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deg_analysis.R
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loadCounts <- function(filename) {
cts_all <- read.table(gzfile(filename), sep='\t', header=T, stringsAsFactors = FALSE)
rownames(cts_all) <- cts_all$Geneid
cts_all$Geneid <- NULL
rowdata <- cts_all[,1:5]
cts_all <- as.matrix(cts_all[,-c(1:5)])
colnames(cts_all) <- sapply(strsplit(colnames(cts_all), '\\.'), function(x) tail(x,2)[1])
cts_all <- cts_all[,order(colnames(cts_all))]
return(cts_all)
}
getRowdata <- function(filename) {
cts_all <- read.table(gzfile(filename), sep='\t', header=T, stringsAsFactors = FALSE)
rownames(cts_all) <- cts_all$Geneid
cts_all$Geneid <- NULL
rowdata <- cts_all[,1:5]
colnames(rowdata)[colnames(rowdata) == 'Length'] <- 'basepairs'
return(rowdata)
}
# plotClusterMeans <- function(mycpheatctsout, x_time, lwd=.1, col=1, ...) {
# N <- max(mycpheatctsout$clusterID)
# par(mfrow=c(N,2))
# for(k in 1:N) {
# x <- t(mycpheatctsout$countmeans[mycpheatctsout$clusterID==k,seq_along(x_time)])
# y <- t(mycpheatctsout$countmeans[mycpheatctsout$clusterID==k,seq_along(x_time)+length(seq_along(x_time))])
# matplot(x_time, x,lty=1, col=col, lwd=lwd, type='l',
# ylim=range(c(x,y)), ...)
# points(x_time, rowMeans(x), pch='o', cex=1.2, col='red')
# abline(h=0, lwd=2, lty=2)
# matplot(x_time, y,lty=1, col=col, lwd=lwd, type='l',
# ylim=range(c(x,y)), ...)
# points(x_time, rowMeans(y), pch='o', cex=1.2, col='red')
# abline(h=0, lwd=2, lty=2)
# }
# }
plotClusterMeans <- function(mycpheatctsout, x_time, genotypes=2, lwd=.1,
plot_means = TRUE,
lwdRenorm = FALSE, col=1, ...) {
N <- max(mycpheatctsout$clusterID)
if(lwdRenorm) {
origlwd <- lwd
lwd <- (1/log10(sapply(mycpheatctsout$clusters_gene_list, length)))^3
lwd <- lwd/max(lwd)
lwd <- lwd * origlwd
} else lwd <- rep(lwd, N)
par(mfrow=c(N,genotypes))
for(k in 1:N) {
X <- mycpheatctsout$countmeans[mycpheatctsout$clusterID==k,]
for(j in 1:genotypes) {
y <- t(X[,seq_along(x_time)+(j-1)*length(seq_along(x_time))])
matplot(x_time, y,lty=1, col=col, lwd=lwd[k], type='l',
ylim=range(X), ...)
if(plot_means) points(x_time, rowMeans(y), pch='o', cex=1.2, col='red')
abline(h=0, lwd=2, lty=2)
}
}
}
modClProf <- function(x, max.nchar=200) {
# add the log10(p.adjust) column
x@compareClusterResult$log10.p.adjust <- log10(x@compareClusterResult$p.adjust)
# reduce the description length
descr <- factor(gsub('\n', ' ', x@compareClusterResult$Description))
descr.levels <- levels(descr)
subsrt.stop <- nchar(descr.levels)
substr.start <- sapply(subsrt.stop-max.nchar, function(x) max(c(x,1)))
new.descr.levels <- substr(descr.levels, substr.start, subsrt.stop)
if(any(duplicated(new.descr.levels))) {
idx <- duplicated(new.descr.levels)
new.descr.levels[idx] <- paste(new.descr.levels[idx], 1:length(which(idx)), sep='_')
}
levels(descr) <- new.descr.levels
new.descr <- as.character(descr)
x@compareClusterResult$Description <- new.descr
# return the object
return(x)
}
### reorder pheatmap clusters based on display order
# clst <- cutree(p$tree_row, k=22)
# clst <- factor(clst, unique(clst[p$tree_row$order]))
# clst <- as.numeric(clst)
# ItemsList <- venn(sc_ec_marker_genes, zcolor = 'style', box = F)
# sc_ec_marker_genes_intersections <- attributes(ItemsList)$intersections
stabilizedFpkms <- function(dds) {
stopifnot(require(DESeq2))
stopifnot('basepairs' %in% colnames(rowData(dds)))
vsd <- vst(dds, blind=FALSE)
feature_length_norm <- rowData(dds)$basepairs/1000
libsize_norm <- mean(colSums(counts(dds)))/10^6
stabilized_fpkms <- 2^assay(vsd)/feature_length_norm/libsize_norm
return(stabilized_fpkms)
}
getClusterGenes <- function(pheat_out, n) {
ec_cutree_row <- cutree(pheat_out$tree_row, n)
clusters_gene <- split(names(ec_cutree_row), ec_cutree_row)
clusters_gene <- clusters_gene[unique(ec_cutree_row[pheat_out$tree_row$order])]
names(clusters_gene) <- 1:length(clusters_gene)
return(clusters_gene)
}
non_redundant <- function(filename) {
file_lines <- readLines(filename)
gene_list <- unlist(strsplit(toupper(file_lines), ',|;'))
gene_table <- table(gene_list)
out <- lapply(length(file_lines):1, function(i) {
x <- names(gene_table)[gene_table==i]
if(length(x)==0) return('-') else
return(paste(x, collapse=';'))
})
out <- unlist(out)
cat(out, sep='\n')
}
log2log2Plot <- function(de1, de2, diffres = NULL,
l2fc_thr=c(0.6, Inf),
padj_thr=1e-1,
replacePadjNAby = 1,
replaceLog2fcNAby = 0,
expr_filter=NULL,
gene_labels = '',
max.overlaps = 10,
seed = 1,
global_trend = FALSE,
yellow_trend = FALSE,
blue_trend = FALSE
#color_scale = c('grey70','blue','gold','forestgreen','red')
) {
color_scale <- c('grey70','blue','gold','forestgreen','red')
# cg <- intersect(rownames(de1), rownames(de2))
# de1 <- data.frame(de1[cg,])
# de2 <- data.frame(de2[cg,])
l2fcmat <- mergeTwoRes(de1, de2, cols = 'log2FoldChange',
suffixes = c('.x','.y'), replaceNAby = replaceLog2fcNAby)
if(!is.null(diffres)) {
l2fcmat <- mergeTwoRes(l2fcmat, diffres, 'log2FoldChange', replaceLog2fcNAby,
suffixes = '.diff')
}
padjmat <- mergeTwoRes(de1, de2, cols = 'padj',
suffixes = c('.x','.y'), replaceNAby = replacePadjNAby)
if(!is.null(diffres)) {
padjmat <- mergeTwoRes(padjmat, diffres, 'padj', replacePadjNAby,
suffixes = '.diff')
}
de1 <- cbind(l2fcmat[,1,drop=F],padjmat[,1,drop=F])
colnames(de1) <- sub('\\..*','',colnames(de1))
de2 <- cbind(l2fcmat[,2,drop=F],padjmat[,2,drop=F])
colnames(de2) <- sub('\\..*','',colnames(de2))
if(!is.null(diffres)) {
de3 <- cbind(l2fcmat[,3,drop=F],padjmat[,3,drop=F])
colnames(de3) <- sub('\\..*','',colnames(de3))
}
l2fc_thr_min <- l2fc_thr[1]
l2fc_thr_max <- l2fc_thr[2]
padj_thr_min <- padj_thr[1]
diffexpressed <- gene_class(de1, de2, padj_thr_min, l2fc_thr_min)$class
if(!is.null(expr_filter)) diffexpressed[!expr_filter] <- 0
if(!is.null(diffres)) {
nondiff <- de3$padj>padj_thr_min | abs(de3$log2FoldChange)<l2fc_thr_min
diffexpressed[nondiff] <- 0
}
deSat <- function(de) {
satFCup <- de$log2FoldChange > l2fc_thr_max
de$log2FoldChange[satFCup] <- l2fc_thr_max
satFCdn <- de$log2FoldChange < -l2fc_thr_max
de$log2FoldChange[satFCdn] <- -l2fc_thr_max
de$saturated <- satFCup | satFCdn
de$saturated[is.na(de$saturated)] <- FALSE
return(de)
}
de1 <- deSat(de1); colnames(de1) <- paste(colnames(de1),'1',sep='_')
de2 <- deSat(de2); colnames(de2) <- paste(colnames(de2),'2',sep='_')
if(!is.null(diffres)) {
de3 <- deSat(de3); colnames(de3) <- paste(colnames(de3),'3',sep='_')
de <- cbind(de1, de2, de3, diffexpressed=diffexpressed)
} else {
de <- cbind(de1, de2, diffexpressed=diffexpressed)
}
de$saturated <- de$saturated_1 | de$saturated_2
de$delabel <- NA
if(is.null(gene_labels)) {
de$delabel[de$diffexpressed != 0] <- rownames(de)[de$diffexpressed != 0]
} else {
idx <- rownames(de) %in% gene_labels
de$delabel[idx] <- rownames(de)[idx]
}
n_gene_class <- table(de$diffexpressed)
# trend lines
if( length(which(diffexpressed == 1)) < 2 ) blue_trend <- FALSE
if( length(which(diffexpressed == 2)) < 2 ) yellow_trend <- FALSE
if( global_trend ) {
# calculate lm based on x-sum-of-squares
global_coef <- coef(with(de, lm(log2FoldChange_2~log2FoldChange_1)))
}
if( blue_trend ) {
# calculate lm based on x-sum-of-squares
blue_coef <- coef(with(subset(de, diffexpressed == 1),
lm(log2FoldChange_2~log2FoldChange_1)))
}
if( yellow_trend ) {
# calculate lm based on y-sum-of-squares (and then invert the slope)
yellow_coef <- coef(with(subset(de, diffexpressed == 2),
lm(log2FoldChange_1~log2FoldChange_2)))
}
assign('log2log2de', de, envir = globalenv())
# plot adding up all layers we have seen so far
g <- ggplot(data=de, aes(x=log2FoldChange_1, y=log2FoldChange_2,
col=diffexpressed, label=delabel, shape=saturated)) +
geom_point(size=1.5) +
# geom_bin2d() +
geom_point(data = subset(de, diffexpressed != 0),
aes(x=log2FoldChange_1, y=log2FoldChange_2,
col=diffexpressed, shape=saturated))
if( global_trend ) g <- g +
geom_abline(intercept = global_coef[1],
slope = global_coef[2],
color = 'black') #'dodgerblue4')
if( blue_trend ) g <- g +
geom_abline(intercept = blue_coef[1],
slope = blue_coef[2],
color = 'royalblue4') #'dodgerblue4')
if( yellow_trend ) g <- g +
geom_abline(intercept = yellow_coef[1],
slope = 1/yellow_coef[2],
color = 'darkgoldenrod')
g <- g + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.5)) +
geom_text_repel(max.overlaps=max.overlaps, size = 3, colour = "black", seed=seed) +
scale_x_continuous(expand = expansion(mult = .015)) +
scale_y_continuous(expand = expansion(mult = c(.01, .015))) +
geom_vline(xintercept=c(-l2fc_thr_min, l2fc_thr_min), linetype="dashed") +
geom_hline(yintercept=c(-l2fc_thr_min, l2fc_thr_min), linetype="dashed") +
scale_color_discrete(name = "Gene class",
labels = c(
paste0("no-degs (",n_gene_class[1],")"),
paste0("x-degs (",n_gene_class[2],")"),
paste0("y-degs (",n_gene_class[3],")"),
paste0("same (",n_gene_class[4],")"),
paste0("opposite (",n_gene_class[5],")")
)[n_gene_class>0],
type=color_scale[n_gene_class>0])
# if(is.finite(l2fc_thr_max)) {
# g <- g + xlim(-l2fc_thr_max, l2fc_thr_max) + ylim(-l2fc_thr_max, l2fc_thr_max)
# }
return(g)
}
# with this function you can set l2fc_thr specific for x and y
log2log2Plot2 <- function(de1, de2, diffres = NULL,
l2fc_thr=c(0.6, Inf),
l2fc_thr_ymax=NULL,
padj_thr=1e-1,
replacePadjNAby = 1,
replaceLog2fcNAby = 0,
expr_filter=NULL,
gene_labels = '',
max.overlaps = 10,
seed = 1,
global_cor = FALSE,
yellow_trend = FALSE,
blue_trend = FALSE
#color_scale = c('grey70','blue','gold','forestgreen','red')
) {
color_scale <- c('grey70','blue','gold','forestgreen','red')
# cg <- intersect(rownames(de1), rownames(de2))
# de1 <- data.frame(de1[cg,])
# de2 <- data.frame(de2[cg,])
l2fcmat <- mergeTwoRes(de1, de2, cols = 'log2FoldChange',
suffixes = c('.x','.y'), replaceNAby = replaceLog2fcNAby)
if(!is.null(diffres)) {
l2fcmat <- mergeTwoRes(l2fcmat, diffres, 'log2FoldChange', replaceLog2fcNAby,
suffixes = '.diff')
}
padjmat <- mergeTwoRes(de1, de2, cols = 'padj',
suffixes = c('.x','.y'), replaceNAby = replacePadjNAby)
if(!is.null(diffres)) {
padjmat <- mergeTwoRes(padjmat, diffres, 'padj', replacePadjNAby,
suffixes = '.diff')
}
de1 <- cbind(l2fcmat[,1,drop=F],padjmat[,1,drop=F])
colnames(de1) <- sub('\\..*','',colnames(de1))
de2 <- cbind(l2fcmat[,2,drop=F],padjmat[,2,drop=F])
colnames(de2) <- sub('\\..*','',colnames(de2))
if(!is.null(diffres)) {
de3 <- cbind(l2fcmat[,3,drop=F],padjmat[,3,drop=F])
colnames(de3) <- sub('\\..*','',colnames(de3))
}
l2fc_thr_min <- l2fc_thr[1]
l2fc_thr_max <- l2fc_thr[2]
if(is.null(l2fc_thr_ymax))
l2fc_thr_ymax <- l2fc_thr_max
padj_thr_min <- padj_thr[1]
diffexpressed <- gene_class(de1, de2, padj_thr_min, l2fc_thr_min)$class
if(!is.null(expr_filter)) diffexpressed[!expr_filter] <- 0
if(!is.null(diffres)) {
nondiff <- de3$padj>padj_thr_min | abs(de3$log2FoldChange)<l2fc_thr_min
diffexpressed[nondiff] <- 0
}
deSat <- function(de, l2fc_thr_max) {
satFCup <- de$log2FoldChange > l2fc_thr_max
de$log2FoldChange[satFCup] <- l2fc_thr_max
satFCdn <- de$log2FoldChange < -l2fc_thr_max
de$log2FoldChange[satFCdn] <- -l2fc_thr_max
de$saturated <- satFCup | satFCdn
de$saturated[is.na(de$saturated)] <- FALSE
return(de)
}
de1 <- deSat(de1, l2fc_thr_max); colnames(de1) <- paste(colnames(de1),'1',sep='_')
de2 <- deSat(de2, l2fc_thr_ymax); colnames(de2) <- paste(colnames(de2),'2',sep='_')
if(!is.null(diffres)) {
de3 <- deSat(de3); colnames(de3) <- paste(colnames(de3),'3',sep='_')
de <- cbind(de1, de2, de3, diffexpressed=diffexpressed)
} else {
de <- cbind(de1, de2, diffexpressed=diffexpressed)
}
de$saturated <- de$saturated_1 | de$saturated_2
de$delabel <- NA
if(is.null(gene_labels)) {
de$delabel[de$diffexpressed != 0] <- rownames(de)[de$diffexpressed != 0]
} else {
idx <- rownames(de) %in% gene_labels
de$delabel[idx] <- rownames(de)[idx]
}
n_gene_class <- table(de$diffexpressed)
# trend lines
if( length(which(diffexpressed == 1)) < 2 ) blue_trend <- FALSE
if( length(which(diffexpressed == 2)) < 2 ) yellow_trend <- FALSE
if( blue_trend ) {
# calculate lm based on x-sum-of-squares
blue_coef <- coef(with(subset(de, diffexpressed == 1),
lm(log2FoldChange_2~log2FoldChange_1)))
}
if( yellow_trend ) {
# calculate lm based on y-sum-of-squares (and then invert the slope)
yellow_coef <- coef(with(subset(de, diffexpressed == 2),
lm(log2FoldChange_1~log2FoldChange_2)))
}
assign('log2log2de', de, envir = globalenv())
# plot adding up all layers we have seen so far
g <- ggplot(data=de, aes(x=log2FoldChange_1, y=log2FoldChange_2,
col=diffexpressed, label=delabel, shape=saturated)) +
geom_point(size=1.5) +
# geom_bin2d() +
geom_point(data = subset(de, diffexpressed != 0),
aes(x=log2FoldChange_1, y=log2FoldChange_2,
col=diffexpressed, shape=saturated))
if( blue_trend ) g <- g +
geom_abline(intercept = blue_coef[1],
slope = blue_coef[2],
color = 'royalblue4') #'dodgerblue4')
if( yellow_trend ) g <- g +
geom_abline(intercept = yellow_coef[1],
slope = 1/yellow_coef[2],
color = 'darkgoldenrod')
g <- g + theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.5)) +
geom_text_repel(max.overlaps=max.overlaps, size = 3, colour = "black", seed=seed) +
scale_x_continuous(expand = expansion(mult = .015)) +
scale_y_continuous(expand = expansion(mult = c(.01, .015))) +
geom_vline(xintercept=c(-l2fc_thr_min, l2fc_thr_min), linetype="dashed") +
geom_hline(yintercept=c(-l2fc_thr_min, l2fc_thr_min), linetype="dashed") +
scale_color_discrete(name = "Gene class",
labels = c(
paste0("no-degs (",n_gene_class[1],")"),
paste0("x-degs (",n_gene_class[2],")"),
paste0("y-degs (",n_gene_class[3],")"),
paste0("same (",n_gene_class[4],")"),
paste0("opposite (",n_gene_class[5],")")
)[n_gene_class>0],
type=color_scale[n_gene_class>0])
# if( global_cor ) {
# annotations <- data.frame(
# xpos = c(Inf),
# ypos = c(Inf),
# annotateText = as.character(round(cor(de$log2FoldChange_1, de$log2FoldChange_2, method='s'),2)),
# hjustvar = c(1) ,
# vjustvar = c(1))
# g <- g +
# geom_text(data=annotations,aes(x=xpos,y=ypos,hjust=hjustvar,vjust=vjustvar,label=annotateText))
# }
#
# if(is.finite(l2fc_thr_max)) {
# g <- g + xlim(-l2fc_thr_max, l2fc_thr_max) + ylim(-l2fc_thr_max, l2fc_thr_max)
# }
return(g)
}
gene_class <- function(resLFC1, resLFC2, alpha = .1, l2fc_thr_min=0) {
merged_df <- merge(data.frame(resLFC1[,c('log2FoldChange','padj')]),
data.frame(resLFC2[,c('log2FoldChange','padj')]), by = 0, all=TRUE)
resLFC1 <- merged_df[,2:3]; rownames(resLFC1) <- merged_df[,1];
colnames(resLFC1) <- sub('\\..*','',colnames(resLFC1))
resLFC2 <- merged_df[,4:5]; rownames(resLFC2) <- merged_df[,1];
colnames(resLFC2) <- sub('\\..*','',colnames(resLFC2))
resLFC1$padj[is.na(resLFC1$padj)] <- 1; resLFC1$log2FoldChange[is.na(resLFC1$log2FoldChange)] <- 0
resLFC2$padj[is.na(resLFC2$padj)] <- 1; resLFC2$log2FoldChange[is.na(resLFC2$log2FoldChange)] <- 0
signif1 <- resLFC1$padj < alpha & abs(resLFC1$log2FoldChange) > l2fc_thr_min
signif2 <- resLFC2$padj < alpha & abs(resLFC2$log2FoldChange) > l2fc_thr_min
logFCsign1 <- resLFC1$log2FoldChange > 0
logFCsign2 <- resLFC2$log2FoldChange > 0
sign1pos <- signif1 & !signif2 & logFCsign1 # 1
sign2pos <- !signif1 & signif2 & logFCsign2 # 2
sign1neg <- signif1 & !signif2 & !logFCsign1 # 3
sign2neg <- !signif1 & signif2 & !logFCsign2 # 4
pospos <- signif1 & signif2 & logFCsign1 & logFCsign2 # 5
posneg <- signif1 & signif2 & logFCsign1 & !logFCsign2 # 6
negneg <- signif1 & signif2 & !logFCsign1 & !logFCsign2 # 7
negpos <- signif1 & signif2 & !logFCsign1 & logFCsign2 # 8
gene_class <- rep(0,nrow(resLFC1)) # both no degs
gene_class[sign1pos|sign1neg] <- 1 # x-degs
gene_class[sign2pos|sign2neg] <- 2 # y-degs
gene_class[pospos|negneg] <- 3 # concordant
gene_class[posneg|negpos] <- 4 # discordant
gene_class <- factor(gene_class, levels=0:4)
return(data.frame(class=gene_class, row.names = rownames(resLFC1)))
}
# gene_class <- function(resLFC1, resLFC2, alpha = .1, l2fc_thr_min=0) {
# resLFC1$padj[is.na(resLFC1$padj)] <- 1
# resLFC2$padj[is.na(resLFC2$padj)] <- 1
# signif_genes <- 1*(resLFC1$padj < alpha) + 1*(resLFC2$padj < alpha)
# signif1 <- resLFC1$padj < alpha & abs(resLFC1$log2FoldChange) > l2fc_thr_min
# signif2 <- resLFC2$padj < alpha & abs(resLFC2$log2FoldChange) > l2fc_thr_min
# logFCsign1 <- resLFC1$log2FoldChange > 0
# logFCsign2 <- resLFC2$log2FoldChange > 0
#
# sign1pos <- signif1 & !signif2 & logFCsign1 # 1
# sign2pos <- !signif1 & signif2 & logFCsign2 # 2
# sign1neg <- signif1 & !signif2 & !logFCsign1 # 3
# sign2neg <- !signif1 & signif2 & !logFCsign2 # 4
# pospos <- signif1 & signif2 & logFCsign1 & logFCsign2 # 5
# posneg <- signif1 & signif2 & logFCsign1 & !logFCsign2 # 6
# negneg <- signif1 & signif2 & !logFCsign1 & !logFCsign2 # 7
# negpos <- signif1 & signif2 & !logFCsign1 & logFCsign2 # 8
#
# gene_class <- rep(0,nrow(resLFC1))
# gene_class[sign1pos|sign1neg] <- 1
# gene_class[sign2pos|sign2neg] <- 2
# gene_class[pospos|negneg] <- 3
# gene_class[posneg|negpos] <- 4
#
# gene_class <- factor(gene_class, levels=0:4)
# return(data.frame(class=gene_class, row.names = rownames(resLFC1)))
#
# # palette <- c('black','blue','gold','forestgreen','red')
# # cols <- palette[1+gene_class]
# #
# # return(data.frame(class=gene_class, col=cols, row.names = rownames(resLFC1)))
#
# }
# comparisons <- c(
# "condition_SOD_45_vs_WT_45",
# "condition_SOD_90_vs_WT_90",
# "condition_SOD_120_vs_WT_120",
# "condition_SOD_135_vs_WT_135"
# )
# genotypic_res <- lapply(comparisons, function(name) makeComparison(name, dds_endo_TRAP))
classicFpkm <- function(dds) {
stopifnot('libsize' %in% names(colData(dds)))
stopifnot('basepairs' %in% names(rowData(dds)))
t(t(counts(dds)/rowData(dds)$basepairs)/dds$libsize)*10^9
}
makeComparison <- function(name, obj, shrink=FALSE) {
ref <- strsplit(name, split = '_vs_')[[1]][2]
obj$condition <- relevel(obj$condition, ref = ref)
obj <- DESeq(obj)
if( shrink ) lfcShrink(obj, coef = name) else results(obj, name = name)
}
makeStrictComparisonOld <- function(name, obj,
counts_filter = -1,
fpkms_filter = -1,
mean_counts_filter = -1,
mean_fpkms_filter = -1,
f = any,
robustSF = FALSE,
shrink=FALSE) {
fac <- strsplit(name, split = '_')[[1]][1]
cnd <- strsplit(sub(paste0(fac, '_'), '', name), split = '_vs_')[[1]][1]
ref <- strsplit(name, split = '_vs_')[[1]][2]
obj <- obj[,obj[[fac]] %in% c(cnd, ref)]
obj[[fac]] <- factor(as.character(obj[[fac]]), levels = c(ref, cnd))
if(robustSF) {
obj <- estimateSizeFactors(obj)
} else {
if(is.null(sizeFactors(obj))) {
print('sizeFactors not set, computing now for the given dds (obj)')
} else {
print('Using pre-computed sizeFactors, if you want to re-evaluate them put robustSF to TRUE')
}
}
ref_fpkms <- fpkm(obj)[,obj[[fac]] == ref, drop=F]
cnd_fpkms <- fpkm(obj)[,obj[[fac]] == cnd, drop=F]
mean_fpkms <- cbind(rowMeans(ref_fpkms), rowMeans(cnd_fpkms))
ref_counts <- counts(obj)[,obj[[fac]] == ref, drop=F]
cnd_counts <- counts(obj)[,obj[[fac]] == cnd, drop=F]
mean_counts <- cbind(rowMeans(ref_counts), rowMeans(cnd_counts))
idx <-
(apply(ref_counts>counts_filter,1,f) |
apply(cnd_counts>counts_filter,1,f)) &
(apply(ref_fpkms>fpkms_filter,1,f) |
apply(cnd_fpkms>fpkms_filter,1,f)) &
apply(mean_fpkms>mean_fpkms_filter,1,f) &
apply(mean_counts>mean_counts_filter,1,f)
obj <- obj[idx,]
# in the robust case, re-estimate sizeFactors after the filtering
if(robustSF) obj <- estimateSizeFactors(obj)
obj <- DESeq(obj)
if( shrink ) lfcShrink(obj, coef = name) else results(obj, name = name)
}
makeStrictComparison <- function(name, obj,
counts_filter = -1,
fpkms_filter = -1,
mean_counts_filter = -1,
mean_fpkms_filter = -1,
f = any,
shrink=FALSE) {
fac <- strsplit(name, split = '_')[[1]][1]
cnd <- strsplit(sub(paste0(fac, '_'), '', name), split = '_vs_')[[1]][1]
ref <- strsplit(name, split = '_vs_')[[1]][2]
obj <- obj[,obj[[fac]] %in% c(cnd, ref)]
obj[[fac]] <- factor(as.character(obj[[fac]]), levels = c(ref, cnd))
ref_fpkms <- classicFpkm(obj)[,obj[[fac]] == ref, drop=F]
cnd_fpkms <- classicFpkm(obj)[,obj[[fac]] == cnd, drop=F]
mean_fpkms <- cbind(rowMeans(ref_fpkms), rowMeans(cnd_fpkms))
ref_counts <- counts(obj)[,obj[[fac]] == ref, drop=F]
cnd_counts <- counts(obj)[,obj[[fac]] == cnd, drop=F]
mean_counts <- cbind(rowMeans(ref_counts), rowMeans(cnd_counts))
idx <-
(apply(ref_counts>counts_filter,1,f) |
apply(cnd_counts>counts_filter,1,f)) &
(apply(ref_fpkms>fpkms_filter,1,f) |
apply(cnd_fpkms>fpkms_filter,1,f)) &
apply(mean_fpkms>mean_fpkms_filter,1,f) &
apply(mean_counts>mean_counts_filter,1,f)
obj <- obj[idx,]
# estimate sizeFactors before testing filtering
obj <- estimateSizeFactors(obj)
obj <- DESeq(obj)
if( shrink ) lfcShrink(obj, coef = name) else results(obj, name = name)
}
makeStableComparison <- function(name, obj,
counts_filter = -1,
fpkms_filter = -1,
mean_counts_filter = -1,
mean_fpkms_filter = -1,
f = any,
robustSF = FALSE,
shrink=FALSE) {
if(robustSF) {
obj <- estimateSizeFactors(obj)
} else {
if(is.null(sizeFactors(obj))) {
print('sizeFactors not set, computing now for the given dds (obj)')
} else {
print('Using pre-computed sizeFactors, if you want to re-evaluate them put robustSF to TRUE')
}
}
fac <- strsplit(name, split = '_')[[1]][1]
cnd <- strsplit(sub(paste0(fac, '_'), '', name), split = '_vs_')[[1]][1]
ref <- strsplit(name, split = '_vs_')[[1]][2]
#
ref_fpkms <- fpkm(obj)[,obj[[fac]] == ref, drop=F]
cnd_fpkms <- fpkm(obj)[,obj[[fac]] == cnd, drop=F]
mean_fpkms <- cbind(rowMeans(ref_fpkms), rowMeans(cnd_fpkms))
ref_counts <- counts(obj)[,obj[[fac]] == ref, drop=F]
cnd_counts <- counts(obj)[,obj[[fac]] == cnd, drop=F]
mean_counts <- cbind(rowMeans(ref_counts), rowMeans(cnd_counts))
#
idx <-
(apply(ref_counts>counts_filter,1,f) |
apply(cnd_counts>counts_filter,1,f)) &
(apply(ref_fpkms>fpkms_filter,1,f) |
apply(cnd_fpkms>fpkms_filter,1,f)) &
apply(mean_fpkms>mean_fpkms_filter,1,f) &
apply(mean_counts>mean_counts_filter,1,f)
# subset object (after fpkm calculation not to alter them)
obj <- obj[idx,obj[[fac]] %in% c(cnd, ref)]
obj[[fac]] <- factor(as.character(obj[[fac]]), levels = c(ref, cnd))
# in the robust case, re-estimate sizeFactors after the filtering
if(robustSF) obj <- estimateSizeFactors(obj)
obj <- DESeq(obj)
if( shrink ) lfcShrink(obj, coef = name) else results(obj, name = name)
}
library(ggplot2)
library(ggrepel)
plotPCA.san <- function (object, intgroup = "condition", ntop = 500,
labels = NA, max.overlaps = 10,
fix.coord = TRUE, returnData = FALSE)
{
rv <- rowVars(assay(object))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop,
length(rv)))]
pca <- prcomp(t(assay(object)[select, ]))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
if (!all(intgroup %in% names(colData(object)))) {
stop("the argument 'intgroup' should specify columns of colData(dds)")
}
intgroup.df <- as.data.frame(colData(object)[, intgroup, drop = FALSE])
group <- if (length(intgroup) > 1) {
factor(apply(intgroup.df, 1, paste, collapse = " : "))
}
else {
colData(object)[[intgroup]]
}
if(all(is.na(labels))) {
labels <- rownames(colData(object))
}
if(is.logical(labels)) {
if(!labels) labels <- rep('', ncol(object)) else labels <- rownames(colData(object))
}
stopifnot(length(labels)==length(rownames(colData(object))))
## Select the PCAs and percentVar that you like instead of 1 and 2
d <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], group = group,
intgroup.df, name = labels)
if (returnData) {
attr(d, "percentVar") <- percentVar[1:2]
return(d)
} else {
p <- ggplot(data = d, aes_string(x = "PC1", y = "PC2", color = "group", label = "name")) +
geom_point(size = 3) +
xlab(paste0("PC1: ", round(percentVar[1] * 100), "% variance")) +
ylab(paste0("PC2: ", round(percentVar[2] * 100), "% variance")) +
geom_text_repel(size=3, max.overlaps = max.overlaps)
if(fix.coord) p <- p + coord_fixed()
return(p)
}
}
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
ggplotColours <- function(n = 6, h = c(0, 360) + 15){
if ((diff(h) %% 360) < 1) h[2] <- h[2] - 360/n
hcl(h = (seq(h[1], h[2], length = n)), c = 100, l = 65)
}
enrichPlot <- function(filename, sheet, order.by='n', FUN='max',
show.labels=TRUE) {
enrich <- read.xlsx(filename, sheet = sheet)
enrich$n <- as.numeric(sapply(strsplit(enrich$Overlap,'\\/'),'[',1))
enrich$ratio <- enrich$n/as.numeric(sapply(strsplit(enrich$Overlap,'\\/'),'[',2))
colnames(enrich)[colnames(enrich) == "Adjusted.P-value"] <- 'p_adjusted'
colnames(enrich)[colnames(enrich) == "Summary.Term"] <- 'summary_term'
colnames(enrich)[grep('selected.genes', colnames(enrich))] <- 'summary_genes'
enrich$p_adjusted <- as.numeric(enrich$p_adjusted)
enrich$summary_term <- replace_na(enrich$summary_term)
enrich$summary_genes <- if(show.labels) replace_na(enrich$summary_genes) else NA
enrich_top1 <- enrich[,c('summary_term','Term','n','ratio','p_adjusted','summary_genes')]
enrich_top1 <- do.call('rbind', lapply(split(enrich_top1, enrich_top1$summary_term),
function(x) {
y <- x[,order.by]
ind <- which(y == do.call(FUN, as.list(y)))[1]
x[ind,]
}))
enrich_top1 <- enrich_top1[order(enrich_top1$n),]
enrich_top1$seq <- 1:nrow(enrich_top1)
gg <- ggplot(enrich_top1,
aes(x=n,
y=seq,
size = ratio,
label = if(show.labels) summary_genes else NULL,
color = -log10(p_adjusted))) + geom_point(alpha=0.7) + scale_x_continuous(trans='log2')
if(show.labels) gg <- gg + geom_text_repel(aes(x=n, y=seq), size=2.5, hjust=-.2, colour='black')
gg <- gg + scale_y_continuous(breaks=enrich_top1$seq, labels = enrich_top1$summary_term)
gg <- gg + scale_color_gradient(low="blue", high="red") + scale_size_area()
gg
}
# splitted_genes <- sapply(split(enrich$Genes, enrich$summary_term),
# function(x) strsplit(x, split=';|,'))
# splitted_genes <- splitted_genes[unique(enrich$summary_term)]
# sorted_Genes <- lapply(splitted_genes, function(x) {
# N <- length(x)
# tabled_genes <- factor(table(unlist(x)), levels=1:N)
# rev(sapply(split(names(tabled_genes), tabled_genes), paste, collapse=';'))
# })
# enrich$sorted_N <- unlist(sapply(sorted_Genes, function(x) length(x):1))
# enrich$sorted_Genes <- unlist(sorted_Genes)
# write.csv(enrich[,c('summary_term','sorted_N','sorted_Genes')], file='')
replace_na <- function(x) {
for(i in 1:length(x))
if(is.na(x[i])) x[i] <- x[i-1]
return(x)
}
volcanoPlot <- function(de,
l2fc_thr=c(0.6, Inf),
padj_thr=c(1e-1, -Inf),
expr_filter=NULL,
gene_labels = NULL,
geom_point_size = 1.5,
max.overlaps = 10,
seed = 1,
color_scale = c("dodgerblue4", "grey50", "firebrick")
) {
de <- data.frame(de)
l2fc_thr_min <- l2fc_thr[1]
l2fc_thr_max <- l2fc_thr[2]
padj_thr_min <- padj_thr[1]
padj_thr_max <- padj_thr[2]
de$diffexpressed <- "NO"
de$diffexpressed[de$log2FoldChange > l2fc_thr_min & de$padj < padj_thr_min] <- "UP"
de$diffexpressed[de$log2FoldChange < -l2fc_thr_min & de$padj < padj_thr_min] <- "DOWN"
de$diffexpressed <- factor(de$diffexpressed, levels = c("DOWN","NO","UP"))
if(!is.null(expr_filter)) de$diffexpressed[!expr_filter] <- "NO"
satFCup <- de$log2FoldChange > l2fc_thr_max
de$log2FoldChange[satFCup] <- l2fc_thr_max
satFCdn <- de$log2FoldChange < -l2fc_thr_max
de$log2FoldChange[satFCdn] <- -l2fc_thr_max
satPval <- de$padj < padj_thr_max
de$padj[satPval] <- padj_thr_max
de$saturated <- satFCup | satFCdn | satPval
de$saturated[is.na(de$saturated)] <- FALSE
de$delabel <- NA
if(is.null(gene_labels)) {
de$delabel[de$diffexpressed != "NO"] <- rownames(de)[de$diffexpressed != "NO"]
} else {
idx <- rownames(de) %in% gene_labels
de$delabel[idx] <- rownames(de)[idx]
}
n_gene_class <- table(de$diffexpressed)
assign('volcanoPlotde', de, envir = globalenv())
if(!is.finite(l2fc_thr_max)) l2fc_thr_max <- max(abs(de$log2FoldChange), na.rm=TRUE)
if(!is.finite(padj_thr_max)) padj_thr_max <- min(de$padj, na.rm=TRUE)
# plot adding up all layers we have seen so far
ggplot(data=de, aes(x=log2FoldChange, y=-log10(padj),
col=diffexpressed, label=delabel, shape=saturated)) +
geom_point(size=geom_point_size) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.5)) +
geom_text_repel(max.overlaps=max.overlaps, size = 3, colour = "black", seed=seed) +
scale_x_continuous(expand = expansion(mult = .015),
limits = c(-l2fc_thr_max, l2fc_thr_max)) +
scale_y_continuous(expand = expansion(mult = c(.01, .015)),
limits = c(0, -log10(padj_thr_max))) +
geom_vline(xintercept=c(-l2fc_thr_min, l2fc_thr_min), linetype="dashed") +
geom_hline(yintercept=-log10(padj_thr_min), linetype="dashed") +
scale_color_discrete(name = "Gene class",
labels = c(
paste0("Depleted (",n_gene_class[1],")"),
paste0("Neutral (",n_gene_class[2],")"),
paste0("Enriched (",n_gene_class[3],")")
)[n_gene_class>0],
type=color_scale[n_gene_class>0])
}
volcanoPlot2 <- function(de,
l2fc_thr=c(0.6, Inf),
padj_thr=c(1e-1, -Inf),
expr_filter=NULL,
gene_labels = NULL,
geom_point_size = 1.5,
max.overlaps = 10,
seed = 1,
color_scale = c("dodgerblue4", "grey50", "firebrick","gold")
) {
de <- data.frame(de)
l2fc_thr_min <- l2fc_thr[1]
l2fc_thr_max <- l2fc_thr[2]
padj_thr_min <- padj_thr[1]
padj_thr_max <- padj_thr[2]
de$diffexpressed <- "NO"
de$diffexpressed[de$log2FoldChange > l2fc_thr_min & de$padj < padj_thr_min] <- "UP"
de$diffexpressed[de$log2FoldChange < -l2fc_thr_min & de$padj < padj_thr_min] <- "DOWN"
de$diffexpressed <- factor(de$diffexpressed, levels = c("DOWN","NO","UP","LABEL"))
if(!is.null(expr_filter)) de$diffexpressed[!expr_filter] <- "NO"
satFCup <- de$log2FoldChange > l2fc_thr_max
de$log2FoldChange[satFCup] <- l2fc_thr_max
satFCdn <- de$log2FoldChange < -l2fc_thr_max
de$log2FoldChange[satFCdn] <- -l2fc_thr_max
satPval <- de$padj < padj_thr_max
de$padj[satPval] <- padj_thr_max
de$saturated <- satFCup | satFCdn | satPval
de$saturated[is.na(de$saturated)] <- FALSE
de$size <- geom_point_size
de$delabel <- NA
if(!is.null(gene_labels)) {
de[gene_labels,'diffexpressed'] <- 'LABEL'
de[gene_labels,'size'] <- geom_point_size * 2
de[gene_labels,'delabel'] <- gene_labels
}
n_gene_class <- table(de$diffexpressed)
assign('volcanoPlotde', de, envir = globalenv())
# plot adding up all layers we have seen so far
ggplot(data=de, aes(x=log2FoldChange, y=-log10(padj), label=delabel,
col=diffexpressed, shape=saturated, size=size)) +
geom_point() +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.5)) +
geom_text_repel(max.overlaps=max.overlaps, size = 4, colour = "black", seed=seed) +
scale_x_continuous(expand = expansion(mult = .015)) +
scale_y_continuous(expand = expansion(mult = c(.01, .015))) +
geom_vline(xintercept=c(-l2fc_thr_min, l2fc_thr_min), linetype="dashed") +
geom_hline(yintercept=-log10(padj_thr_min), linetype="dashed") +
scale_color_discrete(name = "Gene class",
labels = c(
paste0("Depleted (",n_gene_class[1],")"),
paste0("Neutral (",n_gene_class[2],")"),
paste0("Enriched (",n_gene_class[3],")"),
paste0("Labeled (",n_gene_class[4],")")
)[n_gene_class>0],
type=color_scale[n_gene_class>0]) +
guides(size = FALSE)
}
volcanoPlot3 <- function(de,
l2fc_thr=c(0.6, Inf),
padj_thr=c(1e-1, -Inf),
expr_filter=NULL,
gene_labels = NULL,
geom_point_size = 1.5,
max.overlaps = 10,
seed = 1,
color_scale = c("dodgerblue4", "grey50", "firebrick",
"lightblue","orange")
) {
de <- data.frame(de)
l2fc_thr_min <- l2fc_thr[1]
l2fc_thr_max <- l2fc_thr[2]
padj_thr_min <- padj_thr[1]
padj_thr_max <- padj_thr[2]
de$diffexpressed <- "NO"
de$diffexpressed[de$log2FoldChange > l2fc_thr_min & de$padj < padj_thr_min] <- "UP"
de$diffexpressed[de$log2FoldChange < -l2fc_thr_min & de$padj < padj_thr_min] <- "DOWN"
de$diffexpressed <- factor(de$diffexpressed, levels = c("DOWN","NO","UP","DOWN-SOMA","UP-SOMA"))
if(!is.null(expr_filter)) de$diffexpressed[!expr_filter] <- "NO"
satFCup <- de$log2FoldChange > l2fc_thr_max
de$log2FoldChange[satFCup] <- l2fc_thr_max
satFCdn <- de$log2FoldChange < -l2fc_thr_max
de$log2FoldChange[satFCdn] <- -l2fc_thr_max
satPval <- de$padj < padj_thr_max
de$padj[satPval] <- padj_thr_max
de$saturated <- satFCup | satFCdn | satPval
de$saturated[is.na(de$saturated)] <- FALSE
# de$size <- geom_point_size
de$delabel <- NA
if(!is.null(gene_labels)) {
gene_labels <- intersect(gene_labels, rownames(de)[de$diffexpressed != 'NO'])
de[gene_labels,'diffexpressed'] <- paste(de[gene_labels,'diffexpressed'],'SOMA',sep='-')
# de[gene_labels,'size'] <- geom_point_size
# de[gene_labels,'delabel'] <- gene_labels
}
n_gene_class <- table(de$diffexpressed)
assign('volcanoPlotde', de, envir = globalenv())
# plot adding up all layers we have seen so far
ggplot(data=de, aes(x=log2FoldChange, y=-log10(padj), label=delabel,
col=diffexpressed, shape=saturated)) +
geom_point(size=geom_point_size) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "black", size=1.5)) +
geom_text_repel(max.overlaps=max.overlaps, size = 4, colour = "black", seed=seed) +
scale_x_continuous(expand = expansion(mult = .015)) +
scale_y_continuous(expand = expansion(mult = c(.01, .015))) +
geom_vline(xintercept=c(-l2fc_thr_min, l2fc_thr_min), linetype="dashed") +
geom_hline(yintercept=-log10(padj_thr_min), linetype="dashed") +
scale_color_discrete(name = "Gene class",
labels = c(
paste0("Depleted (",n_gene_class[1],")"),
paste0("Neutral (",n_gene_class[2],")"),
paste0("Enriched (",n_gene_class[3],")"),
paste0("Depleted - Soma DEGs (",n_gene_class[4],")"),
paste0("Enriched - Soma DEGs (",n_gene_class[5],")")
)[n_gene_class>0],
type=color_scale[n_gene_class>0])
}
mergeTwoRes <- function(res1, res2, cols, replaceNAby=NA, suffixes = c(".x",".y")) {
l2fcmat <- merge(data.frame(res1[,unlist(sapply(cols, grep, colnames(res1))),drop=F]),
data.frame(res2[,unlist(sapply(cols, grep, colnames(res2))),drop=F]),
by = 0, all=TRUE, suffixes = suffixes)
rownames(l2fcmat) <- l2fcmat$Row.names
l2fcmat$Row.names <- NULL
l2fcmat[is.na(l2fcmat)] <- replaceNAby
return(l2fcmat)
}
degsMatOld <- function(genotypic_res, padj_thr_min=.1, log2fc_thr_min = 0) {
l2fcmat <- sapply(genotypic_res, function(x) x$log2FoldChange)
padjmat <- sapply(genotypic_res, function(x) x$padj)
degsmat <- 1*(padjmat<padj_thr_min & l2fcmat>log2fc_thr_min) -
1*(padjmat<padj_thr_min & l2fcmat<(-log2fc_thr_min))
degsmat[is.na(degsmat)] <- 0
rownames(degsmat) <- rownames(genotypic_res[[1]])
return(degsmat)
}
reportMat <- function(genotypic_res, l2fcNA=NA, padjNA=NA) {
l2fcmat <- mergeTwoRes(genotypic_res[[1]], genotypic_res[[2]],
'log2FoldChange', l2fcNA, suffixes = c('.1','.2'))
if(length(genotypic_res)>2)
for(i in 3:length(genotypic_res))
l2fcmat <- mergeTwoRes(l2fcmat, genotypic_res[[i]], 'log2FoldChange', l2fcNA,
suffixes = c('',paste0('.',i)))
colnames(l2fcmat) <- names(genotypic_res)
padjmat <- mergeTwoRes(genotypic_res[[1]], genotypic_res[[2]],
'padj', padjNA, suffixes = c('.1','.2'))
if(length(genotypic_res)>2)
for(i in 3:length(genotypic_res))
padjmat <- mergeTwoRes(padjmat, genotypic_res[[i]], 'padj', padjNA,
suffixes = c('',paste0('.',i)))
colnames(padjmat) <- names(genotypic_res)
return(list(l2fcmat=l2fcmat, padjmat=padjmat))
}
degsMat <- function(genotypic_res, padj_thr_min=.1, log2fc_thr_min = 0) {
repmat <- reportMat(genotypic_res, l2fcNA=0, padjNA=1)
padjmat <- repmat$padjmat
l2fcmat <- repmat$l2fcmat
degsmat <- 1*(padjmat<padj_thr_min & l2fcmat>log2fc_thr_min) -
1*(padjmat<padj_thr_min & l2fcmat<(-log2fc_thr_min))