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ProcessVast.R
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ProcessVast.R
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#!/usr/bin/env Rscript
### Work up vast-tools output, save tables and do a few analysis of changing events
###
### U. Braunschweig 2019-2024
###
### Changes: New option --vastMain
cArgs <- commandArgs(TRUE)
## Check dependencies
libMissing <- !require("optparse", quietly=T) && stop("Failed to load R package 'optparse'")
### Capture input
opt.list <- list(
make_option(c("-s", "--sampleTab"), action="store", type="character", metavar="CSVFILE",
help="Sample table in CSV format with, at least, columns Sample (an arbitrary, unique name),
and Type (treatment etc.)"),
make_option(c("-c", "--contrTab"), action="store", type="character", metavar="CSVFILE",
help="Contrasts table in CSV format with, at least, columns Experimental, Control, and File."),
make_option(c("-p", "--specEventTable"),action="store", type="character", metavar="CSVFILE",
help="Optional table indicating specific events to be used for an analysis of changing
events. Required are a colsumn EVENTS and one or more other columns containing a string
annotation such as 'neural-HIGH', 'neural-LOW' etc."),
make_option(c("-v", "--vastDir"), action="store", default="./vast", metavar="DIR",
help="Directory with vast-tools output [%default]"),
make_option(c("-I", "--vastMain"), action="store", type="character", metavar="PATH",
help="vast-tools main INCLUSION... table [default: ProcessVast will attempt to find the table]"),
make_option(c("-o", "--outDir"), action="store", default="./splicing", metavar="DIR",
help="Output directory. Will be created if it does not exist [%default]"),
make_option("--continue", action="store_true", default="FALSE",
help="Try to locate saved output tables in --outDir and load them rather then doing full analysis"),
make_option(c("-E", "--vast2EnsemblGene"),action="store", type="character", metavar="FILE",
help="Optional tab-delimited file with required for outputting files for GO analysis,
with at least columns EVENT and EnsemblGeneID"),
make_option("--noGO", action="store_true", default="FALSE",
help="Do not save files for GO analysis."),
make_option(c("-m", "--minRepFrac"), action="store", default=0.5, metavar="REAL",
help="Minimum fraction of replicates of each type that needs to survive filtering [%default]"),
make_option(c("-f", "--filter"), action="store", default="DEFAULT", metavar="DEFAULT|STRICT|LEGACY",
help="Read depth filtering and balance filtering for individual events and replicates.
DEFAULT: CE/MIC/Alt3/Alt5 depth must be SOK/OK/LOW/VLOW; CE balance OK/B1/B2/Bl/Bn; IR depth >= 10;
IR balance pval >= 0.05.
STRICT: CE/MIC/Alt3/Alt5 depth must be SOK/OK/LOW; CE balance OK/B1/B2/Bl/Bn; IR depth >= 15;
IR balance pval >= 0.05.
LEGACY: CE/MIC/Alt3/Alt5 depth must be SOK/OK/LOW; CE balance OK/B1; IR depth >= 15; IR balance pval >= 0.05.
This is the same as the previous default setting.
[%default]"),
make_option("--strictDiff", action="store_true", default="FALSE",
help="If set, significant events will be those where |dPSI| (p>0.95) is greater than 10.
By default, significant events will be those where |dPSI| (p>0.95) is greater
than 0 and the |dPSI| point estimate is greater than 5/10/15 (10 for plots)"),
make_option("--keepFullAlt", action="store_true", default="FALSE",
help="By default, Alt5 and Alt3 events will be reduced to one score for each event,
with 0 indicating full usage of the closest alternative splice site and 1 only the furthest.
When this option is set, tables will be stored as an .Robj that still contain the original
separate sub-events."),
make_option("--scatterForce", action="store_true", default="FALSE",
help="By default, if there are > 9 contrasts, mutual pairwise scatterplots of dPSI by event
type are suppressed. Set this flag to force plotting."),
make_option("--colors", action="store",
default="dodgerblue,grey50,darkmagenta,darkorange1,darkolivegreen2,darkslategray4,burlywood3,cyan3,darkgoldenrod3,firebrick3,navy,seagreen4,darkgoldenrod1,burlywood1,grey80,deeppink,mediumpurple1,yellow2",
metavar="NUM",
help="Colors per sample type for plotting. Sample of type 'Other' automatically gets color 'grey80'
unless this is taken [%default]"),
make_option(c("-C", "--cores"), action="store", default=1, metavar="INT",
help="CPUs to use [%default]")
)
opt <- parse_args(OptionParser(
usage = "Filter vast-tools results, average replicates, generate tables, analyze differential events.
ProcessVast -s <SampleTable> -c <ContrastTable> [OPTIONS]",
option_list=opt.list),
args=cArgs)
libMissing <- !require("gplots", quietly=T) && stop("Failed to load R package 'gplots'")
libMissing <- !require("parallel", quietly=T) && {
warning("Failed to load R package 'parallel', running with a single core.")
opt$cores <- 1
}
### Constants ###
maxPlotContr <- 9
### Function defs ###
saveLog <- function(logName, opt, sampleTab, contrTab) {
### Save a log file
titl <- "*** Processing vast-tools results ***\n\n== Options =="
if (opt$continue) {
selOpt <- which(names(opt) %in% c("sampleTab","contrTab","vast2EnsemblGene","specEventTable","continue",
"scatterForce","outDir","noGO")
)
} else {
selOpt <- which(names(opt) %in% c("sampleTab","contrTab","vast2EnsemblGene","specEventTable","continue",
"strictDiff","keepFullAlt","minRepFrac","filter",
"scatterForce","vastDir","outDir","noGO")
)
}
optTable <- data.frame(option = names(opt)[selOpt],
value = unlist(opt[selOpt]),
stringsAsFactors=F
)
optTable$option <- paste0(optTable$option,
sapply(max(nchar(optTable$option)) + 1 - nchar(optTable$option), FUN=function(x) {
paste(rep(" ", x), collapse="")
})
)
suppressWarnings({
write.table(titl,
row.names=F, col.names=F, quote=F, sep='\t',
file=logName)
write.table(paste(optTable$option, optTable$value, sep=": "),
row.names=F, col.names=F, quote=F, sep='\t',
file=logName, append=T)
write.table("\n== Samples ==",
row.names=F, col.names=F, quote=F, sep='\t',
file=logName, append=T)
write.table(sampleTab[,names(sampleTab) %in% c("Sample","Type")],
row.names=F, col.names=T, quote=F, sep='\t',
file=logName, append=T)
write.table("\n== Contrasts ==",
row.names=F, col.names=F, quote=F, sep='\t',
file=logName, append=T)
write.table(contrTab[,names(contrTab) %in% c("Experimental","Control","File")],
row.names=F, col.names=T, quote=F, sep='\t',
file=logName, append=T)
write.table("\n== Time stamp ==",
row.names=F, col.names=F, quote=F, sep='\t',
file=logName, append=T)
})
}
cleanAS <- function(x, # Quality column of one sample
complexCol, # Colum 'COMPLEX' of vast-tools output
covThresCE = c("SOK","OK","LOW"), # Allowable coverage score (score 3) for CE
covThresOther = c("SOK","OK","LOW"), # Allowable coverage score (score 3) for ME, Alt3, Alt5
covThresIR = 15, # Minimum coverage (score 3) for IR
balThresCE = c("OK","B1"), # Allowable balance score (score 4) for CE
balThresIR = 0.05 # Maximum balance p-value (score 4, bigger is better) for IR
) {
### Filter PSI output of vast-tools pipeline using the quality column
### Value: logical vector indicating which events passed the criteria
is.ce <- complexCol %in% c("S","C1","C2","C3","ANN")
is.ir <- grepl("IR", complexCol)
is.o <- complexCol %in% c("Alt5", "Alt3", "MIC")
score1 <- sub("([^,]+),.+", "\\1", x)
score3 <- sub("[^,]+,[^,]+,([^,]+),.+", "\\1", x)
score4 <- sub("[^,]+,[^,]+,[^,]+,([^,]+),.+", "\\1", x)
score5 <- sub("[^,]*,[^,]*,[^,]*,[^,]*,([^,@]*)[@,].*", "\\1", x)
oldVast <- ifelse(any(grepl("=", score3)), FALSE, TRUE)
cat("Vast-tools version", ifelse(oldVast, "<", ">="), "2.2.2 detected", "\n")
if (oldVast) { # The content of score4 has changed in vast-tools 2.2.2
ok.ce <- is.ce & score3 %in% covThresCE & score4 %in% balThresCE
ok.o <- is.o & score3 %in% covThresOther
} else {
ok.ce <- is.ce & score1 %in% covThresCE & score4 %in% balThresCE
ok.o <- is.o & score1 %in% covThresOther
}
suppressWarnings(
nreads <- as.integer(sub("([^=]*)=.+", "\\1", score4)) +
as.integer(sub("[^=]*=([^=]*)=.*", "\\1", score4)) +
as.integer(sub("[^=]*=[^=]*=(.*)", "\\1", score4))
)
suppressWarnings(
ok.ir <- is.ir & nreads >= covThresIR & as.numeric(score5) > balThresIR
)
ok.ce | ok.ir | ok.o
}
removeVastDupEvents <- function(vast) {
### For vast-tools version with duplicate event IDs, remove duplicates, prioritizing MIC events
cat("Duplicated vast-tools EVENT IDs found... removing.\n")
vast[!(vast$EVENT %in% vast$EVENT[duplicated(vast$EVENT)]),]
}
reshapeVast <- function(vast) {
### Re-introduce dummy 'quality' columns into filtered INCLUSION... table
tmp <- matrix(nrow=nrow(vast), ncol=(ncol(vast) - 6) * 2)
colnames(tmp) <- paste0(rep(names(vast)[-c(1:6)], each=2), c("","-Q"))
tmp[,seq(1, ncol(tmp) - 1, 2)] <- as.matrix(vast[,7:ncol(vast)])
data.frame(vast[,1:6], tmp, check.names=F)
}
mergeToVast <- function(x, events) {
### Merge DIFF tables to conform to the vast-tools main table
x <- merge(x, data.frame(EVENT=vast$EVENT, vastInd=1:nrow(vast)), by.x=2, by.y=1, all.y=T)
x <- x[order(x$vastInd), c(2,1,3:6)]
}
diffPointEst <- function(x, contrTab, diffres, sampleTab, vast, minFrac=0.5) {
### Extract PSI point estimates from DIFF tables, combine if multiple,
### and filter by minimum number of sample that survived quality filtering.
### x: sample type
cat(x, "\n")
tmp1 <- tmp2 <- diffres[[1]][,c()]
if (length(grep(x, contrTab$Experimental)) > 0) {
tmp1 <- sapply(which(contrTab$Experimental == x), FUN=function(y) {sapply(diffres[y], "[[", 3)})
}
if (length(grep(x, contrTab$Control)) > 0) {
tmp2 <- sapply(which(contrTab$Control == x), FUN=function(y) {sapply(diffres[y], "[[", 4)})
}
if (!is.numeric(tmp1)) {tmp1 <- as.numeric(rep(NA, nrow(tmp2)))}
if (!is.numeric(tmp2)) {tmp2 <- as.numeric(rep(NA, nrow(tmp1)))}
tmp <- 100 * rowMeans(cbind(tmp1, tmp2), na.rm=T)
filt <- vast[,sampleTab$vastCol[sampleTab$Type == x]]
if (any(is.null(dim(filt)))) {filt <- as.matrix(filt)}
minNotNa <- ceiling(ncol(filt) * minFrac)
tmp[apply(filt, MAR=1, FUN=function(y) {length(which(!is.na(y)))}) < minNotNa] <- NA
tmp
}
dpsiMake <- function(i, contrTab, uqContr, means) {
### Generate dPSI from DIFF point estimates
means[,which(uqContr == contrTab$Experimental[i])] - means[,which(uqContr == contrTab$Control[i])]
}
altCombinePSI <- function(vast, dat) {
### Combine mean PSI/PIR values of Alt5/Alt3 into a compound score
info <- data.frame(vast[,1:6], vastInd=1:nrow(vast))[order(vast$EVENT),]
dat <- as.matrix(dat[info$vastInd,])
info$EVENT <- sub("(.+ALT[AD][0-9]+)-.*", "\\1", info$EVENT)
if (all(c("Alt5","Alt3") %in% info$COMPLEX)) {
dat.a5 <- dat[info$COMPLEX == "Alt5",]
info.a5 <- info[info$COMPLEX == "Alt5",]
posInd.a5 <- altIndex(dat.a5, uniqEvent=info.a5$EVENT, aggreg="altInd")
info.a5 <- info.a5[nrow(info.a5):1,]
info.a5 <- info.a5[!duplicated(info.a5$EVENT),]
info.a5 <- info.a5[!is.na(info.a5$EVENT),]
info.a5 <- info.a5[nrow(info.a5):1, 1:6]
dat.a3 <- dat[info$COMPLEX == "Alt3",]
info.a3 <- info[info$COMPLEX == "Alt3",]
posInd.a3 <- altIndex(dat.a3, uniqEvent=info.a3$EVENT, aggreg="altInd")
info.a3 <- info.a3[nrow(info.a3):1,]
info.a3 <- info.a3[!duplicated(info.a3$EVENT),]
info.a3 <- info.a3[!is.na(info.a3$EVENT),]
info.a3 <- info.a3[nrow(info.a3):1, 1:6]
out <- rbind(data.frame(info[,-7], dat)[!(info$COMPLEX %in% c("Alt5","Alt3")),],
data.frame(info.a5, posInd.a5[,-1]),
data.frame(info.a3, posInd.a3[,-1])
)
} else {
out <- data.frame(info[,-7], dat)[!(info$COMPLEX %in% c("Alt5","Alt3")),]
}
type <- character(nrow(out))
type[out$COMPLEX %in% c("C1","C2","C3","S","ANN")] <- "CE"
type[grep("IR", out$COMPLEX)] <- "IR"
type[out$COMPLEX == "MIC"] <- "MIC"
type[out$COMPLEX == "Alt5"] <- "Alt5"
type[out$COMPLEX == "Alt3"] <- "Alt3"
type[is.na(out$COMPLEX) & grepl("EX[0-9]+", out$EVENT) & out$LENGTH > 27] <- "CE" # some events have no COMPLEX
type[is.na(out$COMPLEX) & grepl("EX[0-9]+", out$EVENT) & out$LENGTH <= 27] <- "MIC" #
type[type == "" & grepl("A_", out$COMPLEX)] <- "CE"
type[type == "CE" & out$LENGTH <= 27] <- "MIC"
data.frame(out[,1:6],
TYPE = type,
out[,7:ncol(out)]
)
}
altCombine.sig <- function(vast, diffsig) {
### Convert DIFF results such that for Alt5/Alt3, only the biggest MV.dPSI is taken
info <- data.frame(vast[,1:6], vastInd=1:nrow(vast))[order(vast$EVENT),]
dat <- as.matrix(diffsig[info$vastInd,])
info$EVENT <- sub("(.+ALT[AD][0-9]+)-.*", "\\1", info$EVENT)
if (all(c("Alt5","Alt3") %in% info$COMPLEX)) {
dat.a5 <- as.matrix(dat[info$COMPLEX == "Alt5",])
info.a5 <- info[info$COMPLEX == "Alt5",]
anySig.a5 <- altIndex(dat.a5, uniqEvent=info.a5$EVENT, aggreg="max")
rownames(anySig.a5) <- anySig.a5[,1]
dat.a3 <- as.matrix(dat[info$COMPLEX == "Alt3",])
info.a3 <- info[info$COMPLEX == "Alt3",]
anySig.a3 <- altIndex(dat.a3, uniqEvent=info.a3$EVENT, aggreg="max")
rownames(anySig.a3) <- anySig.a3[,1]
out <- rbind(as.matrix(dat[!(info$COMPLEX %in% c("Alt5","Alt3")),]),
as.matrix(anySig.a5[,-1]),
as.matrix(anySig.a3[,-1])
)
} else {
out <- as.matrix(dat[!(info$COMPLEX %in% c("Alt5","Alt3")),])
}
out
}
altIndex <- function(dat, uniqEvent, aggreg=c("altInd","max","any")[1]) {
posInd <- aggregate(dat,
by = list(uniqEvent),
FUN = switch(aggreg,
altInd = function(x) {mean(x[-1] * 1:(length(x) - 1))},
max = max,
any = any)
)
posInd[!(posInd[,1] %in% uniqEvent[duplicated(uniqEvent)]), -1] <- NA # only one subevent
posInd
}
orderEvents <- function(info) {
### Generate a sorting for events
ind <- numeric(nrow(info))
ind[info$TYPE == "CE"] <- 1
ind[info$TYPE == "MIC"] <- 2
ind[info$TYPE == "IR"] <- 3
ind[info$TYPE == "Alt5"] <- 4
ind[info$TYPE == "Alt3"] <- 5
order(ind, info$GENE, info$EVENT)
}
setRowCol <- function(x) {
### Select a layout for x panels
if (x == 1) ncol=1
if (x %in% c(2,4)) ncol=2
if (x %in% c(3,5,6,9)) ncol=3
if (x %in% c(7,8) | x > 9) ncol=4
nrow <- ceiling(x / ncol)
c(nrow, ncol)
}
changingEventTypes <- function(dpsi, sig, info, includeANN=T, main=NA, percentage=T,
cols=c("gold","dodgerblue"),
ylim=NA, cex.main=1.2) {
### Barplot of up- and downregulated events, by event type
### dpsi : vector of dPSI
### sig : logical vector indicating which dpsi are signficant
### eventCol : column EVENT from vast-tools splicing table
### complexCol : column COMPLEX from vast-tools splicing table
### lengthCol : column LENGTH -"-
### MICsize : maxium size for cassette exons to be classified as microexons
### includeANN : Include 'ANN' events, which are mostly constitutive exons, in counts for CE and MIC?
### main : (optional) title
### Changes: New option 'includeANN'
if (any(is.na(main) | length(main) != ncol(dpsi))) {main <- rep("", ncol(dpsi))}
if (!includeANN) {info$TYPE[info$COMPLEX == "ANN"] <- "ANN"}
## Count numbers of events
change <- array(dim=c(2, length(setdiff(unique(info$TYPE), NA)), ncol(dpsi)),
dimnames=list(c("up","down"), c("CE","MIC","Alt3","Alt5","IR"), c()))
allev <- matrix(nrow=ncol(dpsi), ncol=length(setdiff(unique(info$TYPE), NA)))
for (i in 1:ncol(dpsi)) {
change[1,,i] <- sapply(dimnames(change)[[2]],
FUN=function(x) {length(which(dpsi[sig[,i] & info$TYPE == x, i] > 0))})
change[2,,i] <- sapply(dimnames(change)[[2]],
FUN=function(x) {length(which(dpsi[sig[,i] & info$TYPE == x, i] < 0))})
allev[i,] <- sapply(dimnames(change)[[2]],
FUN=function(x) {length(which(!is.na(dpsi[info$TYPE == x, i])))})
}
if (percentage) {
ylab <- "% of changing events"
plotchange <- change
for (i in 1:ncol(dpsi)) {
plotchange[,,i] <- 100 * t(t(plotchange[,,i]) / allev[i,])
}
} else {
ylab <- "Number of changing events"
plotchange <- change
}
if (any(is.na(ylim))) {
ylim <- c(-max(plotchange[2,,], na.rm=T), max(plotchange[1,,], na.rm=T))
ylim[1] <- min(ylim[1], -0.1 * (ylim[2] - ylim[1])) # make sure the labels fit
ylim[2] <- max(ylim[2], 0.1 * (ylim[2] - ylim[1]))
}
## Plotting
rowCol <- setRowCol(ncol(dpsi))
par(mfrow=rowCol, mar=c(3,4,3,1))
for (i in 1:ncol(dpsi)) {
plot(1,1, type="n", bty="n", xaxt="n", xlim=c(0.3,7.7), ylim=ylim,
xlab="", ylab=ylab, main=main[i], cex.main=cex.main)
axis(1, at=seq(1, 7 ,1.5), tick=F, line=F, labels=colnames(change), cex.axis=1.2)
rect(seq(0.5, 6.5, 1.5), 0, seq(1.5, 7.5, 1.5), plotchange[1,,i], col=cols[1], border=cols[1])
rect(seq(0.5, 6.5, 1.5), -plotchange[2,,i], seq(1.5, 7.5, 1.5), 0, col=cols[2], border=cols[2])
abline(h=0)
text(seq(1, 7 ,1.5), 0.05 * (ylim[2] - ylim[1]), change[1,,i])
text(seq(1, 7 ,1.5), -0.05 * (ylim[2] - ylim[1]), change[2,,i])
par(xpd=NA)
text(4, ylim[2] + 0.06 * (ylim[2] - ylim[1]) ,
paste(format(sum(allev[i,]), big.mark=","), "events with coverage"))
par(xpd=F)
}
}
changingSpecEvents <- function(specEv, dpsi.i, sig.i, info, includeANN=T, percentage=T, main=NA,
ylim=NA, cex.main=1.2) {
### Plot the % of events among ones submitted in an extra file as bar graphs,
### with one panel per grouping (e.g. neural-differential)
specEv <- merge(data.frame(EVENT = info$EVENT, ind=1:nrow(info)),
specEv,
by.x=1, by.y=which(names(specEv) == "EVENT"), all.x=T)
specEv <- as.data.frame(specEv[order(specEv$ind),-c(1,2)])
## Count numbers of events
if (!includeANN) {info$TYPE[info$COMPLEX == "ANN"] <- "ANN"}
specKinds <- apply(specEv, MAR=2, getSpecKinds)
nSpecKinds <- sapply(specKinds, nrow)
dat <- lapply(1:ncol(specEv), FUN=function(x) {
change <- array(dim=c(2, length(setdiff(unique(info$TYPE), NA)), nSpecKinds[x]),
dimnames=list(c("up","down"), c("CE","MIC","Alt3","Alt5","IR"), specKinds[[x]]$kind))
allev <- matrix(nrow=nSpecKinds[x], ncol=length(setdiff(unique(info$TYPE), NA)))
for (i in 1:nSpecKinds[x]) {
change[1,,i] <- sapply(dimnames(change)[[2]],
FUN=function(y) {length(which(dpsi.i[sig.i & info$TYPE == y &
specEv[,x] == specKinds[[x]]$kind[i]] > 0))})
change[2,,i] <- sapply(dimnames(change)[[2]],
FUN=function(y) {length(which(dpsi.i[sig.i & info$TYPE == y &
specEv[,x] == specKinds[[x]]$kind[i]] < 0))})
allev[i,] <- sapply(dimnames(change)[[2]],
FUN=function(y) {length(which(!is.na(dpsi.i[info$TYPE == y &
specEv[,x] == specKinds[[x]]$kind[i]])))})
}
if (percentage) {
ylab <- "% of changing events"
plotchange <- change
for (i in 1:nSpecKinds[x]) {
plotchange[,,i] <- 100 * t(t(plotchange[,,i]) / allev[i,])
}
} else {
ylab <- "Number of changing events"
plotchange <- change
}
list(ylab = ylab,
change = change,
allev = allev,
plotchange = plotchange
)
})
if (any(is.na(ylim))) {
ylim <- c(-max(sapply(dat, FUN=function(x) {max(x$plotchange[2,,], na.rm=T)})),
max(sapply(dat, FUN=function(x) {max(x$plotchange[1,,], na.rm=T)})))
ylim[1] <- min(ylim[1], -0.1 * (ylim[2] - ylim[1])) # make sure the labels fit
ylim[2] <- max(ylim[2], 0.2 * (ylim[2] - ylim[1]))
}
specRowCol <- setRowCol(ncol(specEv))
par(mfrow=specRowCol, mar=c(3,4,3,1))
for (i in 1:ncol(specEv)) {
xpos <- seq(1, 7, 1.5)
xfine <- seq(0, 1, length.out=1 + nSpecKinds[i]) - 0.5
xnumb <- (xfine[-1] + xfine[-length(xfine)]) / 2
plot(1,1, type="n", bty="n", xaxt="n", xlim=c(0.3,7.7), ylim=ylim,
xlab="", ylab=dat[[i]]$ylab, cex.main=cex.main)
if (i == 1) {title(main=main, adj=0, cex.main=1.5)}
axis(1, at=xpos, tick=F, line=F, labels=colnames(dat[[1]]$change), cex.axis=1.2)
for (j in 1:nSpecKinds[i]) {
rect(xpos + xfine[j], 0, xpos + xfine[j + 1], dat[[i]]$plotchange[1,,j],
col=specKinds[[i]]$col[j], border=specKinds[[i]]$col[j])
rect(xpos + xfine[j], -dat[[i]]$plotchange[2,,j], xpos + xfine[j + 1], 0,
col=specKinds[[i]]$col[j], border=specKinds[[i]]$col[j])
text(xpos + xnumb[j], 0.03 * (ylim[2] - ylim[1]), dat[[i]]$change[1,,j], srt=90, adj=c(0,0.5))
text(xpos + xnumb[j], -0.03 * (ylim[2] - ylim[1]), dat[[i]]$change[2,,j], srt=90, adj=c(1,0.5))
text(xpos + xnumb[j], ylim[1], dat[[i]]$allev[j,], srt=90, adj=c(0,0.5))
}
abline(h=0)
legend("topright", legend=specKinds[[i]]$kind, fill=specKinds[[i]]$col, border="white", bty="n")
}
}
getSpecKinds <- function(specEv1) {
### Called by changingSpecEvents(), extracts the levels of a grouping and assigns colors
reds <- c("brown1","brown4")
blues <- c("dodgerblue","darkblue")
cols <- c("dodgerblue","darkmagenta","darkorange1","darkolivegreen2","darkslategray4","burlywood3",
"cyan3","darkgoldenrod3","firebrick3","navy")
kinds <- sort(unique(specEv1))
kinds <- data.frame(kind=kinds, col=NA)
kinds$col[grep("HIGH",kinds$kind)] <- colorRampPalette(reds)(length(grep("HIGH",kinds$kind)))
kinds$col[grep("LOW",kinds$kind)] <- colorRampPalette(blues)(length(grep("LOW",kinds$kind)))
if (any(is.na(kinds$col))) {
kinds$col[is.na(kinds$col)] <- rep(cols, ceiling(length(which(is.na(kinds$col))) / length(cols)))[
1:length(which(is.na(kinds$col)))]
}
kinds
}
scatterXY <- function(x,y, cols=c("dodgerblue","brown1","darkmagenta","darkolivegreen3")) {
x <- which(colnames(dpsi) == x)
y <- which(colnames(dpsi) == y)
notna <- !is.na(dpsi[,x]) & !is.na(dpsi[,y])
change <- which((diff10[,x] | diff10[,y]) & notna)
par(mfrow=c(1,5), mar=c(5,4,3,1))
for (i in unique(info$TYPE)) {
evType <- info$TYPE %in% i
use <- which(notna & evType)
lims <- range(as.numeric(c(dpsi[use, x], dpsi[use, y])), na.rm=T)
sigcols <- rep(NA, length(use))
sigcols[diff10[use, x]] <- cols[1]
sigcols[diff10[use, y]] <- cols[2]
sigcols[diff10[use, x] & diff10[use, y]] <- cols[3]
sigcols[diff10[use, x] & diff10[use, y] & sign(dpsi[use, x]) != sign(dpsi[use, y])] <- cols[4]
smoothScatter(dpsi[use[is.na(sigcols)],x], dpsi[use[is.na(sigcols)],y], pch=".", xlim=lims, ylim=lims,
colramp=colorRampPalette(c("white","grey60","grey10","grey3","black")),
bandwidth=c(0.05,0.05), nbin=500,
nrpoints=0, transformation=function(x) {x^0.15},
xlab=colnames(dpsi)[x], ylab=colnames(dpsi)[y])
points(dpsi[use,x], dpsi[use,y], pch=20, col=sigcols)
title(main=paste(format(length(use), big.mark=","), i, "events"), adj=1)
abline(a=0, b=1, lty=2)
legend("topleft",
legend=c("Significant changes (dPSI>10):",
paste(c(colnames(dpsi)[x], colnames(dpsi)[y],"both","divergent"), " (",
sapply(cols, FUN=function(x) {length(which(sigcols == x))}), ")", sep="")),
pch=20, col=c(NA,"dodgerblue","brown1","darkmagenta","darkolivegreen3"), bty="n")
legend("bottomright",
legend=paste("r =", round(cor(dpsi[use,x], dpsi[use,y], use="c"), 2)),
bty="n")
}
}
cleanV2G <- function(v2g, info) {
if (any(grepl("ALT[AD][0-9]+-[0-9]+/", v2g$EVENT))) {
v2g$EVENT <- sub("(.+ALT[AD][0-9]+)-.*", "\\1", v2g$EVENT)
v2g <- v2g[!duplicated(v2g$EVENT),]
}
v2g <- merge(data.frame(EVENT=info$EVENT, ind=1:nrow(info)),
v2g,
by.x=1, by.y=grep("EVENT", names(v2g)), all.x=T
)
v2g <- v2g[order(v2g$ind), -2]
if (length(which(sub("-[0-9]+/[0-9]+", "", info$EVENT[info$COMPLEX != "ANN"]) %in%
sub("-[0-9]+/[0-9]+", "", v2g$EVENT))) / length(which(info$COMPLEX != "ANN")) < 0.9) {
stop("Less than 90% of vast-tools events found in ", basename(opt$specEventTable))
}
v2g
}
saveGOfiles <- function(v2g, info, opt, contrTab, diff, dpsi) {
GOoutPath <- file.path(opt$outDir, "GO", "input")
for (i in 1:nrow(contrTab)) {
write.table(getBG(i,c("MIC","CE"), diff), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("BG_", contrTab$Contrast[i], ".CE.MIC.txt")))
write.table(getBG(i,"MIC", diff), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("BG_", contrTab$Contrast[i], ".MIC.txt")))
write.table(getBG(i,"IR", diff), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("BG_", contrTab$Contrast[i], ".IR.txt")))
write.table(getBG(i,"Alt5", diff), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("BG_", contrTab$Contrast[i], ".Alt5.txt")))
write.table(getBG(i,"Alt3", diff), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("BG_", contrTab$Contrast[i], ".Alt3.txt")))
write.table(getFG(i,c("MIC","CE"), diff,"up"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".CE.MIC.up.txt")))
write.table(getFG(i,c("MIC","CE"), diff,"dn"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".CE.MIC.dn.txt")))
write.table(getFG(i,"MIC", diff,"up"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".MIC.up.txt")))
write.table(getFG(i,"MIC", diff,"dn"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".MIC.dn.txt")))
write.table(getFG(i,"IR", diff,"up"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".IR.up.txt")))
write.table(getFG(i,"IR", diff,"dn"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".IR.dn.txt")))
write.table(getFG(i,"Alt5", diff,"up"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".Alt5.up.txt")))
write.table(getFG(i,"Alt5", diff,"dn"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".Alt5.dn.txt")))
write.table(getFG(i,"Alt3", diff,"up"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".Alt3.up.txt")))
write.table(getFG(i,"Alt3", diff,"dn"), row.names=F, col.names=F, quote=F,
file=file.path(GOoutPath, paste0("AS_", contrTab$Contrast[i], ".Alt3.dn.txt")))
}
}
getBG <- function(x, kind, diff) {
changing <- as.character(unique(v2g$EnsemblGeneID[which(diff[,x] & info$TYPE %in% kind)]))
setdiff(unique(c(changing, as.character(v2g$EnsemblGeneID[!is.na(dpsi[,x]) & info$TYPE %in% kind]))), NA)
}
getFG <- function(x, kind, diff, way=c("up","dn", "change")) {
if (way == "up") {
return(setdiff(unique(v2g$EnsemblGeneID[which(dpsi[,x] > 0 & diff[,x] & info$TYPE %in% kind)]), NA))
}
if (way == "dn") {
return(setdiff(unique(v2g$EnsemblGeneID[which(dpsi[,x] < 0 & diff[,x] & info$TYPE %in% kind)]), NA))
}
if (way == "change") {
return(setdiff(unique(v2g$EnsemblGeneID[which(diff[,x] & info$TYPE %in% kind)]), NA))
}
}
### Body ###
## Check input
pathSlots <- which(names(opt) %in% c("vastDir","outDir","sampleTab","contrTab","specEventTable","vast2EnsemblGene"))
suppressWarnings(opt[pathSlots] <- sapply(opt[pathSlots], normalizePath))
if (!file.exists(opt$vastDir) && !opt$continue) {stop("vast-tools folder not found at ", opt$vastDir)}
if (!file.exists(opt$sampleTab)) {stop(opt$sampleTab, " not found")}
if (!file.exists(opt$contrTab)) {stop(opt$contrTab, " not found")}
if (!is.null(opt$specEventTable) && !file.exists(opt$specEventTable)) {
stop(opt$specEventTable, " not found")
}
if (!opt$noGO && is.null(opt$vast2EnsemblGene)) {
stop("If --noGO is not set, vast2EnsemblGene is required to generate GO input files")
}
if (!opt$noGO && !file.exists(opt$vast2EnsemblGene)) {
stop(opt$vast2EnsemblGene, " not found")
}
if (!(opt$minRepFrac > 0 & opt$minRepFrac <= 1)) {stop("--minRepFrac must be in ]0, 1]")}
if (!(opt$filter %in% c("DEFAULT","STRICT","LEGACY"))) {stop("--filter must be DEFAULT|STRICT|LEGACY")}
if (!opt$continue) {
if (is.null(opt$vastMain)) {
vastMain <- sort(dir(opt$vastDir, pattern="INCLUSION_LEVELS_FULL-[[:alnum:]]{4,6}(-[[:alnum:]]{1,4})?.tab.*",
full.names=T),
decreasing=T)[1]
if (is.na(vastMain)) {stop("vast-tools main table not found in ", opt$vastDir)}
} else {
vastMain <- opt$vastMain
if (!file.exists(vastMain)) {stop("vast-tools main table ", opt$vastMain, " not found")}
}
} else {
if (!is.null(opt$vastMain)) {
stop("Flag --continue is set but --vastMain is also provided")
}
vastMain <- sort(dir(opt$outDir, pattern="INCLUSION_LEVELS_FULL-[[:alnum:]]{4,6}(-[[:alnum:]]{1,4})?_clean.*.tab.*",
full.names=T),
decreasing=T)[1]
if (length(vastMain) != 1) {
stop("Flag --continue is set but can't find filtered vast-tools inclusion table in ", opt$outDir)
}
if (!file.exists(file.path(opt$outDir, "AStables.Robj"))) {
stop("Flag --continue is set but can't find ", file.path(opt$outDir, "AStables.Robj"))
}
}
sampleTab <- read.csv(opt$sampleTab, as.is=T)
if (!all(c("Sample","Type") %in% names(sampleTab))) {
stop("Sample table must contain columns Sample, Type")
}
contrTab <- read.csv(opt$contrTab, as.is=T)
if (!all(c("Experimental","Control","File") %in% names(contrTab))) {
stop("Contrast table must contain columns Experimental, Control, File")
}
uqContr <- unique(unlist(lapply(1:nrow(contrTab), FUN=function(x) {contrTab[x,1:2]})))
foundContr <- uqContr %in% sampleTab$Type
if (!all(foundContr)) {
stop("Sample type(s) ", paste(uqContr[!foundContr], collapse=", "), " has/have no samples associated")
}
if (is.null(contrTab$Contrast)) {
contrTab$Contrast <- paste(contrTab$Experimental, contrTab$Control, sep=":")
}
diffFiles <- dir(opt$vastDir)
contrTab$found <- contrTab$File %in% diffFiles
if (!all(contrTab$found) && !opt$continue) {
stop("DIFF table(s) not found: ", paste(contrTab$File[!contrTab$found], collapse=", "))
}
cols <- strsplit(opt$colors, split=",")[[1]]
if (!all(cols %in% colors())) {
stop("Color(s) not defined: ", paste(cols[!(cols %in% colors())], collapse=", "))
}
if (length(uqContr) > length(cols)) {cols <- rep(cols, length(uqContr) %/% length(cols) + 1)[1:length(uqContr)]}
sampleTab$col <- sapply(sampleTab$Type, FUN=function(x) {
out <- cols[which(uqContr == x)]
if (length(out) == 0) {out <- "black"}
out
})
if (!("grey80" %in% sampleTab$col)) {
sampleTab$col[tolower(sampleTab$Type) == "other"] <- "grey80"
}
## Read data
cat("Reading ", basename(vastMain), "...\n", sep="")
vast <- read.delim(vastMain, check.names=F)
print(names(vast))
if (any(duplicated(vast$EVENT))) {
vast <- removeVastDupEvents(vast)
}
if (opt$continue) {
vast <- reshapeVast(vast)
}
sampleTab$vastCol <- sapply(sampleTab$Sample, FUN=function(x) {
out <- which(names(vast) == x)
if (length(out) != 1) out <- NA
out
})
if (any(is.na(sampleTab$vastCol))) {
stop("Sample(s) present in sample table but not found in vast-tools output:\n",
paste(sampleTab$Sample[is.na(sampleTab$vastCol)], collapse=", "))
}
if (!is.null(opt$vast2EnsemblGene)) {
v2g <- read.delim(opt$vast2EnsemblGene)
if (!all(c("EVENT","EnsemblGeneID") %in% names(v2g))) {
stop("vast2EnsemblGene table needs columns 'EVENT', 'EnsemblGeneID'")
}
}
if (!dir.exists(opt$outDir)) {dir.create(opt$outDir, recursive=T)}
if (opt$continue) {
cat("Loading AS tables in AStables.Robj...\n")
load(file.path(opt$outDir, "AStables.Robj"))
}
## Save log file
contTag <- ifelse(opt$continue, "_plots", "")
logName <- file.path(opt$outDir, paste0("vastResultsProcessing", contTag, ".log"))
saveLog(logName, opt, sampleTab, contrTab)
if (!opt$continue) {
## Qual score filtering
cat("Quality filtering...\n")
if (opt$filter == "DEFAULT") {
covThresCE <- c("SOK","OK","LOW","VLOW")
covThresOther <- c("SOK","OK","LOW","VLOW")
covThresIR <- 10
balThresCE <- c("OK","B1","B2","Bl","Bn")
}
if (opt$filter == "STRICT") {
covThresCE <- c("SOK","OK","LOW")
covThresOther <- c("SOK","OK","LOW")
covThresIR <- 15
balThresCE <- c("OK","B1","B2","Bl","Bn")
}
if (opt$filter == "LEGACY") {
covThresCE <- c("SOK","OK","LOW")
covThresOther <- c("SOK","OK","LOW")
covThresIR <- 15
balThresCE <- c("OK","B1")
}
for (i in seq(8, ncol(vast), 2)) { # for all quality columns...
vast[,i - 1] <- ifelse(cleanAS(vast[,i],
covThresCE=covThresCE, covThresOther=covThresOther, covThresIR=covThresIR,
balThresCE=balThresCE,
complexCol=vast$COMPLEX), vast[,i-1], NA)
}
gz1 <- gzfile(file.path(opt$outDir, paste0(sub("\\.tab(\\.gz)?", "_clean", basename(vastMain)),
substr(format(Sys.time(), "%Y%m%d"),3,8),
".tab.gz")),
"w")
write.table(vast[,-seq(8, ncol(vast), 2)], file=gz1, row.names=F, col.names=T, quote=F, sep='\t')
try(close(gz1))
## Extract point estimates from DIFF files
cat("Extracting average PSI...\n")
diffres <- mclapply(file.path(opt$vastDir, contrTab$File), read.delim, mc.cores=opt$cores)
names(diffres) <- contrTab$Contrast
diffres <- mclapply(diffres, FUN=mergeToVast, events=vast$EVENT, mc.cores=opt$cores)
means <- sapply(uqContr, diffPointEst,
minFrac=opt$minRepFrac, contrTab=contrTab, diffres=diffres, sampleTab=sampleTab, vast=vast)
cat("\n")
rownames(means) <- vast$EVENT
cat("Extracting differential events...\n")
diffsig <- sapply(names(diffres), FUN=function(x) {diffres[[x]]$MV.dPsi._at_0.95})
rownames(diffsig) <- vast$EVENT
## Combine Alt5/Alt3 subevents
if (opt$keepFullAlt) {
means.long <- means
diffsig.long <- diffsig
type <- character(nrow(vast))
type[vast$COMPLEX %in% c("C1","C2","C3","S","ANN")] <- "CE"
type[grep("IR-", vast$COMPLEX)] <- "IR"
type[vast$COMPLEX == "MIC"] <- "MIC"
type[type == "CE" & vast$LENGTH <= 27] <- "MIC"
type[vast$COMPLEX == "Alt5"] <- "Alt5"
type[vast$COMPLEX == "Alt3"] <- "Alt3"
info.long <- data.frame(vast[,1:6], TYPE=type)
}
means.shr <- altCombinePSI(vast=vast, dat=means)
diffsig.shr <- altCombine.sig(vast=vast, diffsig=diffsig)
reorder <- orderEvents(info=means.shr[,1:7])
info <- means.shr[reorder, 1:7]
means <- as.matrix(means.shr[reorder, 8:ncol(means.shr)])
diffsig <- as.matrix(diffsig.shr[reorder,])
## Generate dpsi and diff tables
dpsi <- sapply(1:nrow(contrTab), dpsiMake, contrTab=contrTab, uqContr=uqContr, means=means)
colnames(dpsi) <- contrTab$Contrast
if (opt$strictDiff) {
diff05 <- 100 * diffsig > 5 & !is.na(dpsi)
diff10 <- 100 * diffsig > 10 & !is.na(dpsi)
diff15 <- 100 * diffsig > 15 & !is.na(dpsi)
} else {
diff05 <- 100 * diffsig > 0 & abs(dpsi) > 5
diff10 <- 100 * diffsig > 0 & abs(dpsi) > 10
diff15 <- 100 * diffsig > 0 & abs(dpsi) > 15
}
if (opt$keepFullAlt) {
dpsi.long <- sapply(1:nrow(contrTab), dpsiMake, contrTab=contrTab, uqContr=uqContr, means=means.long)
colnames(dpsi.long) <- contrTab$Contrast
if (opt$strictDiff) {
diff05.long <- 100 * diffsig.long > 5 & !is.na(dpsi.long)
diff10.long <- 100 * diffsig.long > 10 & !is.na(dpsi.long)
diff15.long <- 100 * diffsig.long > 15 & !is.na(dpsi.long)
} else {
diff05.long <- 100 * diffsig.long > 0 & abs(dpsi.long) > 5
diff10.long <- 100 * diffsig.long > 0 & abs(dpsi.long) > 10
diff15.long <- 100 * diffsig.long > 0 & abs(dpsi.long) > 15
}
}
## Save tables
gz1 <- gzfile(file.path(opt$outDir, "ASmeans.tab.gz"), "w")
write.table(data.frame(info, means),
file=gz1,
row.names=F, col.names=T, quote=F, sep='\t')
close(gz1)
gz1 <- gzfile(file.path(opt$outDir, "dPSI.tab.gz"), "w")
write.table(data.frame(info, dpsi),
file=gz1,
row.names=F, col.names=T, quote=F, sep='\t')
close(gz1)
save(info, means, dpsi, diffsig, diff05, diff10, diff15,
file=file.path(opt$outDir, "AStables.Robj")
)
if (opt$keepFullAlt) {
save(info.long, means.long, dpsi.long, diffsig.long, diff05.long, diff10.long, diff15.long,
file=file.path(opt$outDir, "AStables_fullAltSS.Robj")
)
}
out <- data.frame(info, round(dpsi,2), diff15)[which(apply(diff15, MAR=1, FUN=function(x) {any(x)})),]
names(out)[8:(ncol(out))] <- paste(rep(c("dPSI","signif"), each=ncol(dpsi)),
rep(names(out)[8:(7 + ncol(dpsi))], 2), sep=".")
gz1 <- gzfile(file.path(opt$outDir, "AS.DiffEvents_dPSI.15.tab.gz"), "w")
write.table(out, file=gz1, row.names=F, col.names=T, quote=F, sep='\t')
close(gz1)
out <- data.frame(info, round(dpsi,2), diff10)[which(apply(diff10, MAR=1, FUN=function(x) {any(x)})),]
names(out)[8:(ncol(out))] <- paste(rep(c("dPSI","signif"), each=ncol(dpsi)),
rep(names(out)[8:(7 + ncol(dpsi))], 2), sep=".")
gz1 <- gzfile(file.path(opt$outDir, "AS.DiffEvents_dPSI.10.tab.gz"), "w")
write.table(out, file=gz1, row.names=F, col.names=T, quote=F, sep='\t')
close(gz1)
out <- data.frame(info, round(dpsi,2), diff05)[which(apply(diff05, MAR=1, FUN=function(x) {any(x)})),]
names(out)[8:(ncol(out))] <- paste(rep(c("dPSI","signif"), each=ncol(dpsi)),
rep(names(out)[8:(7 + ncol(dpsi))], 2), sep=".")
gz1 <- gzfile(file.path(opt$outDir, "AS.DiffEvents_dPSI.05.tab.gz"), "w")
write.table(out, file=gz1, row.names=F, col.names=T, quote=F, sep='\t')
close(gz1)
}
### Analyses ###
## Numbers of changing events
rowCol <- setRowCol(ncol(dpsi))
pdf(file.path(opt$outDir, "ChangingEvents.dPSI.15_bars.pdf"),
wid=1.8 + rowCol[2] * 2.9, hei=1.6 + rowCol[1] * 2.7)
changingEventTypes(dpsi=dpsi, sig=diff15, info=info, includeANN=T, main=contrTab$Contrast)
dev.off()
pdf(file.path(opt$outDir, "ChangingEvents.dPSI.10_bars.pdf"),
wid=1.8 + rowCol[2] * 2.9, hei=1.6 + rowCol[1] * 2.7)
changingEventTypes(dpsi=dpsi, sig=diff10, info=info, includeANN=T, main=contrTab$Contrast)
dev.off()
pdf(file.path(opt$outDir, "ChangingEvents.dPSI.05_bars.pdf"),
wid=1.8 + rowCol[2] * 2.9, hei=1.6 + rowCol[1] * 2.7)
changingEventTypes(dpsi=dpsi, sig=diff05, info=info, includeANN=T, main=contrTab$Contrast)
dev.off()
## Clustering of single samples by correlation
suppressWarnings(
use <-
apply(vast[,seq(7,ncol(vast) - 1,2)], MAR=1,
FUN=function(x) {length(which(is.na(x)))}) < 1/3 * nrow(sampleTab) &
apply(vast[,seq(7,ncol(vast) - 1,2)], MAR=1, max, na.rm=T) -
apply(vast[,seq(7,ncol(vast) - 1,2)], MAR=1, min, na.rm=T) > 10
)
pdf(file.path(opt$outDir, "PSI.cluster.corr.single.pdf"),
wid=5 + 0.1 * nrow(sampleTab), hei=5.2 + 0.1 * nrow(sampleTab))
cors <- cor(vast[use,seq(7,ncol(vast) - 1,2)], use="p")
heatmap.2(cors, trace="n", col=heat.colors, na.col="grey90", margins=c(12,12),
cex.main=0.5, main=paste("Correlation of PSI\n (events with PSI range > 10)"),
key.title="", key.xlab="Corr. coefficient",
labRow=sampleTab$Sample, labCol=sampleTab$Sample)
dev.off()
## Plot MDS
distSing <- dist(t(vast[use,seq(7,ncol(vast) - 1,2)]))
mds <- cmdscale(distSing, k=2)
xlim <- range(mds[,1]) + c(-0.08,0.08) * (max(mds[,1]) - min(mds[,1]))
ylim <- range(mds[,2]) + c(-0.08,0.08) * (max(mds[,2]) - min(mds[,2]))
pdf(file.path(opt$outDir, "MDS_samplePSI.pdf"), wid=5.5, hei=6)
plot(mds[,c(1,2)], pch=20, col=sampleTab$col, xlim=xlim, ylim=ylim,
main="Multi-dimensional scaling", xlab="DIM 1", ylab="DIM 2")
text(mds[,c(1,2)], labels=sub("\\.R1$", "", rownames(mds)),
cex=ifelse(nrow(sampleTab) > 12, 0.6, 0.8), pos=1, col=sampleTab$col)
dev.off()
## Clustering of differential events
if (ncol(dpsi) > 1) {
change <- which(apply(diff10, MAR=1, FUN=function(x) {any(x)}) &
rowSums(is.na(dpsi)) < 1/4 * ncol(dpsi)
)
pdf(file.path(opt$outDir, "dPSI.cluster.corr.pdf"),
wid=6 + 0.1 * nrow(contrTab), hei=6 + 0.1 * nrow(contrTab))
cors <- cor(dpsi[change,], use="p")
heatmap.2(cors, trace="n", col=ifelse(min(cors) < 0, cm.colors, heat.colors), na.col="grey90", margins=c(16,16),
cex.main=0.4, main="Correlation of dPSI\n(changing events with dPSI >= 10 only)",
denscol="grey40", key.title="", key.xlab="Corr. coefficient",
labRow=contrTab$Contrast, labCol=contrTab$Contrast)
dev.off()
}
## Clustering by dPSI euclidian distance
if (ncol(dpsi) > 1) {
try({
distTreat <- dist(t(dpsi[change,]))
distEvent <- dist(dpsi[change,])
clustTreat <- hclust(distTreat, method="ward.D2")
clustEvent <- hclust(distEvent, method="ward.D2")
clustTreat <- as.dendrogram(clustTreat)
clustEvent <- as.dendrogram(clustEvent)
clustEvent <- reorder(clustEvent, wts=rowMeans(dpsi[change,], na.rm=T))
typecol <- ifelse(info$TYPE == "CE", "deepskyblue", NA)
typecol[info$TYPE == "MIC"] <- "darkblue"
typecol[info$TYPE == "IR"] <- "orange"
typecol[info$TYPE == "Alt5"] <- "darkolivegreen"
typecol[info$TYPE == "Alt3"] <- "darkolivegreen2"
dpsi.plot <- as.matrix(dpsi[change,-3])
dpsi.plot[dpsi.plot < -30] <- -30
dpsi.plot[dpsi.plot > 30] <- 30
mycols <- c("cyan4", cm.colors(9), "magenta4")
pdf(file.path(opt$outDir, "AS.cluster.dPSI.pdf"),
wid=3 + 0.1 * nrow(contrTab), hei=11)
heatmap.2(as.matrix(dpsi[change,]), mar=c(22,3), main="dPSI of changing events",
trace="n", col=mycols, na.color="grey80", labRow="", labCol=contrTab$Contrast,
RowSideColors=typecol[change], lwid=c(1,2,1), lhei=c(1,6),
cexCol=1.4, density.info="none", sepcolor=NA, sepwidth=c(0,0), key.title="", key.xlab="dPSI",
Rowv=clustEvent, Colv=clustTreat,
breaks=c(-100, seq(-50,50,length.out=10), 100))