| title | author | date | output |
|---|---|---|---|
RV144 case-control gene-expression analysis |
Slim Fourati |
October 7, 2015 |
github_documents |
loading require packages
suppressPackageStartupMessages(library(package = "readr"))
suppressPackageStartupMessages(library(package = "Biobase"))
suppressPackageStartupMessages(library(package = "impute"))
suppressPackageStartupMessages(library(package = "org.Hs.eg.db"))
suppressPackageStartupMessages(library(package = "ggplot2"))
suppressPackageStartupMessages(library(package = "limma"))
suppressPackageStartupMessages(library(package = "knitr"))
suppressPackageStartupMessages(library(package = "pheatmap"))
suppressPackageStartupMessages(library(package = "grid"))
suppressPackageStartupMessages(library(package = "gtable"))
suppressPackageStartupMessages(library(package = "RCurl"))
suppressPackageStartupMessages(library(package = "dplyr"))
suppressPackageStartupMessages(library(package = "tidyr"))
suppressPackageStartupMessages(library(package = "tibble"))set default options/variables
workDir <- dirname(getwd())
opts_chunk$set(tidy = FALSE, fig.path = "../figure/")
options(stringsAsFactors = FALSE,
width = 80,
mc.cores = detectCores() - 1,
readr.num_columns = 0)read non-normalized matrix
rawFile <- file.path(workDir,
"input/GA_illumina_expression.rv144.matrix_non_norm.csv")
rawMat <- read_csv(file = rawFile, progress = FALSE)read arrays annotation
arraysAnnotFile <- file.path(workDir,
"input",
"GA_illumina_expression.rv144.metadata.csv")
arraysAnnotation <- read_csv(file = arraysAnnotFile, progress = FALSE)
# remove unused phenotypic information
arraysAnnotation <- select(arraysAnnotation,
-title,
-`source name`,
-organism,
-molecule,
-label,
-description,
-platform)
# remove prefix 'characteristics` of column names
names(arraysAnnotation) <- gsub(pattern = "^[^:]+: (.+)$",
replacement = "\\1",
names(arraysAnnotation))read features annotation
featuresAnnotFile <- file.path(workDir,
"input/Illumina_HumanHT12_V4.hg19.chip")
featuresAnnotation <- read_tsv(file = featuresAnnotFile, progress = FALSE) %>%
as.data.frame()
rownames(featuresAnnotation) <- featuresAnnotation$IlmnIDread clinical annotation
clinicalAnnotFile <- file.path(workDir,
"input/rv144.master_wk26.csv")
clinicalAnnotation <- read_csv(file = clinicalAnnotFile,
col_types = paste(rep("c", times = 21),
collapse = ""))
clinicalAnnotation <- lapply(clinicalAnnotation,
FUN = type.convert,
as.is = TRUE) %>%
data.frame(check.names = FALSE)
# remove unused columns
clinicalAnnotation <- clinicalAnnotation %>%
select(-v7, -v9)
rownames(clinicalAnnotation) <- clinicalAnnotation$pincreate non-normalized ExpressionSet
# format raw matrix
rNames <- rawMat$"ID_REF"
rawMat <- rawMat[, -grep(pattern = "ID_REF|Detection Pval",
colnames(rawMat))]
rawMat <- as.matrix(rawMat)
rownames(rawMat) <- rNames
# format phenodata
arraysAnnotation <- as.data.frame(arraysAnnotation)
rownames(arraysAnnotation) <- arraysAnnotation$"Sample name"
arraysAnnotation <- arraysAnnotation[colnames(rawMat), ]
# format feature annotation
featuresAnnotation <- as.data.frame(featuresAnnotation)
featuresAnnotation <- featuresAnnotation[rownames(rawMat), ]
# create ExpressionSet
esetRaw <- ExpressionSet(assayData = rawMat,
phenoData = AnnotatedDataFrame(arraysAnnotation),
featureData = AnnotatedDataFrame(featuresAnnotation))
# save raw ExpressionSet
save(esetRaw, file = file.path(workDir, "output/rv144.esetRaw.RData"))quality control: kernel densities plot
intensities <- as.vector(exprs(esetRaw))
# log2 transform (replace intensities < 1 by 1 to prevent -Inf)
intensities <- log2(pmax(intensities, 1))
arrays <- rep(sampleNames(esetRaw), each = nrow(esetRaw))
scanNote <- rep(esetRaw$"scanning note",
each = nrow(esetRaw))
plotDF <- data.frame(intensities, arrays, scanNote)
plotDens <- ggplot(data = plotDF,
mapping = aes(x = intensities,
group = factor(arrays),
color = scanNote)) +
scale_color_discrete(name = "scanning note") +
stat_density(geom = "path", position = "identity") +
labs(x = "log2 intensity",
title = "density plot [log2 raw intensities]") +
theme_bw() +
theme(legend.key = element_blank())
print(plotDens)
# print problematic array name
outlierArrays <- filter(pData(esetRaw),
`scanning note` != "ok") %>%
.$"Sample name"
print(outlierArrays)## [1] "RV144_d305968_DMSO"
# exclude arrays from raw ExpressionSet
esetRaw <- esetRaw[, setdiff(sampleNames(esetRaw), outlierArrays)]quality control: multidimentional scaling plot
# create tempory ExpressionSet and normalize the data
esetTemp <- esetRaw
# exclude problematic arrays
esetTemp <- esetTemp[, setdiff(sampleNames(esetTemp), outlierArrays)]
rawMat <- exprs(esetTemp)
normMat <- normalizeBetweenArrays(rawMat, method = "quantile")
normMat <- log2(normMat)
# generate a multidimensional scaling plot
distMat <- dist(t(normMat))
mds <- cmdscale(d = distMat, k = ncol(normMat) - 1, eig = TRUE)
mdsStDev <- apply(mds$points, MARGIN = 2, FUN = sd)
mdsPercVar <- round((mdsStDev^2)/sum(mdsStDev^2) * 100)
plotDF <- data.frame(V1 = mds$points[, 1],
V2 = mds$points[, 2])
# color dots by whether a sample is a hybridization control
plotDF$controlSamples <- grepl(pattern = "control",
rownames(plotDF))
qcMdS <- ggplot(data = plotDF,
mapping = aes(x = V1, y = V2, color = controlSamples)) +
geom_point(size = 3) +
labs(x = paste0("1st dimension (", mdsPercVar[1], "%)"),
y = paste0("2nd dimension (", mdsPercVar[2], "%)"),
title = "Multidimentional scaling plot") +
theme_bw() +
theme(legend.key = element_blank())
print(qcMdS)
# print control array names
controlArrays <- filter(pData(esetRaw),
grepl(pattern = "control", `Sample name`)) %>%
.$"Sample name"
print(controlArrays)## [1] "RV144_control" "RV144_control_rep12" "RV144_control_rep8"
## [4] "RV144_control_rep11" "RV144_control_rep1" "RV144_control_rep6"
## [7] "RV144_control_rep7" "RV144_control_rep2" "RV144_control_rep10"
## [10] "RV144_control_rep14" "RV144_control_rep13" "RV144_control_rep9"
## [13] "RV144_control_rep5" "RV144_control_rep4" "RV144_control_rep3"
quality control: multidimentional scaling plot excluding hybridization controls
# create tempory ExpressionSet and normalize the data
esetTemp <- esetRaw
# exclude problematic arrays
esetTemp <- esetTemp[, setdiff(sampleNames(esetTemp),
c(outlierArrays, controlArrays))]
rawMat <- exprs(esetTemp)
normMat <- normalizeBetweenArrays(rawMat, method = "quantile")
normMat <- log2(normMat)
# generate a multidimensional scaling plot
distMat <- dist(t(normMat))
mds <- cmdscale(d = distMat, k = ncol(normMat) - 1, eig = TRUE)
mdsStDev <- apply(mds$points, MARGIN = 2, FUN = sd)
mdsPercVar <- round((mdsStDev^2)/sum(mdsStDev^2) * 100)
plotDF <- data.frame(V1 = mds$points[, 1],
V2 = mds$points[, 2])
# color dots by protocol used for RNA extraction
plotDF$RNAextractionProtocol <-
pData(esetTemp)[rownames(plotDF), "RNA extraction protocol"]
qcMdS <- ggplot(data = plotDF,
mapping = aes(x = V1, y = V2, color = RNAextractionProtocol)) +
geom_point(size = 3) +
scale_color_manual(name =
"RNA extraction protocol",
values = c("orange", "red")) +
labs(x = paste0("1st dimension (", mdsPercVar[1], "%)"),
y = paste0("2nd dimension (", mdsPercVar[2], "%)"),
title = "Multidimentional scaling plot") +
theme_bw() +
theme(legend.key = element_blank())
print(qcMdS)
# print control array generated with different RNA extraction protocol
rnaExtractionArrays <- filter(pData(esetRaw),
grepl(
pattern = "Rneasy",
`RNA extraction protocol`)) %>%
.$"Sample name"
print(rnaExtractionArrays)## [1] "RV144_d825482_92TH023ENV" "RV144_d824374_92TH023ENV"
## [3] "RV144_d849655_92TH023ENV" "RV144_d723863_92TH023ENV"
## [5] "RV144_d203143_92TH023ENV" "RV144_d827158_92TH023ENV"
## [7] "RV144_d736104_92TH023ENV" "RV144_d603208_92TH023ENV"
## [9] "RV144_d627156_92TH023ENV" "RV144_d431442_92TH023ENV"
## [11] "RV144_d127852_92TH023ENV" "RV144_d513999_92TH023ENV"
normalizing raw expression
eset <- esetRaw
# order esetRaw by idat file name and features by ProbeID
eset <- eset[order(as.numeric(fData(eset)$ProbeID)),
order(eset$"idat file")]
# impute missing intensities (intensities = 0)
rawMat <- exprs(eset)
rawMat[rawMat == 0] <- NA
suppressWarnings(capture.output(rawMat <- impute.knn(data = rawMat)$data,
file = "/dev/null"))
exprs(eset) <- rawMat
# remove outlier
outlierArrays <- sampleNames(eset)[eset$outlierFlag != ""]
eset <- eset[, setdiff(sampleNames(eset),
c(outlierArrays, controlArrays, rnaExtractionArrays))]
# quantile normalized and log2 transform expression
normMat <- normalizeBetweenArrays(exprs(eset), method = "quantile")
# surrogate remplacement
normMat[normMat < 2^0.1] <- 2^0.1
normMat <- log2(normMat)
exprs(eset) <- normMat
# save normalized ExpressionSet
save(eset, file = file.path(workDir, "output/rv144.eset.RData"))quality control: gender check
symbol2chr <- merge(as.data.frame(org.Hs.egSYMBOL),
as.data.frame(org.Hs.egCHR),
by = "gene_id")
pb2symbol <- strsplit(fData(eset)$SYMBOL, split = " /// ") %>%
setNames(fData(eset)$IlmnID) %>%
stack() %>%
mutate(ind = as.vector(ind))
pb2chr <- merge(symbol2chr, pb2symbol, by.x = "symbol", by.y = "values")
pbY <- filter(pb2chr, chromosome %in% "Y") %>%
.$ind
plotDF <- data.frame(mu = colMeans(exprs(eset)[pbY, ])) %>%
rownames_to_column() %>%
merge(pData(eset), by.x = "rowname", by.y = "Sample name") %>%
merge(clinicalAnnotation, by.x = "donor", by.y = "pin")
ggplot(data = plotDF, mapping = aes(x = dem_sex, y = mu)) +
geom_jitter() +
labs(y = "Average expression of chr.Y probes") +
theme_bw()
exploratory analysis: heatmap based on top 100 most varying transcripts
bluered <- colorRampPalette(colors = c("blue", "white", "red"))
varList <- apply(exprs(eset), MARGIN = 1, FUN = var)
esetTemp <- eset[order(varList, decreasing = TRUE)[1:100], ]
esetTemp$donor <- factor(esetTemp$donor)
exploHeat <- pheatmap(mat = exprs(esetTemp),
color = bluered(100),
scale = "row",
treeheight_row = 0,
annotation_col = pData(esetTemp)[,
c("donor",
"stimulation")],
show_colnames = FALSE,
show_rownames = FALSE,
main = paste0("top 100 varying probes",
"[Euclidean distance, complete linkage]"),
silent = TRUE)
colorName <- textGrob("z-score", x = 0.5, y = 1.05, gp = gpar(fontface = "bold"))
exploHeat$gtable <- gtable_add_grob(exploHeat$gtable,
colorName,
t = 3,
l = 5,
b = 5,
clip = "off",
name = "colorName")
grid.draw(exploHeat$gtable)
# test association with stimulation
tab <- table(cutree(exploHeat$tree_col, k = 2),
esetTemp$stimulation)
fit <- fisher.test(tab)
print(tab)##
## 92TH023ENV DMSO
## 1 29 122
## 2 171 90
print(fit)##
## Fisher's Exact Test for Count Data
##
## data: tab
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.07483947 0.20646421
## sample estimates:
## odds ratio
## 0.1258127
# test for clustering of samples by participants
participantList <- esetTemp$donor[exploHeat$tree_col$order]
y <- sum(participantList[-length(participantList)] == participantList[-1])
# derive p-value using a 1000 fold permutation test
B <- 1000
yhat <- mclapply(1:B, function(seed) {
set.seed(seed = seed)
participantList <- sample(esetTemp$donor)
return(value = sum(participantList[-length(participantList)] ==
participantList[-1]))
})
print(paste0("permutation test: p<=", max(mean(unlist(yhat) >= y), 1/B)))## [1] "permutation test: p<=0.001"
create a ENV-DMSO ExpressionSet
# identify complete pair of ENV-DMSO stimulated samples
flag <- dplyr::select(pData(eset),
`Sample name`,
donor,
stimulation) %>%
spread(stimulation, `Sample name`) %>%
filter(!is.na(`92TH023ENV`) & !is.na(DMSO))
esetBaselined <- eset[, flag$"92TH023ENV"]
exprs(esetBaselined) <- exprs(esetBaselined) - exprs(eset[, flag$DMSO])
# save ENV-DMSO expression
save(esetBaselined, file = file.path(workDir, "output/rv144.esetBaselined.RData"))differential expression analysis: stimulation effect
# create list to save linear models
fits <- list()
# identify samples stimulated both by ENV and DMSO
flag <- pData(eset) %>%
select(`Sample name`, donor, stimulation) %>%
spread(stimulation, `Sample name`) %>%
filter(!is.na(`92TH023ENV`) & !is.na(DMSO)) %>%
gather(stimulation, `Sample name`, -donor)
esetTemp <- eset[, flag$"Sample name"]
# identify genes differently expressed separetly for the VACCINE and PLACEBO
# treatment groups
TreatmentStim <- interaction(esetTemp$treatment,
esetTemp$stimulation,
sep = "_")
design <- model.matrix(~0+TreatmentStim)
colnames(design) <- gsub(pattern = "TreatmentStim",
replacement = "",
colnames(design))
rownames(design) <- sampleNames(esetTemp)
# fit a fixed effect to the treatment/strim and a random effect to the donor in
# the linear regression model
donor <- factor(esetTemp$donor)
corfit <- duplicateCorrelation(exprs(esetTemp),
design = design,
block = esetTemp$donor)
fit <- lmFit(esetTemp,
design = design,
block = donor,
correlation = corfit$consensus)
contrastLS <- c("PLACEBO_92TH023ENV-PLACEBO_DMSO",
"VACCINE_92TH023ENV-VACCINE_DMSO")
contrastMat <- makeContrasts(contrasts = contrastLS, levels = fit$design)
fit2 <- contrasts.fit(fit = fit, contrasts = contrastMat)
fit2 <- eBayes(fit = fit2)
fits[["stimulation"]] <- list(fit = fit, fit2 = fit2)
# differential expression analysis: HIV case vs. control
TreatmentInfect <- interaction(esetBaselined$treatment,
esetBaselined$"HIV infection",
sep = "_",
lex.order = TRUE)
design <- model.matrix(~0+TreatmentInfect)
colnames(design) <- gsub(pattern = "TreatmentInfect",
replacement = "",
colnames(design))
rownames(design) <- sampleNames(esetBaselined)
fit <- lmFit(esetBaselined, design = design)
contrastLS <- c("PLACEBO_No-PLACEBO_Yes",
"VACCINE_No-VACCINE_Yes")
contrastMat <- makeContrasts(contrasts = contrastLS, levels = fit$design)
fit2 <- contrasts.fit(fit = fit, contrasts = contrastMat)
fit2 <- eBayes(fit = fit2)
# top 10 differently expressed transcript in the placebo group
topTable(fit = fit2, coef = 1) %>%
select(SYMBOL, logFC, t, P.Value, adj.P.Val) %>%
print()## SYMBOL logFC t P.Value adj.P.Val
## ILMN_1896145 --- 0.3017263 4.652130 5.976632e-06 0.2828321
## ILMN_1711883 HTR3D -0.2017627 -4.159871 4.730030e-05 0.9988950
## ILMN_1677374 --- -0.1844982 -4.068273 6.819273e-05 0.9988950
## ILMN_1717324 --- 0.2424789 3.875968 1.440693e-04 0.9988950
## ILMN_3239480 UCA1 -0.1652911 -3.821776 1.769920e-04 0.9988950
## ILMN_3299441 --- -0.2514595 -3.790279 1.992803e-04 0.9988950
## ILMN_1691361 --- -0.1809396 -3.727657 2.517173e-04 0.9988950
## ILMN_1685045 CHI3L2 -0.1708355 -3.724162 2.549989e-04 0.9988950
## ILMN_1725139 CA9 -0.1671642 -3.704351 2.743727e-04 0.9988950
## ILMN_1684897 SPDYE1 /// SPDYE5 -0.2025732 -3.684547 2.951235e-04 0.9988950
# top 10 differently expressed transcript in the vaccine group
topTable(fit = fit2, coef = 2) %>%
select(SYMBOL, logFC, t, P.Value, adj.P.Val) %>%
print()## SYMBOL logFC t P.Value adj.P.Val
## ILMN_2140990 CAMK1 0.4370108 4.388951 1.845103e-05 0.3459782
## ILMN_2376133 KIAA1191 -0.1687398 -4.310844 2.554186e-05 0.3459782
## ILMN_1759787 THBD -0.2611175 -4.301246 2.657524e-05 0.3459782
## ILMN_2093027 MYO1B -0.1768234 -4.278022 2.924398e-05 0.3459782
## ILMN_1664602 --- -0.1857514 -3.939602 1.128235e-04 0.5696287
## ILMN_1655230 --- -0.0997451 -3.935295 1.147168e-04 0.5696287
## ILMN_1787680 VIMP -0.1504375 -3.926250 1.187913e-04 0.5696287
## ILMN_3238859 FAM120AOS -0.1350209 -3.852309 1.576563e-04 0.5696287
## ILMN_1657234 CCL20 -0.7692402 -3.850290 1.588705e-04 0.5696287
## ILMN_2105919 FGF2 -0.1026889 -3.846069 1.614375e-04 0.5696287
# save MArrayLM in list
fits[["infection"]] <- list(fit = fit, fit2 = fit2)
#save MArrayLM list
save(fits, file = file.path(workDir, "output/rv144.fits.RData"))modelName <- "stimulation"
fit2 <- fits[[modelName]][["fit2"]]
for (coefName in colnames(fit2)) {
coefLS <- unlist(strsplit(coefName, split = "-")) %>%
gsub(pattern = "\\(|\\)", replacement = "")
sampleLS <- fit2$design %>%
as.data.frame() %>%
rownames_to_column() %>%
select_(.dots = c("rowname", coefLS)) %>%
filter(apply(., MARGIN = 1, FUN = function(x) any(x %in% 1)))
top <- topTable(fit = fit2,
coef = coefName,
number = Inf) %>%
mutate(SYMBOL = gsub(pattern = "^([^ ]+) ///.+",
replacement = "\\1",
SYMBOL)) %>%
filter(SYMBOL != "---") %>%
top_n(n = 50, wt = abs(t))
exprsMat <- exprs(eset)[top$IlmnID, sampleLS$rowname] %>%
(function(x) return(value = t(scale(t(x)))))
breakLS <- c(-1 * max(abs(exprsMat)),
seq(from = -1 * min(abs(range(exprsMat))),
to = min(abs(range(exprsMat))),
length.out = 99),
max(abs(exprsMat)))
colAnnotDF <- pData(eset) %>%
.[, c("stimulation", "treatment")]
rowAnnotDF <- top %>%
mutate(`fold-change sign` = ifelse(test = sign(logFC) %in% 1,
yes = "UP",
no = "DN"),
`-log10 P.Value` = -1 * log10(P.Value),
`adj. p <= 0.05` = factor(adj.P.Val <= 0.05)) %>%
select(IlmnID, `fold-change sign`, `-log10 P.Value`, `adj. p <= 0.05`)
rownames(rowAnnotDF) <- rowAnnotDF$IlmnID
rowAnnotDF$IlmnID <- NULL
colorAnnotLS <- list(treatment = c(PLACEBO = "blue", VACCINE = "green"),
"fold-change sign" = c(UP = "red", DN = "blue"))
pheat <- pheatmap(mat = exprsMat,
color = bluered(100),
breaks = breakLS,
cellwidth = 2,
cellheight = 2,
treeheight_row = 0,
annotation_col = colAnnotDF,
annotation_row = rowAnnotDF,
annotation_colors = colorAnnotLS,
show_colnames = FALSE,
main = coefName,
fontsize_row = 2,
labels_row = top$SYMBOL)
}
modelName <- "infection"
fit2 <- fits[[modelName]][["fit2"]]
for (coefName in colnames(fit2)) {
coefLS <- unlist(strsplit(coefName, split = "-")) %>%
gsub(pattern = "\\(|\\)", replacement = "")
sampleLS <- fit2$design %>%
as.data.frame() %>%
rownames_to_column() %>%
select_(.dots = c("rowname", coefLS)) %>%
filter(apply(., MARGIN = 1, FUN = function(x) any(x %in% 1)))
top <- topTable(fit = fit2,
coef = coefName,
number = Inf) %>%
mutate(SYMBOL = gsub(pattern = "^([^ ]+) ///.+",
replacement = "\\1",
SYMBOL)) %>%
filter(SYMBOL != "---") %>%
top_n(n = 50, wt = abs(t))
exprsMat <- exprs(eset)[top$IlmnID, sampleLS$rowname] %>%
(function(x) return(value = t(scale(t(x)))))
breakLS <- c(-1 * max(abs(exprsMat)),
seq(from = -1 * min(abs(range(exprsMat))),
to = min(abs(range(exprsMat))),
length.out = 99),
max(abs(exprsMat)))
colAnnotDF <- pData(eset) %>%
.[, c("treatment", "HIV infection")]
rowAnnotDF <- top %>%
mutate(`fold-change sign` = ifelse(test = sign(logFC) %in% 1,
yes = "UP",
no = "DN"),
`-log10 P.Value` = -1 * log10(P.Value),
`adj. p <= 0.05` = factor(adj.P.Val <= 0.05)) %>%
select(IlmnID, `fold-change sign`, `-log10 P.Value`, `adj. p <= 0.05`)
rownames(rowAnnotDF) <- rowAnnotDF$IlmnID
rowAnnotDF$IlmnID <- NULL
colorAnnotLS <- list(treatment = c(PLACEBO = "blue", VACCINE = "green"),
"HIV infection" = c(No = "black", Yes = "red"),
"fold-change sign" = c(UP = "red", DN = "blue"))
pheat <- pheatmap(mat = exprsMat,
color = bluered(100),
breaks = breakLS,
cellwidth = 4,
cellheight = 4,
treeheight_row = 0,
annotation_col = colAnnotDF,
annotation_row = rowAnnotDF,
annotation_colors = colorAnnotLS,
show_colnames = FALSE,
main = coefName,
fontsize_row = 4,
labels_row = top$SYMBOL)
}
print session info
sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin17.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.6
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.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] grid stats4 parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 tibble_1.4.2 tidyr_0.8.1
## [4] dplyr_0.7.6 RCurl_1.95-4.11 bitops_1.0-6
## [7] gtable_0.2.0 pheatmap_1.0.10 limma_3.36.2
## [10] ggplot2_3.0.0 org.Hs.eg.db_3.6.0 AnnotationDbi_1.42.1
## [13] IRanges_2.14.10 S4Vectors_0.18.3 impute_1.54.0
## [16] Biobase_2.40.0 BiocGenerics_0.26.0 readr_1.1.1
## [19] knitr_1.20
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.18 highr_0.7 RColorBrewer_1.1-2 bindr_0.1.1
## [5] pillar_1.3.0 compiler_3.5.1 plyr_1.8.4 tools_3.5.1
## [9] statmod_1.4.30 digest_0.6.15 bit_1.1-14 RSQLite_2.1.1
## [13] evaluate_0.11 memoise_1.1.0 pkgconfig_2.0.1 rlang_0.2.1
## [17] DBI_1.0.0 withr_2.1.2 stringr_1.3.1 hms_0.4.2
## [21] tidyselect_0.2.4 bit64_0.9-7 glue_1.3.0 R6_2.2.2
## [25] purrr_0.2.5 blob_1.1.1 magrittr_1.5 scales_1.0.0
## [29] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3 stringi_1.2.4
## [33] lazyeval_0.2.1 munsell_0.5.0 crayon_1.3.4