 0Feature selection using LASSO
Prepare clean data: Integrate all available multi-omics datasets.
inclSet<-list(RNA=t(proMat.glm), drugs= t(viabMat.glm), Protein = t(proMat.glm), gen = geneMat)
cleanData <- generateData(inclSet, censor = 5)Perform lasso regression (3-fold repeated CV)
lassoResults <- list()
for (eachMeasure in names(cleanData$allResponse)) {
dataResult <- list()
for (eachDataset in names(cleanData$allExplain)) {
y <- cleanData$allResponse[[eachMeasure]]
X <- cleanData$allExplain[[eachDataset]]
glmRes <- runGlm(X, y, method = "lasso", repeats = 20, folds = 3)
dataResult[[eachDataset]] <- glmRes
}
lassoResults[[eachMeasure]] <- dataResult
}
save(lassoResults, file = "../output/lassoResults_IC50.RData", version = 2)Visualizing results
load("../output/lassoResults_IC50.RData")Variane explained (R2)
Averaged R2 for all drugs
#load save data
outList.train <- plotVar(lassoResults,cv=FALSE)
outList.cv <- plotVar(lassoResults, cv=TRUE)
sumTab.cv <- outList.cv$summary %>% mutate(group = "CV")
sumTab.train <- outList.train$summary %>% mutate(group = "train")
sumTab <- bind_rows(sumTab.train, sumTab.cv)
std <- function(x) sd(x)/sqrt(length(x))
plotTab <- sumTab %>%
group_by(set,group) %>% summarise(R2 = mean(meanR2), sem = std(meanR2))
ggplot(plotTab, aes(x=set, y = R2, fill = group)) + geom_bar(stat="identity", width = 0.5, position = "dodge") +
geom_errorbar(aes(ymin = R2 -sem, ymax=R2+sem), width= 0.5, position = "dodge") +
theme(legend.position = "none") + theme_bw()
For individual drugs
plotTab <- filter(sumTab, set != "")
#rank by variance explained by proteomics data
drugRank <- plotTab %>% filter(set == "protein", group == "CV") %>% arrange(desc(meanR2)) %>% pull(drug)
plotList <- lapply(drugRank, function(name){
eachTab <- filter(plotTab, drug == name)
ggplot(eachTab,(aes(x=set, y = meanR2, fill = group))) +
geom_bar(stat ="identity",position = "dodge2", width=0.5) +
ggtitle(name) + coord_cartesian(ylim = c(0,1)) +
geom_errorbar(aes(ymax = meanR2 + sdR2, ymin = meanR2-sdR2), position = "dodge2", width=0.5) +
theme_bw() + theme(legend.position = "none")
})
plot_grid(plotlist = plotList, ncol =3)
Proteins selected for each drug
Here I only use the top 10 drugs that best explained by proteomics
drugList <- drugRank[1:10]
plotList <- lapply(drugList,function(n) {
plotMat <- lassoResults[[n]]$protein$coefMat
plotMat <- plotMat[rowMeans(plotMat)!=0,]
plotTab <- plotMat %>% data.frame() %>% rownames_to_column("id") %>%
gather(key = "rep",value = "coef",-id) %>%
group_by(id) %>% summarise(meanCoef = mean(coef),semCoef=std(coef),freq=mean(sign(abs(coef)))) %>%
mutate(id=str_remove(id,"con.protein")) %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol) %>%
arrange(desc(meanCoef)) %>% mutate(symbol = factor(symbol, levels = symbol))
ggplot(plotTab, aes(x=symbol,y=meanCoef,fill = freq)) +
geom_bar(stat = "identity") + geom_errorbar(aes(ymax=meanCoef+semCoef, ymin = meanCoef-semCoef)) +
scale_fill_gradient(high="darkred", low="darkblue",name ="frequency", limits=c(0,1)) +
theme(legend.position = "right", axis.text.x = element_text(angle = 90, hjust=1, vjust = 0.5)) +
ggtitle(n) +
ylab("Coefficient") + xlab("")
})
plotList[[1]]
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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] DESeq2_1.24.0 forcats_0.4.0
[3] stringr_1.4.0 dplyr_0.8.5
[5] purrr_0.3.3 readr_1.3.1
[7] tidyr_1.0.0 tibble_3.0.0
[9] tidyverse_1.3.0 SummarizedExperiment_1.14.0
[11] DelayedArray_0.10.0 BiocParallel_1.18.0
[13] matrixStats_0.54.0 Biobase_2.44.0
[15] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
[17] IRanges_2.18.1 S4Vectors_0.22.0
[19] BiocGenerics_0.30.0 glmnet_2.0-18
[21] foreach_1.4.4 Matrix_1.2-17
[23] jyluMisc_0.1.5 pheatmap_1.0.12
[25] cowplot_0.9.4 ggplot2_3.3.0
[27] limma_3.40.2
loaded via a namespace (and not attached):
[1] shinydashboard_0.7.1 tidyselect_1.0.0 RSQLite_2.1.1
[4] AnnotationDbi_1.46.0 htmlwidgets_1.3 grid_3.6.0
[7] maxstat_0.7-25 munsell_0.5.0 codetools_0.2-16
[10] DT_0.7 withr_2.1.2 colorspace_1.4-1
[13] knitr_1.23 rstudioapi_0.10 ggsignif_0.5.0
[16] labeling_0.3 git2r_0.26.1 slam_0.1-45
[19] GenomeInfoDbData_1.2.1 KMsurv_0.1-5 bit64_0.9-7
[22] farver_2.0.3 rprojroot_1.3-2 vctrs_0.2.4
[25] generics_0.0.2 TH.data_1.0-10 xfun_0.8
[28] sets_1.0-18 R6_2.4.0 locfit_1.5-9.1
[31] bitops_1.0-6 fgsea_1.10.0 assertthat_0.2.1
[34] promises_1.0.1 scales_1.1.0 multcomp_1.4-10
[37] nnet_7.3-12 gtable_0.3.0 sandwich_2.5-1
[40] workflowr_1.6.0 rlang_0.4.5 genefilter_1.66.0
[43] cmprsk_2.2-8 splines_3.6.0 acepack_1.4.1
[46] broom_0.5.2 checkmate_2.0.0 yaml_2.2.0
[49] abind_1.4-5 modelr_0.1.5 crosstalk_1.0.0
[52] backports_1.1.4 httpuv_1.5.1 Hmisc_4.2-0
[55] tools_3.6.0 relations_0.6-8 ellipsis_0.2.0
[58] gplots_3.0.1.1 RColorBrewer_1.1-2 Rcpp_1.0.1
[61] base64enc_0.1-3 visNetwork_2.0.7 zlibbioc_1.30.0
[64] RCurl_1.95-4.12 ggpubr_0.2.1 rpart_4.1-15
[67] zoo_1.8-6 haven_2.2.0 cluster_2.1.0
[70] exactRankTests_0.8-30 fs_1.4.0 magrittr_1.5
[73] data.table_1.12.2 openxlsx_4.1.0.1 reprex_0.3.0
[76] survminer_0.4.4 mvtnorm_1.0-11 hms_0.5.2
[79] shinyjs_1.0 mime_0.7 evaluate_0.14
[82] xtable_1.8-4 XML_3.98-1.20 rio_0.5.16
[85] readxl_1.3.1 gridExtra_2.3 compiler_3.6.0
[88] KernSmooth_2.23-15 crayon_1.3.4 htmltools_0.4.0
[91] mgcv_1.8-28 later_0.8.0 Formula_1.2-3
[94] geneplotter_1.62.0 lubridate_1.7.4 DBI_1.0.0
[97] dbplyr_1.4.2 MASS_7.3-51.4 car_3.0-3
[100] cli_1.1.0 marray_1.62.0 gdata_2.18.0
[103] igraph_1.2.4.1 pkgconfig_2.0.2 km.ci_0.5-2
[106] foreign_0.8-71 piano_2.0.2 xml2_1.2.2
[109] annotate_1.62.0 XVector_0.24.0 drc_3.0-1
[112] rvest_0.3.5 digest_0.6.19 rmarkdown_1.13
[115] cellranger_1.1.0 fastmatch_1.1-0 survMisc_0.5.5
[118] htmlTable_1.13.1 curl_3.3 shiny_1.3.2
[121] gtools_3.8.1 lifecycle_0.2.0 nlme_3.1-140
[124] jsonlite_1.6 carData_3.0-2 pillar_1.4.3
[127] lattice_0.20-38 httr_1.4.1 plotrix_3.7-6
[130] survival_2.44-1.1 glue_1.3.2 zip_2.0.2
[133] iterators_1.0.10 bit_1.1-14 stringi_1.4.3
[136] blob_1.1.1 latticeExtra_0.6-28 caTools_1.17.1.2
[139] memoise_1.1.0