R/reportdb.R
In pmoulos/metaseqR2: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms
Defines functions
.formatForReport
.downloadJsLibs
.dbImportPlot
.reportDbTblDef
.initReportDbTables
.createDexiePlotDb
.createSqlPlotDb
.makeReportEnv
.makeReportEnv <- function(e) {
re <- new.env(parent=globalenv())
re$theList <- e$theList
re$tLink <- e$tLink
re$createTracks <- e$createTracks
re$trackExportPath <- e$trackExportPath
re$trackInfo <- e$trackInfo
re$offlineReport <- e$offlineReport
re$reportDb <- e$reportDb
re$org <- e$org
re$refdb <- e$refdb
re$utrOpts <- e$utrOpts
re$qcPlots <- e$qcPlots
re$sampleList <- e$sampleList
re$geneData <- e$geneData
re$geneCounts <- e$geneCounts
re$geneDataExpr <- e$geneDataExpr
re$normGenesExpr <- e$normGenesExpr
re$fromRaw <- e$fromRaw
re$transLevel <- e$transLevel
re$countType <- e$countType
re$fileType <- e$fileType
re$geneFilters <- e$geneFilters
re$exonFilters <- e$exonFilters
re$normalization <- e$normalization
re$statistics <- e$statistics
re$metaP <- e$metaP
re$pcut <- e$pcut
re$contrast <- e$contrast
re$adjustMethod <- e$adjustMethod
re$sumpList <- e$sumpList
re$logOffset <- e$logOffset
re$countsName <- e$countsName
re$excludeList <- e$excludeList
re$libsizeList <- e$libsizeList
re$reportMessages <- e$reportMessages
re$preset <- e$preset
re$whenApplyFilter <- e$whenApplyFilter
re$normArgs <- e$normArgs
re$statArgs <- e$statArgs
re$figFormat <- e$figFormat
re$exportWhere <- e$exportWhere
re$exportWhat <- e$exportWhat
re$exportScale <- e$exportScale
re$exportValues <- e$exportValues
re$exportStats <- e$exportStats
re$execTime <- e$execTime
re$theZeros <- e$theZeros
re$theDead <- e$theDead
re$exonFilterResult <- e$exonFilterResult
re$geneFilterResult <- e$geneFilterResult
re$geneFilterCutoff <- e$geneFilterCutoff
re$runLog <- e$runLog
re$geneDataFiltered <- e$geneDataFiltered
re$totalGeneData <- e$totalGeneData
re$normGenes <- e$normGenes
re$geneCountsZero <- e$geneCountsZero
re$contrastList <- e$contrastList
re$reportTop <- e$reportTop
re$cpList <- e$cpList
re$reportTables <- e$reportTables
re$exportCountsTable <- e$exportCountsTable
re$FUN_CALL <- e$FUN_CALL
re$PROJECT_PATH <- e$PROJECT_PATH
return(re)
}
.createSqlPlotDb <- function(obj) {
sampleList <- obj$sampleList
contrastList <- obj$contrastList
qcPlots <- obj$qcPlots
geneCounts <- obj$geneCounts
geneData <- obj$geneData
geneDataExpr <- obj$geneDataExpr
geneDataFiltered <- obj$geneDataFiltered
totalGeneData <- obj$totalGeneData
normGenes <- obj$normGenes
normGenesExpr <- obj$normGenesExpr
cpList <- obj$cpList
sumpList <- obj$sumpList
pcut <- obj$pcut
metaP <- obj$metaP
whenApplyFilter <- obj$whenApplyFilter
PROJECT_PATH <- obj$PROJECT_PATH
samples <- unlist(sampleList)
nsa <- length(samples)
covarsRaw <- covarsStat <- NULL
if (any(qcPlots %in% c("biodetection","countsbio","saturation",
"rnacomp","readnoise"))) {
covarsRaw <- list(
data=geneCounts,
length=width(geneData),
gc=as.numeric(geneData$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneData)),
start=start(geneData),
end=end(geneData)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneData$biotype),
gene_name=as.character(geneData$gene_name)
)
}
if ("biodist" %in% qcPlots) {
covarsStat <- list(
data=normGenesExpr,
length=width(geneDataExpr),
gc=as.numeric(geneDataExpr$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneDataExpr)),
start=start(geneDataExpr),
end=end(geneDataExpr)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneDataExpr$biotype),
gene_name=as.character(geneDataExpr$gene_name)
)
}
rdb <- file.path(PROJECT_PATH$data,"reportdb.sqlite")
disp("Opening plot database in ",rdb)
con <- dbConnect(dbDriver("SQLite"),dbname=rdb)
rs <- .initReportDbTables(con)
if ("mds" %in% qcPlots) {
disp("Importing mds...")
json <- diagplotMds(geneCounts,sampleList,output="json")
.dbImportPlot(con,"MDS","mds","generic",json)
}
if ("biodetection" %in% qcPlots) {
disp(" Importing biodetection...")
cap <- capture.output({
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="biodetection",output="json")
})
for (s in samples) {
disp(" ",s)
name <- paste("biodetection",s,sep="_")
.dbImportPlot(con,name,"biodetection","generic",json[[s]])
}
}
if ("countsbio" %in% qcPlots) {
disp(" Importing countsbio...")
cap <- capture.output({
jsonList <- diagplotNoiseq(geneCounts,sampleList,
covars=covarsRaw,whichPlot="countsbio",output="json")
})
for (s in samples) {
disp(" ",s)
name <- paste("countsbio",s,sep="_")
.dbImportPlot(con,name,"countsbio","sample",
jsonList[["sample"]][[s]])
}
for (b in names(jsonList[["biotype"]])) {
disp(" ",b)
name <- paste("countsbio",b,sep="_")
.dbImportPlot(con,name,"countsbio","biotype",
jsonList[["biotype"]][[b]])
}
}
if ("saturation" %in% qcPlots) {
disp(" Importing saturation...")
jsonList <- diagplotNoiseq(geneCounts,sampleList,
covars=covarsRaw,whichPlot="saturation",output="json")
for (s in unlist(sampleList)) {
disp(" ",s)
name <- paste("saturation",s,sep="_")
.dbImportPlot(con,name,"saturation","sample",
jsonList[["sample"]][[s]])
}
for (b in names(jsonList[["biotype"]])) {
disp(" ",b)
name <- paste("saturation",b,sep="_")
.dbImportPlot(con,name,"saturation","biotype",
jsonList[["biotype"]][[b]])
}
}
if ("readnoise" %in% qcPlots) {
disp(" Importing readnoise...")
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="readnoise",output="json")
.dbImportPlot(con,"ReadNoise","readnoise","generic",json)
}
if ("pairwise" %in% qcPlots) {
disp(" Importing pairwise...")
jsonList <- diagplotPairs(geneCounts,output="json")
for (name in names(jsonList$xy)) {
disp(" ",name)
.dbImportPlot(con,name,"pairwise","xy",jsonList$xy[[name]])
.dbImportPlot(con,name,"pairwise","md",jsonList$md[[name]])
}
}
if ("filtered" %in% qcPlots) {
disp(" Importing filtered...")
jsonList <- diagplotFiltered(geneDataFiltered,totalGeneData,
output="json")
.dbImportPlot(con,"filtered_chromosome","filtered","chromosome",
jsonList[["chromosome"]])
.dbImportPlot(con,"filtered_biotype","filtered","biotype",
jsonList[["biotype"]])
}
if ("boxplot" %in% qcPlots) {
disp(" Importing boxplot...")
jsonUnorm <- diagplotBoxplot(geneCounts,name=sampleList,
isNorm=FALSE,output="json")
jsonNorm <- diagplotBoxplot(normGenes,name=sampleList,
isNorm=FALSE,output="json")
.dbImportPlot(con,"Boxplot","boxplot","unorm",jsonUnorm)
.dbImportPlot(con,"Boxplot","boxplot","norm",jsonNorm)
}
if ("gcbias" %in% qcPlots) {
disp(" Importing gcbias...")
covarRaw <- as.numeric(geneData$gc_content)
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="gcbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="gcbias",output="json")
.dbImportPlot(con,"GCBias","gcbias","unorm",jsonUnorm)
.dbImportPlot(con,"GCBias","gcbias","norm",jsonNorm)
}
if ("lengthbias" %in% qcPlots) {
disp(" Importing lengthbias...")
covarRaw <- as.numeric(geneData$gc_content)
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="lengthbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="lengthbias",output="json")
.dbImportPlot(con,"LengthBias","lengthbias","unorm",jsonUnorm)
.dbImportPlot(con,"LengthBias","lengthbias","norm",jsonNorm)
}
if ("meandiff" %in% qcPlots) {
disp(" Importing meandif...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meandiff",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meandiff",output="json")
for (n in names(jsonUnorm)) {
for (s in names(jsonUnorm[[n]])) {
disp(" ",s)
.dbImportPlot(con,s,"meandiff","unorm",
jsonUnorm[[n]][[s]])
.dbImportPlot(con,s,"meandiff","norm",
jsonNorm[[n]][[s]])
}
}
}
if ("meanvar" %in% qcPlots) {
disp(" Importing meanvar...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meanvar",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meanvar",output="json")
.dbImportPlot(con,"MeanVar","meanvar","unorm",jsonUnorm)
.dbImportPlot(con,"MeanVar","meanvar","orm",jsonNorm)
}
if ("rnacomp" %in% qcPlots) {
disp(" Importing rnacomp...")
cap1 <- capture.output({
jsonUnorm <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="rnacomp",isNorm=FALSE,output="json")
})
cap2 <- capture.output({
jsonNorm <- diagplotNoiseq(normGenes,sampleList,
covars=if (whenApplyFilter=="prenorm") covarsStat else covarsRaw,
whichPlot="rnacomp",isNorm=TRUE,output="json")
})
.dbImportPlot(con,"RnaComp","rnacomp","unorm",jsonUnorm)
.dbImportPlot(con,"RnaComp","rnacomp","norm",jsonNorm)
}
if ("volcano" %in% qcPlots) {
disp(" Importing volcano")
nn <- names(contrastList)
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
for (contr in colnames(fc)) {
disp(" ",n," ",contr)
json <- diagplotVolcano(fc[,contr],sumpList[[n]],contr,
altNames=geneDataExpr$gene_name,output="json")
.dbImportPlot(con,paste("volcano",contr,sep="_"),
"volcano","generic",json)
}
}
}
if ("mastat" %in% qcPlots) {
disp(" Importing mastat")
nn <- names(contrastList)
for (n in nn) {
m <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
a <- makeA(n,sampleList,normGenesExpr,1)
for (contr in colnames(m)) {
disp(" ",n," ",contr)
json <- diagplotMa(m[,contr],a[,contr],sumpList[[n]],
contr,altNames=geneDataExpr$gene_name,output="json")
.dbImportPlot(con,paste("mastat",contr,sep="_"),
"mastat","generic",json)
}
}
}
if ("biodist" %in% qcPlots) {
disp(" Importing biodist")
nn <- names(contrastList)
for (n in nn) {
disp(" ",n)
cap <- capture.output({
json <- diagplotNoiseq(normGenesExpr,sampleList,
covars=covarsStat,whichPlot="biodist",
biodistOpts=list(p=cpList[[n]],pcut=pcut,name=n),
output="json")
})
.dbImportPlot(con,paste("biodist",n,sep="_"),"biodist",
"chromosome",json$chromosome)
.dbImportPlot(con,paste("biodist",n,sep="_"),"biodist",
"biotype",json$biotype)
}
}
if ("statvenn" %in% qcPlots) {
disp(" Importing statvenn")
nn <- names(contrastList)
geneNames <- as.character(geneDataExpr$gene_name)
names(geneNames) <- names(geneDataExpr)
for (n in nn) {
disp(" ",n)
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
# To narrow We take always the first condition versus the reference
# to narrow down a bit the list of DE genes
fc <- fc[,1,drop=FALSE]
fcmat <- matrix(data=apply(fc,2,function(x) {
rep(x,length(colnames(cpList[[n]])))
}),ncol=ncol(fc)*ncol(cpList[[n]]))
colnames(fcmat) <- colnames(cpList[[n]])
# Deregulated
json <- makeJVennStatData(cpList[[n]],fcmat=fcmat,pcut=pcut,
direction="dereg",altNames=geneNames[rownames(cpList[[n]])])
.dbImportPlot(con,paste("statvenn",n,"dereg",sep="_"),"statvenn",
"stat_dereg",toJSON(json,auto_unbox=TRUE,null="null"))
# Up
json <- makeJVennStatData(cpList[[n]],fcmat=fcmat,pcut=pcut,
direction="up",altNames=geneNames[rownames(cpList[[n]])])
.dbImportPlot(con,paste("statvenn",n,"up",sep="_"),"statvenn",
"stat_up",toJSON(json,auto_unbox=TRUE,null="null"))
# Down
json <- makeJVennStatData(cpList[[n]],fcmat=fcmat,pcut=pcut,
direction="down",altNames=geneNames[rownames(cpList[[n]])])
.dbImportPlot(con,paste("statvenn",n,"down",sep="_"),"statvenn",
"stat_down",toJSON(json,auto_unbox=TRUE,null="null"))
}
}
if ("foldvenn" %in% qcPlots) {
disp(" Importing foldvenn")
nn <- names(contrastList)
## Fold venn cannot be created for ANOVA-like cases, only p-values can
## be used in such cases, so a failsafe
#tmp <- strsplit(nn,"_vs_")
#tmpl <- lengths(tmp)
#nn2 <- NULL
#if (any(tmpl) > 2) {
# jj1 <- which(tmpl==2)
# jj2 <- which(tmpl>2)
# nn2 <- nn[jj2]
# nn <- nn[jj1]
#}
# We need to construct a matrix with p-values for each contrast and
# each statistical algorithm
if (ncol(cpList[[1]]) > 1) {
N <- names(cpList)
prePmat <- lapply(N,function(n,C,S) {
many <- C[[n]]
one <- S[[n]]
mat <- cbind(many,one)
colnames(mat)[ncol(mat)] <- metaP
return(mat)
},cpList,sumpList)
names(prePmat) <- N
}
else
prePmat <- sumpList
# Now we need to reformat prePmat to a list names according to each
# algorithm
algs <- colnames(prePmat[[1]])
pmat <- lapply(algs,function(a,P) {
algMat <- matrix(NA,nrow(P[[1]]),length(P))
colnames(algMat) <- names(P)
rownames(algMat) <- rownames(P[[1]])
for (cnt in names(P))
algMat[,cnt] <- prePmat[[cnt]][,a]
return(algMat)
},prePmat)
# We need to construct a matrix with fold changes for each contrast
fList <- vector("list",length(nn))
names(fList) <- nn
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
fc <- fc[,1,drop=FALSE]
fList[[n]] <- fc
}
fcmat <- do.call("cbind",fList)
fcmat <- fcmat[rownames(pmat),]
# Based on pmat and fcmat, we calculate Venns
geneNames <- geneDataExpr$gene_name
names(geneNames) <- names(geneDataExpr)
for (n in names(pmat)) {
disp(" ",n)
jsonList[[n]] <- list(dereg=NULL,up=NULL,down=NULL)
for (d in c("dereg","up","down"))
json <- makeJVennFoldData(pmat=pmat,fcmat=fcmat,pcut=pcut,
direction=d,altNames=geneNames[rownames(pmat[[n]])])
.dbImportPlot(con,paste("foldvenn",n,d,sep="_"),"foldvenn",
paste0("fold_",d),toJSON(json,auto_unbox=TRUE,null="null"))
}
}
if ("deregulogram" %in% qcPlots) {
disp(" Importing deregulogram")
cntPairs <- combn(names(contrastList),2)
for (i in seq_len(ncol(cntPairs))) {
disp(" ",cntPairs[1,i]," and ",cntPairs[2,i])
namc <- paste0(cntPairs[1,i],"__",cntPairs[2,i])
fmat <- cbind(
log2(makeFoldChange(cntPairs[1,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE],
log2(makeFoldChange(cntPairs[2,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE]
)
pmat <- do.call("cbind",sumpList[c(cntPairs[1,i],
cntPairs[2,i])])
colnames(pmat) <- colnames(fmat)
json <- diagplotDeregulogram(fmat,pmat,pcut=pcut,fcut=1,
output="json",altNames=geneDataExpr$gene_name)
.dbImportPlot(con,paste("deregulogram",namc,sep="_"),
"deregulogram","generic",json)
}
}
# Close SQLite connection
dbDisconnect(con)
}
.createDexiePlotDb <- function(obj) {
sampleList <- obj$sampleList
contrastList <- obj$contrastList
qcPlots <- obj$qcPlots
geneCounts <- obj$geneCounts
geneData <- obj$geneData
geneDataExpr <- obj$geneDataExpr
geneDataFiltered <- obj$geneDataFiltered
totalGeneData <- obj$totalGeneData
normGenes <- obj$normGenes
normGenesExpr <- obj$normGenesExpr
cpList <- obj$cpList
sumpList <- obj$sumpList
pcut <- obj$pcut
metaP <- obj$metaP
whenApplyFilter <- obj$whenApplyFilter
PROJECT_PATH <- obj$PROJECT_PATH
samples <- unlist(sampleList)
nsa <- length(samples)
covarsRaw <- covarsStat <- NULL
if (any(qcPlots %in% c("biodetection","countsbio","saturation",
"rnacomp","readnoise"))) {
covarsRaw <- list(
data=geneCounts,
length=width(geneData),
gc=as.numeric(geneData$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneData)),
start=start(geneData),
end=end(geneData)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneData$biotype),
gene_name=as.character(geneData$gene_name)
)
}
if ("biodist" %in% qcPlots) {
covarsStat <- list(
data=normGenesExpr,
length=width(geneDataExpr),
gc=as.numeric(geneDataExpr$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneDataExpr)),
start=start(geneDataExpr),
end=end(geneDataExpr)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneDataExpr$biotype),
gene_name=as.character(geneDataExpr$gene_name)
)
}
plots <- list()
listIndex <- 0
if ("mds" %in% qcPlots) {
disp(" Importing mds...")
json <- diagplotMds(geneCounts,sampleList,output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="MDS",
type="mds",
subtype="generic",
json=fromJSON(json)
)
}
if ("biodetection" %in% qcPlots) {
disp(" Importing biodetection...")
cap <- capture.output({
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="biodetection",output="json")
})
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("biodetection",samples[i],sep="_"),
type="biodetection",
subtype="generic",
json=fromJSON(json[[samples[i]]])
)
}
}
if ("countsbio" %in% qcPlots) {
disp(" Importing countsbio...")
cap <- capture.output({
jsonList <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="countsbio",output="json")
})
for (i in seq_along(jsonList$sample)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("countsbio",samples[i],sep="_"),
type="countsbio",
subtype="sample",
json=fromJSON(jsonList$sample[[samples[i]]])
)
}
biotypes <- names(jsonList$biotype)
for (i in seq_along(jsonList$biotype)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("countsbio",biotypes[i],sep="_"),
type="countsbio",
subtype="biotype",
json=fromJSON(jsonList$biotype[[biotypes[i]]])
)
}
}
if ("saturation" %in% qcPlots) {
disp(" Importing saturation...")
jsonList <- diagplotNoiseq(geneCounts,sampleList,
covars=covarsRaw,whichPlot="saturation",output="json")
for (i in seq_along(jsonList$sample)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("saturation",samples[i],sep="_"),
type="saturation",
subtype="sample",
json=fromJSON(jsonList$sample[[samples[i]]])
)
}
biotypes <- names(jsonList$biotype)
for (i in seq_along(jsonList$biotype)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("saturation",biotypes[i],sep="_"),
type="saturation",
subtype="biotype",
json=fromJSON(jsonList$biotype[[biotypes[i]]])
)
}
}
if ("readnoise" %in% qcPlots) {
disp(" Importing readnoise...")
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="readnoise",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="ReadNoise",
type="readnoise",
subtype="generic",
json=fromJSON(json)
)
}
if ("pairwise" %in% qcPlots) {
disp(" Importing pairwise...")
jsonList <- diagplotPairs(geneCounts,output="json")
nams <- names(jsonList$xy)
for (i in seq_along(jsonList$xy)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=nams[i],
type="pairwise",
subtype="xy",
json=fromJSON(jsonList$xy[[nams[i]]])
)
}
for (i in seq_along(jsonList$md)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=nams[i],
type="pairwise",
subtype="md",
json=fromJSON(jsonList$md[[nams[i]]])
)
}
}
if ("filtered" %in% qcPlots) {
disp(" Importing filtered...")
jsonList <- diagplotFiltered(geneDataFiltered,totalGeneData,
output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="filtered_chromosome",
type="filtered",
subtype="chromosome",
json=fromJSON(jsonList[["chromosome"]])
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="filtered_biotype",
type="filtered",
subtype="biotype",
json=fromJSON(jsonList[["biotype"]])
)
}
if ("boxplot" %in% qcPlots) {
disp(" Importing boxplot...")
jsonUnorm <- diagplotBoxplot(geneCounts,name=sampleList,
isNorm=FALSE,output="json")
jsonNorm <- diagplotBoxplot(normGenes,name=sampleList,
isNorm=FALSE,output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="Boxplot",
type="boxplot",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="Boxplot",
type="boxplot",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("gcbias" %in% qcPlots) {
disp(" Importing gcbias...")
covarRaw <- as.numeric(geneData$gc_content)
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="gcbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="gcbias",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="GCBias",
type="gcbias",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="GCBias",
type="gcbias",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("lengthbias" %in% qcPlots) {
disp(" Importing lengthbias...")
covarRaw <- width(geneData[rownames(geneCounts)])
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="lengthbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="lengthbias",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="LengthBias",
type="lengthbias",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="LengthBias",
type="lengthbias",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("meandiff" %in% qcPlots) {
disp(" Importing meandif...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meandiff",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meandiff",output="json")
np <- sum(lengths(jsonUnorm))
for (i in seq_along(jsonUnorm)) {
for (j in seq_along(jsonUnorm[[i]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=names(jsonUnorm[[i]])[j],
type="meandiff",
subtype="unorm",
json=fromJSON(jsonUnorm[[i]][[j]])
)
}
}
for (i in seq_along(jsonNorm)) {
for (j in seq_along(jsonNorm[[i]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=names(jsonNorm[[i]])[j],
type="meandiff",
subtype="norm",
json=fromJSON(jsonNorm[[i]][[j]])
)
}
}
}
if ("meanvar" %in% qcPlots) {
disp(" Importing meanvar...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meanvar",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meanvar",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="MeanVar",
type="meanvar",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="MeanVar",
type="meanvar",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("rnacomp" %in% qcPlots) {
disp(" Importing rnacomp...")
cap1 <- capture.output({
jsonUnorm <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="rnacomp",isNorm=FALSE,output="json")
})
cap2 <- capture.output({
jsonNorm <- diagplotNoiseq(normGenes,sampleList,
covars=if (whenApplyFilter=="prenorm") covarsStat else covarsRaw,
whichPlot="rnacomp",isNorm=TRUE,output="json")
})
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="RnaComp",
type="rnacomp",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="RnaComp",
type="rnacomp",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("volcano" %in% qcPlots) {
disp(" Importing volcano")
nn <- names(contrastList)
json <- list()
namc <- character(0)
index <- 0
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
for (contr in colnames(fc)) {
disp(" ",n," ",contr)
index <- index + 1
namc <- c(namc,contr)
json[[index]] <- diagplotVolcano(fc[,contr],sumpList[[n]],contr,
altNames=geneDataExpr$gene_name,output="json")
}
}
names(json) <- namc
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("volcano",namc[i],sep="_"),
type="volcano",
subtype="generic",
json=fromJSON(json[[i]])
)
}
}
if ("mastat" %in% qcPlots) {
disp(" Importing mastat")
nn <- names(contrastList)
json <- list()
namc <- character(0)
index <- 0
for (n in nn) {
m <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
a <- makeA(n,sampleList,normGenesExpr,1)
for (contr in colnames(m)) {
disp(" ",n," ",contr)
index <- index + 1
namc <- c(namc,contr)
json[[index]] <- diagplotMa(m[,contr],a[,contr],sumpList[[n]],
contr,altNames=geneDataExpr$gene_name,output="json")
}
}
names(json) <- namc
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("mastat",namc[i],sep="_"),
type="mastat",
subtype="generic",
json=fromJSON(json[[i]])
)
}
}
if ("biodist" %in% qcPlots) {
disp(" Importing biodist")
nn <- names(contrastList)
jsonList <- vector("list",length(nn))
names(jsonList) <- nn
for (n in nn) {
disp(" ",n)
cap <- capture.output({
jsonList[[n]] <- diagplotNoiseq(normGenesExpr,sampleList,
covars=covarsStat,whichPlot="biodist",
biodistOpts=list(p=cpList[[n]],pcut=pcut,name=n),
output="json")
})
}
for (i in seq_along(jsonList)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("biodist",nn[i],sep="_"),
type="biodist",
subtype="chromosome",
json=fromJSON(jsonList[[i]]$chromosome)
)
}
for (i in seq_along(jsonList)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("biodist",nn[i],sep="_"),
type="biodist",
subtype="biotype",
json=fromJSON(jsonList[[i]]$biotype)
)
}
}
if ("statvenn" %in% qcPlots) {
disp(" Importing statvenn")
nn <- names(contrastList)
jsonList <- vector("list",length(nn))
names(jsonList) <- nn
geneNames <- geneDataExpr$gene_name
names(geneNames) <- names(geneDataExpr)
for (n in nn) {
disp(" ",n)
jsonList[[n]] <- list(dereg=NULL,up=NULL,down=NULL)
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
# To narrow We take always the first condition versus the reference
# to narrow down a bit the list of DE genes
fc <- fc[,1,drop=FALSE]
fcmat <- matrix(data=apply(fc,2,function(x) {
rep(x,length(colnames(cpList[[n]])))
}),ncol=ncol(fc)*ncol(cpList[[n]]))
colnames(fcmat) <- colnames(cpList[[n]])
jsonList[[n]]$dereg <- makeJVennStatData(cpList[[n]],fcmat=fcmat,
pcut=pcut,direction="dereg",
altNames=geneNames[rownames(cpList[[n]])])
jsonList[[n]]$up <- makeJVennStatData(cpList[[n]],fcmat=fcmat,
pcut=pcut,direction="up",
altNames=geneNames[rownames(cpList[[n]])])
jsonList[[n]]$down <- makeJVennStatData(cpList[[n]],fcmat=fcmat,
pcut=pcut,direction="down",
altNames=geneNames[rownames(cpList[[n]])])
}
for (i in seq_along(jsonList)) {
for (d in names(jsonList[[i]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("statvenn",nn[i],d,sep="_"),
type="statvenn",
subtype=paste0("stat_",d),
json=jsonList[[i]][[d]]
)
}
}
}
if ("foldvenn" %in% qcPlots) {
disp(" Importing foldvenn")
nn <- names(contrastList)
# We need to construct a matrix with p-values for each contrast and
# each statistical algorithm
if (ncol(cpList[[1]]) > 1) {
N <- names(cpList)
prePmat <- lapply(N,function(n,C,S) {
many <- C[[n]]
one <- S[[n]]
mat <- cbind(many,one)
colnames(mat)[ncol(mat)] <- metaP
return(mat)
},cpList,sumpList)
names(prePmat) <- N
}
else
prePmat <- sumpList
# Now we need to reformat prePmat to a list names according to each
# algorithm
algs <- colnames(prePmat[[1]])
pmat <- lapply(algs,function(a,P) {
algMat <- matrix(NA,nrow(P[[1]]),length(P))
colnames(algMat) <- names(P)
rownames(algMat) <- rownames(P[[1]])
for (cnt in names(P))
algMat[,cnt] <- prePmat[[cnt]][,a]
return(algMat)
},prePmat)
names(pmat) <- algs
# We need to construct a matrix with fold changes for each contrast
fList <- vector("list",length(nn))
names(fList) <- nn
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
fc <- fc[,1,drop=FALSE]
fList[[n]] <- fc
}
fcmat <- do.call("cbind",fList)
fcmat <- fcmat[rownames(pmat[[1]]),]
# Based on pmat and fcmat, we calculate Venns
jsonList <- vector("list",length(pmat))
names(jsonList) <- names(pmat)
geneNames <- geneDataExpr$gene_name
names(geneNames) <- names(geneDataExpr)
for (n in names(jsonList)) {
disp(" ",n)
jsonList[[n]] <- list(dereg=NULL,up=NULL,down=NULL)
for (d in c("dereg","up","down"))
jsonList[[n]][[d]] <- makeJVennFoldData(pmat=pmat[[n]],
fcmat=fcmat,pcut=pcut,direction=d,
altNames=geneNames[rownames(pmat[[n]])])
}
for (n in names(jsonList)) {
for (d in names(jsonList[[n]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("foldvenn",n,d,sep="_"),
type="foldvenn",
subtype=paste0("fold_",d),
json=jsonList[[n]][[d]]
)
}
}
}
if ("deregulogram" %in% qcPlots) {
disp(" Importing deregulogram")
cntPairs <- combn(names(contrastList),2)
json <- vector("list",ncol(cntPairs))
namc <- character(ncol(cntPairs))
counter <- 0
for (i in seq_len(ncol(cntPairs))) {
counter <- counter + 1
disp(" ",cntPairs[1,i]," and ",cntPairs[2,i])
namc[counter] <- paste0(cntPairs[1,i],"__",cntPairs[2,i])
fmat <- cbind(
log2(makeFoldChange(cntPairs[1,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE],
log2(makeFoldChange(cntPairs[2,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE]
)
pmat <- do.call("cbind",sumpList[c(cntPairs[1,i],
cntPairs[2,i])])
colnames(pmat) <- colnames(fmat)
json[[counter]] <- diagplotDeregulogram(fmat,pmat,fcut=0.3,
pcut=pcut,output="json",altNames=geneDataExpr$gene_name)
}
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("deregulogram",namc[i],sep="_"),
type="deregulogram",
subtype="generic",
json=fromJSON(json[[i]])
)
}
}
disp("Writing plot database in ",file.path(PROJECT_PATH$data,
"reportdb.js"))
jsonPlots <- toJSON(plots,auto_unbox=TRUE,null="null",pretty=TRUE)
code <- paste0("var plotData = ",jsonPlots)
cat(code,file=file.path(PROJECT_PATH$data,"reportdb.js"),sep="\n")
}
.initReportDbTables <- function(con) {
queries <- .reportDbTblDef()
rs <- dbSendQuery(con,queries[[1]])
if (dbHasCompleted(rs))
dbClearResult(rs)
for (n in names(queries)) {
rs <- dbSendStatement(con,queries[[n]])
if (dbHasCompleted(rs))
dbClearResult(rs)
}
}
.reportDbTblDef <- function() {
return(list(
enable_fkey="PRAGMA foreign_keys=1;",
plot=paste(
"CREATE TABLE IF NOT EXISTS plot (",
"_id INTEGER PRIMARY KEY AUTOINCREMENT,",
"name TEXT,",
"type TEXT,",
"subtype TEXT,",
"json TEXT",
");"
),
clear="DELETE FROM plot;"
))
}
.dbImportPlot <- function(con,name,type,subtype=NULL,json) {
if (is.null(subtype))
query <- paste("INSERT INTO plot (name, type, subtype, json) ",
"VALUES (","'",name,"', ","'",type,"', NULL, :j)",sep="")
else
query <- paste("INSERT INTO plot (name, type, subtype, json) ",
"VALUES (","'",name,"', ","'",type,"', ","'",
subtype,"', :j)",sep="")
#print(query)
nr <- dbExecute(con,query,params=list(j=json))
return(nr)
}
.downloadJsLibs <- function(prPath,reportDb) {
disp("Downloading required JavaScript libraries...")
if (!file.exists(file.path(prPath$js,"pace.min.js")))
download.file(paste0("https://raw.github.com/HubSpot/pace/",
"v1.0.0/pace.min.js"),
file.path(prPath$js,"pace.min.js"))
if (!file.exists(file.path(prPath$js,"highcharts.js")))
download.file("https://code.highcharts.com/highcharts.js",
file.path(prPath$js,"highcharts.js"))
if (!file.exists(file.path(prPath$js,"highcharts-more.js")))
download.file("https://code.highcharts.com/highcharts-more.js",
file.path(prPath$js,"highcharts-more.js"))
if (!file.exists(file.path(prPath$js,"exporting.js")))
download.file("https://code.highcharts.com/modules/exporting.js",
file.path(prPath$js,"exporting.js"))
if (!file.exists(file.path(prPath$js,"offline-exporting.js")))
download.file(
"https://code.highcharts.com/modules/offline-exporting.js",
file.path(prPath$js,"offline-exporting.js"))
if (!file.exists(file.path(prPath$js,"export-data.js")))
download.file(
"https://code.highcharts.com/modules/export-data.js",
file.path(prPath$js,"export-data.js"))
if (!file.exists(file.path(prPath$js,"canvas2svg.js")))
download.file(
"http://jvenn.toulouse.inrae.fr/app/js/canvas2svg.js",
file.path(prPath$js,"canvas2svg.js"))
if (!file.exists(file.path(prPath$js,"jvenn.min.js")))
download.file(
"http://jvenn.toulouse.inrae.fr/app/js/jvenn.min.js",
file.path(prPath$js,"jvenn.min.js"))
if (reportDb == "sqlite") {
if (!file.exists(file.path(prPath$js,"sql.js")))
download.file(
"https://cdnjs.cloudflare.com/ajax/libs/sql.js/0.5.0/js/sql.js",
file.path(prPath$js,"sql.js"))
}
else if (reportDb == "dexie") {
if (!file.exists(file.path(prPath$js,"dexie.min.js")))
download.file(
"https://unpkg.com/dexie@2.0.4/dist/dexie.min.js",
file.path(prPath$js,"dexie.min.js"))
}
}
.formatForReport <- function(x,o=NULL,r=NULL,v=NULL) {
# Replace seqnames name
colnames(x)[1] <- "chromosome"
# Link to UCSC genome browser with position
if (!is.null(o)) {
x$chromosome <-
paste('<a href="http://genome.ucsc.edu/cgi-bin/hgTracks?db=',
getUcscOrganism(o),'&position=',paste(x$chromosome,":",
x$start,"-",x$end,sep=""),'" target="_blank">',x$chromosome,
'</a>',sep="")
}
# Link to gene pages - only for Ensembl, RefSeq, not custom GTFs! UCSC does
# not offer a related page.
if (!is.null(r)) {
if (r == "ensembl") {
h <- getHost(o,v)
if ("gene_id" %in% names(x)) # transLevel = "gene"
x$gene_id <- paste('<a href="',h,'/Gene/Summary?g=',x$gene_id,
'" target="_blank">',x$gene_id,'</a>',sep="")
# transLevel = "transcript"
else if ("transcript_id" %in% names(x)) {
# Stub
}
else if ("exon_id" %in% names(x)) { # transLevel = "exon"
# Stub
}
}
else if (r == "refseq") {
if ("gene_id" %in% names(x)) # transLevel = "gene"
x$gene_id <- paste('<a href="https://www.ncbi.nlm.nih.gov/',
'nuccore/',x$gene_id,'" target="_blank">',x$gene_id,'</a>',
sep="")
else if ("transcript_id" %in% names(x)) { # transLevel="transcript"
# Stub
}
else if ("exon_id" %in% names(x)) { # transLevel = "exon"
# Stub
}
}
}
# Round rpgm and fold change
tmpi1 <- grep("log2_normalized_fold_change_",colnames(x))
tmpi2 <- grep("rpgm_normalized_mean_counts_",colnames(x))
tmpi <- c(tmpi1,tmpi2)
if (length(tmpi) > 0)
x[,tmpi] <- round(x[,tmpi],digits=4)
# Round p-values and FDR
tmpi1 <- grep("p-value",colnames(x))
tmpi2 <- grep("FDR",colnames(x))
tmpi <- c(tmpi1,tmpi2)
if (length(tmpi) > 0)
x[,tmpi] <- round(x[,tmpi],digits=6)
colnames(x) <- gsub("log2_normalized_fold_change_","",colnames(x))
colnames(x) <- gsub("rpgm_normalized_mean_counts_","",colnames(x))
tmpi <- grep("p-value",colnames(x))
if (length(tmpi) > 0)
colnames(x)[tmpi] <- "p-value"
tmpi <- grep("FDR",colnames(x))
if (length(tmpi) > 0)
colnames(x)[tmpi] <- "FDR"
return(x)
}
pmoulos/metaseqR2 documentation built on May 20, 2024, 5:48 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions .formatForReport .downloadJsLibs .dbImportPlot .reportDbTblDef .initReportDbTables .createDexiePlotDb .createSqlPlotDb .makeReportEnv
.makeReportEnv <- function(e) {
re <- new.env(parent=globalenv())
re$theList <- e$theList
re$tLink <- e$tLink
re$createTracks <- e$createTracks
re$trackExportPath <- e$trackExportPath
re$trackInfo <- e$trackInfo
re$offlineReport <- e$offlineReport
re$reportDb <- e$reportDb
re$org <- e$org
re$refdb <- e$refdb
re$utrOpts <- e$utrOpts
re$qcPlots <- e$qcPlots
re$sampleList <- e$sampleList
re$geneData <- e$geneData
re$geneCounts <- e$geneCounts
re$geneDataExpr <- e$geneDataExpr
re$normGenesExpr <- e$normGenesExpr
re$fromRaw <- e$fromRaw
re$transLevel <- e$transLevel
re$countType <- e$countType
re$fileType <- e$fileType
re$geneFilters <- e$geneFilters
re$exonFilters <- e$exonFilters
re$normalization <- e$normalization
re$statistics <- e$statistics
re$metaP <- e$metaP
re$pcut <- e$pcut
re$contrast <- e$contrast
re$adjustMethod <- e$adjustMethod
re$sumpList <- e$sumpList
re$logOffset <- e$logOffset
re$countsName <- e$countsName
re$excludeList <- e$excludeList
re$libsizeList <- e$libsizeList
re$reportMessages <- e$reportMessages
re$preset <- e$preset
re$whenApplyFilter <- e$whenApplyFilter
re$normArgs <- e$normArgs
re$statArgs <- e$statArgs
re$figFormat <- e$figFormat
re$exportWhere <- e$exportWhere
re$exportWhat <- e$exportWhat
re$exportScale <- e$exportScale
re$exportValues <- e$exportValues
re$exportStats <- e$exportStats
re$execTime <- e$execTime
re$theZeros <- e$theZeros
re$theDead <- e$theDead
re$exonFilterResult <- e$exonFilterResult
re$geneFilterResult <- e$geneFilterResult
re$geneFilterCutoff <- e$geneFilterCutoff
re$runLog <- e$runLog
re$geneDataFiltered <- e$geneDataFiltered
re$totalGeneData <- e$totalGeneData
re$normGenes <- e$normGenes
re$geneCountsZero <- e$geneCountsZero
re$contrastList <- e$contrastList
re$reportTop <- e$reportTop
re$cpList <- e$cpList
re$reportTables <- e$reportTables
re$exportCountsTable <- e$exportCountsTable
re$FUN_CALL <- e$FUN_CALL
re$PROJECT_PATH <- e$PROJECT_PATH
return(re)
}
.createSqlPlotDb <- function(obj) {
sampleList <- obj$sampleList
contrastList <- obj$contrastList
qcPlots <- obj$qcPlots
geneCounts <- obj$geneCounts
geneData <- obj$geneData
geneDataExpr <- obj$geneDataExpr
geneDataFiltered <- obj$geneDataFiltered
totalGeneData <- obj$totalGeneData
normGenes <- obj$normGenes
normGenesExpr <- obj$normGenesExpr
cpList <- obj$cpList
sumpList <- obj$sumpList
pcut <- obj$pcut
metaP <- obj$metaP
whenApplyFilter <- obj$whenApplyFilter
PROJECT_PATH <- obj$PROJECT_PATH
samples <- unlist(sampleList)
nsa <- length(samples)
covarsRaw <- covarsStat <- NULL
if (any(qcPlots %in% c("biodetection","countsbio","saturation",
"rnacomp","readnoise"))) {
covarsRaw <- list(
data=geneCounts,
length=width(geneData),
gc=as.numeric(geneData$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneData)),
start=start(geneData),
end=end(geneData)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneData$biotype),
gene_name=as.character(geneData$gene_name)
)
}
if ("biodist" %in% qcPlots) {
covarsStat <- list(
data=normGenesExpr,
length=width(geneDataExpr),
gc=as.numeric(geneDataExpr$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneDataExpr)),
start=start(geneDataExpr),
end=end(geneDataExpr)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneDataExpr$biotype),
gene_name=as.character(geneDataExpr$gene_name)
)
}
rdb <- file.path(PROJECT_PATH$data,"reportdb.sqlite")
disp("Opening plot database in ",rdb)
con <- dbConnect(dbDriver("SQLite"),dbname=rdb)
rs <- .initReportDbTables(con)
if ("mds" %in% qcPlots) {
disp("Importing mds...")
json <- diagplotMds(geneCounts,sampleList,output="json")
.dbImportPlot(con,"MDS","mds","generic",json)
}
if ("biodetection" %in% qcPlots) {
disp(" Importing biodetection...")
cap <- capture.output({
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="biodetection",output="json")
})
for (s in samples) {
disp(" ",s)
name <- paste("biodetection",s,sep="_")
.dbImportPlot(con,name,"biodetection","generic",json[[s]])
}
}
if ("countsbio" %in% qcPlots) {
disp(" Importing countsbio...")
cap <- capture.output({
jsonList <- diagplotNoiseq(geneCounts,sampleList,
covars=covarsRaw,whichPlot="countsbio",output="json")
})
for (s in samples) {
disp(" ",s)
name <- paste("countsbio",s,sep="_")
.dbImportPlot(con,name,"countsbio","sample",
jsonList[["sample"]][[s]])
}
for (b in names(jsonList[["biotype"]])) {
disp(" ",b)
name <- paste("countsbio",b,sep="_")
.dbImportPlot(con,name,"countsbio","biotype",
jsonList[["biotype"]][[b]])
}
}
if ("saturation" %in% qcPlots) {
disp(" Importing saturation...")
jsonList <- diagplotNoiseq(geneCounts,sampleList,
covars=covarsRaw,whichPlot="saturation",output="json")
for (s in unlist(sampleList)) {
disp(" ",s)
name <- paste("saturation",s,sep="_")
.dbImportPlot(con,name,"saturation","sample",
jsonList[["sample"]][[s]])
}
for (b in names(jsonList[["biotype"]])) {
disp(" ",b)
name <- paste("saturation",b,sep="_")
.dbImportPlot(con,name,"saturation","biotype",
jsonList[["biotype"]][[b]])
}
}
if ("readnoise" %in% qcPlots) {
disp(" Importing readnoise...")
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="readnoise",output="json")
.dbImportPlot(con,"ReadNoise","readnoise","generic",json)
}
if ("pairwise" %in% qcPlots) {
disp(" Importing pairwise...")
jsonList <- diagplotPairs(geneCounts,output="json")
for (name in names(jsonList$xy)) {
disp(" ",name)
.dbImportPlot(con,name,"pairwise","xy",jsonList$xy[[name]])
.dbImportPlot(con,name,"pairwise","md",jsonList$md[[name]])
}
}
if ("filtered" %in% qcPlots) {
disp(" Importing filtered...")
jsonList <- diagplotFiltered(geneDataFiltered,totalGeneData,
output="json")
.dbImportPlot(con,"filtered_chromosome","filtered","chromosome",
jsonList[["chromosome"]])
.dbImportPlot(con,"filtered_biotype","filtered","biotype",
jsonList[["biotype"]])
}
if ("boxplot" %in% qcPlots) {
disp(" Importing boxplot...")
jsonUnorm <- diagplotBoxplot(geneCounts,name=sampleList,
isNorm=FALSE,output="json")
jsonNorm <- diagplotBoxplot(normGenes,name=sampleList,
isNorm=FALSE,output="json")
.dbImportPlot(con,"Boxplot","boxplot","unorm",jsonUnorm)
.dbImportPlot(con,"Boxplot","boxplot","norm",jsonNorm)
}
if ("gcbias" %in% qcPlots) {
disp(" Importing gcbias...")
covarRaw <- as.numeric(geneData$gc_content)
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="gcbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="gcbias",output="json")
.dbImportPlot(con,"GCBias","gcbias","unorm",jsonUnorm)
.dbImportPlot(con,"GCBias","gcbias","norm",jsonNorm)
}
if ("lengthbias" %in% qcPlots) {
disp(" Importing lengthbias...")
covarRaw <- as.numeric(geneData$gc_content)
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="lengthbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="lengthbias",output="json")
.dbImportPlot(con,"LengthBias","lengthbias","unorm",jsonUnorm)
.dbImportPlot(con,"LengthBias","lengthbias","norm",jsonNorm)
}
if ("meandiff" %in% qcPlots) {
disp(" Importing meandif...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meandiff",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meandiff",output="json")
for (n in names(jsonUnorm)) {
for (s in names(jsonUnorm[[n]])) {
disp(" ",s)
.dbImportPlot(con,s,"meandiff","unorm",
jsonUnorm[[n]][[s]])
.dbImportPlot(con,s,"meandiff","norm",
jsonNorm[[n]][[s]])
}
}
}
if ("meanvar" %in% qcPlots) {
disp(" Importing meanvar...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meanvar",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meanvar",output="json")
.dbImportPlot(con,"MeanVar","meanvar","unorm",jsonUnorm)
.dbImportPlot(con,"MeanVar","meanvar","orm",jsonNorm)
}
if ("rnacomp" %in% qcPlots) {
disp(" Importing rnacomp...")
cap1 <- capture.output({
jsonUnorm <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="rnacomp",isNorm=FALSE,output="json")
})
cap2 <- capture.output({
jsonNorm <- diagplotNoiseq(normGenes,sampleList,
covars=if (whenApplyFilter=="prenorm") covarsStat else covarsRaw,
whichPlot="rnacomp",isNorm=TRUE,output="json")
})
.dbImportPlot(con,"RnaComp","rnacomp","unorm",jsonUnorm)
.dbImportPlot(con,"RnaComp","rnacomp","norm",jsonNorm)
}
if ("volcano" %in% qcPlots) {
disp(" Importing volcano")
nn <- names(contrastList)
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
for (contr in colnames(fc)) {
disp(" ",n," ",contr)
json <- diagplotVolcano(fc[,contr],sumpList[[n]],contr,
altNames=geneDataExpr$gene_name,output="json")
.dbImportPlot(con,paste("volcano",contr,sep="_"),
"volcano","generic",json)
}
}
}
if ("mastat" %in% qcPlots) {
disp(" Importing mastat")
nn <- names(contrastList)
for (n in nn) {
m <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
a <- makeA(n,sampleList,normGenesExpr,1)
for (contr in colnames(m)) {
disp(" ",n," ",contr)
json <- diagplotMa(m[,contr],a[,contr],sumpList[[n]],
contr,altNames=geneDataExpr$gene_name,output="json")
.dbImportPlot(con,paste("mastat",contr,sep="_"),
"mastat","generic",json)
}
}
}
if ("biodist" %in% qcPlots) {
disp(" Importing biodist")
nn <- names(contrastList)
for (n in nn) {
disp(" ",n)
cap <- capture.output({
json <- diagplotNoiseq(normGenesExpr,sampleList,
covars=covarsStat,whichPlot="biodist",
biodistOpts=list(p=cpList[[n]],pcut=pcut,name=n),
output="json")
})
.dbImportPlot(con,paste("biodist",n,sep="_"),"biodist",
"chromosome",json$chromosome)
.dbImportPlot(con,paste("biodist",n,sep="_"),"biodist",
"biotype",json$biotype)
}
}
if ("statvenn" %in% qcPlots) {
disp(" Importing statvenn")
nn <- names(contrastList)
geneNames <- as.character(geneDataExpr$gene_name)
names(geneNames) <- names(geneDataExpr)
for (n in nn) {
disp(" ",n)
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
# To narrow We take always the first condition versus the reference
# to narrow down a bit the list of DE genes
fc <- fc[,1,drop=FALSE]
fcmat <- matrix(data=apply(fc,2,function(x) {
rep(x,length(colnames(cpList[[n]])))
}),ncol=ncol(fc)*ncol(cpList[[n]]))
colnames(fcmat) <- colnames(cpList[[n]])
# Deregulated
json <- makeJVennStatData(cpList[[n]],fcmat=fcmat,pcut=pcut,
direction="dereg",altNames=geneNames[rownames(cpList[[n]])])
.dbImportPlot(con,paste("statvenn",n,"dereg",sep="_"),"statvenn",
"stat_dereg",toJSON(json,auto_unbox=TRUE,null="null"))
# Up
json <- makeJVennStatData(cpList[[n]],fcmat=fcmat,pcut=pcut,
direction="up",altNames=geneNames[rownames(cpList[[n]])])
.dbImportPlot(con,paste("statvenn",n,"up",sep="_"),"statvenn",
"stat_up",toJSON(json,auto_unbox=TRUE,null="null"))
# Down
json <- makeJVennStatData(cpList[[n]],fcmat=fcmat,pcut=pcut,
direction="down",altNames=geneNames[rownames(cpList[[n]])])
.dbImportPlot(con,paste("statvenn",n,"down",sep="_"),"statvenn",
"stat_down",toJSON(json,auto_unbox=TRUE,null="null"))
}
}
if ("foldvenn" %in% qcPlots) {
disp(" Importing foldvenn")
nn <- names(contrastList)
## Fold venn cannot be created for ANOVA-like cases, only p-values can
## be used in such cases, so a failsafe
#tmp <- strsplit(nn,"_vs_")
#tmpl <- lengths(tmp)
#nn2 <- NULL
#if (any(tmpl) > 2) {
# jj1 <- which(tmpl==2)
# jj2 <- which(tmpl>2)
# nn2 <- nn[jj2]
# nn <- nn[jj1]
#}
# We need to construct a matrix with p-values for each contrast and
# each statistical algorithm
if (ncol(cpList[[1]]) > 1) {
N <- names(cpList)
prePmat <- lapply(N,function(n,C,S) {
many <- C[[n]]
one <- S[[n]]
mat <- cbind(many,one)
colnames(mat)[ncol(mat)] <- metaP
return(mat)
},cpList,sumpList)
names(prePmat) <- N
}
else
prePmat <- sumpList
# Now we need to reformat prePmat to a list names according to each
# algorithm
algs <- colnames(prePmat[[1]])
pmat <- lapply(algs,function(a,P) {
algMat <- matrix(NA,nrow(P[[1]]),length(P))
colnames(algMat) <- names(P)
rownames(algMat) <- rownames(P[[1]])
for (cnt in names(P))
algMat[,cnt] <- prePmat[[cnt]][,a]
return(algMat)
},prePmat)
# We need to construct a matrix with fold changes for each contrast
fList <- vector("list",length(nn))
names(fList) <- nn
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
fc <- fc[,1,drop=FALSE]
fList[[n]] <- fc
}
fcmat <- do.call("cbind",fList)
fcmat <- fcmat[rownames(pmat),]
# Based on pmat and fcmat, we calculate Venns
geneNames <- geneDataExpr$gene_name
names(geneNames) <- names(geneDataExpr)
for (n in names(pmat)) {
disp(" ",n)
jsonList[[n]] <- list(dereg=NULL,up=NULL,down=NULL)
for (d in c("dereg","up","down"))
json <- makeJVennFoldData(pmat=pmat,fcmat=fcmat,pcut=pcut,
direction=d,altNames=geneNames[rownames(pmat[[n]])])
.dbImportPlot(con,paste("foldvenn",n,d,sep="_"),"foldvenn",
paste0("fold_",d),toJSON(json,auto_unbox=TRUE,null="null"))
}
}
if ("deregulogram" %in% qcPlots) {
disp(" Importing deregulogram")
cntPairs <- combn(names(contrastList),2)
for (i in seq_len(ncol(cntPairs))) {
disp(" ",cntPairs[1,i]," and ",cntPairs[2,i])
namc <- paste0(cntPairs[1,i],"__",cntPairs[2,i])
fmat <- cbind(
log2(makeFoldChange(cntPairs[1,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE],
log2(makeFoldChange(cntPairs[2,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE]
)
pmat <- do.call("cbind",sumpList[c(cntPairs[1,i],
cntPairs[2,i])])
colnames(pmat) <- colnames(fmat)
json <- diagplotDeregulogram(fmat,pmat,pcut=pcut,fcut=1,
output="json",altNames=geneDataExpr$gene_name)
.dbImportPlot(con,paste("deregulogram",namc,sep="_"),
"deregulogram","generic",json)
}
}
# Close SQLite connection
dbDisconnect(con)
}
.createDexiePlotDb <- function(obj) {
sampleList <- obj$sampleList
contrastList <- obj$contrastList
qcPlots <- obj$qcPlots
geneCounts <- obj$geneCounts
geneData <- obj$geneData
geneDataExpr <- obj$geneDataExpr
geneDataFiltered <- obj$geneDataFiltered
totalGeneData <- obj$totalGeneData
normGenes <- obj$normGenes
normGenesExpr <- obj$normGenesExpr
cpList <- obj$cpList
sumpList <- obj$sumpList
pcut <- obj$pcut
metaP <- obj$metaP
whenApplyFilter <- obj$whenApplyFilter
PROJECT_PATH <- obj$PROJECT_PATH
samples <- unlist(sampleList)
nsa <- length(samples)
covarsRaw <- covarsStat <- NULL
if (any(qcPlots %in% c("biodetection","countsbio","saturation",
"rnacomp","readnoise"))) {
covarsRaw <- list(
data=geneCounts,
length=width(geneData),
gc=as.numeric(geneData$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneData)),
start=start(geneData),
end=end(geneData)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneData$biotype),
gene_name=as.character(geneData$gene_name)
)
}
if ("biodist" %in% qcPlots) {
covarsStat <- list(
data=normGenesExpr,
length=width(geneDataExpr),
gc=as.numeric(geneDataExpr$gc_content),
chromosome=data.frame(
chromosome=as.character(seqnames(geneDataExpr)),
start=start(geneDataExpr),
end=end(geneDataExpr)
),
factors=data.frame(class=asClassVector(sampleList)),
biotype=as.character(geneDataExpr$biotype),
gene_name=as.character(geneDataExpr$gene_name)
)
}
plots <- list()
listIndex <- 0
if ("mds" %in% qcPlots) {
disp(" Importing mds...")
json <- diagplotMds(geneCounts,sampleList,output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="MDS",
type="mds",
subtype="generic",
json=fromJSON(json)
)
}
if ("biodetection" %in% qcPlots) {
disp(" Importing biodetection...")
cap <- capture.output({
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="biodetection",output="json")
})
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("biodetection",samples[i],sep="_"),
type="biodetection",
subtype="generic",
json=fromJSON(json[[samples[i]]])
)
}
}
if ("countsbio" %in% qcPlots) {
disp(" Importing countsbio...")
cap <- capture.output({
jsonList <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="countsbio",output="json")
})
for (i in seq_along(jsonList$sample)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("countsbio",samples[i],sep="_"),
type="countsbio",
subtype="sample",
json=fromJSON(jsonList$sample[[samples[i]]])
)
}
biotypes <- names(jsonList$biotype)
for (i in seq_along(jsonList$biotype)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("countsbio",biotypes[i],sep="_"),
type="countsbio",
subtype="biotype",
json=fromJSON(jsonList$biotype[[biotypes[i]]])
)
}
}
if ("saturation" %in% qcPlots) {
disp(" Importing saturation...")
jsonList <- diagplotNoiseq(geneCounts,sampleList,
covars=covarsRaw,whichPlot="saturation",output="json")
for (i in seq_along(jsonList$sample)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("saturation",samples[i],sep="_"),
type="saturation",
subtype="sample",
json=fromJSON(jsonList$sample[[samples[i]]])
)
}
biotypes <- names(jsonList$biotype)
for (i in seq_along(jsonList$biotype)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("saturation",biotypes[i],sep="_"),
type="saturation",
subtype="biotype",
json=fromJSON(jsonList$biotype[[biotypes[i]]])
)
}
}
if ("readnoise" %in% qcPlots) {
disp(" Importing readnoise...")
json <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="readnoise",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="ReadNoise",
type="readnoise",
subtype="generic",
json=fromJSON(json)
)
}
if ("pairwise" %in% qcPlots) {
disp(" Importing pairwise...")
jsonList <- diagplotPairs(geneCounts,output="json")
nams <- names(jsonList$xy)
for (i in seq_along(jsonList$xy)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=nams[i],
type="pairwise",
subtype="xy",
json=fromJSON(jsonList$xy[[nams[i]]])
)
}
for (i in seq_along(jsonList$md)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=nams[i],
type="pairwise",
subtype="md",
json=fromJSON(jsonList$md[[nams[i]]])
)
}
}
if ("filtered" %in% qcPlots) {
disp(" Importing filtered...")
jsonList <- diagplotFiltered(geneDataFiltered,totalGeneData,
output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="filtered_chromosome",
type="filtered",
subtype="chromosome",
json=fromJSON(jsonList[["chromosome"]])
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="filtered_biotype",
type="filtered",
subtype="biotype",
json=fromJSON(jsonList[["biotype"]])
)
}
if ("boxplot" %in% qcPlots) {
disp(" Importing boxplot...")
jsonUnorm <- diagplotBoxplot(geneCounts,name=sampleList,
isNorm=FALSE,output="json")
jsonNorm <- diagplotBoxplot(normGenes,name=sampleList,
isNorm=FALSE,output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="Boxplot",
type="boxplot",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="Boxplot",
type="boxplot",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("gcbias" %in% qcPlots) {
disp(" Importing gcbias...")
covarRaw <- as.numeric(geneData$gc_content)
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="gcbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="gcbias",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="GCBias",
type="gcbias",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="GCBias",
type="gcbias",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("lengthbias" %in% qcPlots) {
disp(" Importing lengthbias...")
covarRaw <- width(geneData[rownames(geneCounts)])
# Covers whenApplyFilter="prenorm" case
covarNorm <- as.numeric(geneData[rownames(normGenes)]$gc_content)
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,covar=covarRaw,
isNorm=FALSE,whichPlot="lengthbias",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,covar=covarNorm,
isNorm=TRUE,whichPlot="lengthbias",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="LengthBias",
type="lengthbias",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="LengthBias",
type="lengthbias",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("meandiff" %in% qcPlots) {
disp(" Importing meandif...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meandiff",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meandiff",output="json")
np <- sum(lengths(jsonUnorm))
for (i in seq_along(jsonUnorm)) {
for (j in seq_along(jsonUnorm[[i]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=names(jsonUnorm[[i]])[j],
type="meandiff",
subtype="unorm",
json=fromJSON(jsonUnorm[[i]][[j]])
)
}
}
for (i in seq_along(jsonNorm)) {
for (j in seq_along(jsonNorm[[i]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=names(jsonNorm[[i]])[j],
type="meandiff",
subtype="norm",
json=fromJSON(jsonNorm[[i]][[j]])
)
}
}
}
if ("meanvar" %in% qcPlots) {
disp(" Importing meanvar...")
jsonUnorm <- diagplotEdaseq(geneCounts,sampleList,isNorm=FALSE,
whichPlot="meanvar",output="json")
jsonNorm <- diagplotEdaseq(normGenes,sampleList,isNorm=TRUE,
whichPlot="meanvar",output="json")
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="MeanVar",
type="meanvar",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="MeanVar",
type="meanvar",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("rnacomp" %in% qcPlots) {
disp(" Importing rnacomp...")
cap1 <- capture.output({
jsonUnorm <- diagplotNoiseq(geneCounts,sampleList,covars=covarsRaw,
whichPlot="rnacomp",isNorm=FALSE,output="json")
})
cap2 <- capture.output({
jsonNorm <- diagplotNoiseq(normGenes,sampleList,
covars=if (whenApplyFilter=="prenorm") covarsStat else covarsRaw,
whichPlot="rnacomp",isNorm=TRUE,output="json")
})
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="RnaComp",
type="rnacomp",
subtype="unorm",
json=fromJSON(jsonUnorm)
)
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name="RnaComp",
type="rnacomp",
subtype="norm",
json=fromJSON(jsonNorm)
)
}
if ("volcano" %in% qcPlots) {
disp(" Importing volcano")
nn <- names(contrastList)
json <- list()
namc <- character(0)
index <- 0
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
for (contr in colnames(fc)) {
disp(" ",n," ",contr)
index <- index + 1
namc <- c(namc,contr)
json[[index]] <- diagplotVolcano(fc[,contr],sumpList[[n]],contr,
altNames=geneDataExpr$gene_name,output="json")
}
}
names(json) <- namc
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("volcano",namc[i],sep="_"),
type="volcano",
subtype="generic",
json=fromJSON(json[[i]])
)
}
}
if ("mastat" %in% qcPlots) {
disp(" Importing mastat")
nn <- names(contrastList)
json <- list()
namc <- character(0)
index <- 0
for (n in nn) {
m <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
a <- makeA(n,sampleList,normGenesExpr,1)
for (contr in colnames(m)) {
disp(" ",n," ",contr)
index <- index + 1
namc <- c(namc,contr)
json[[index]] <- diagplotMa(m[,contr],a[,contr],sumpList[[n]],
contr,altNames=geneDataExpr$gene_name,output="json")
}
}
names(json) <- namc
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("mastat",namc[i],sep="_"),
type="mastat",
subtype="generic",
json=fromJSON(json[[i]])
)
}
}
if ("biodist" %in% qcPlots) {
disp(" Importing biodist")
nn <- names(contrastList)
jsonList <- vector("list",length(nn))
names(jsonList) <- nn
for (n in nn) {
disp(" ",n)
cap <- capture.output({
jsonList[[n]] <- diagplotNoiseq(normGenesExpr,sampleList,
covars=covarsStat,whichPlot="biodist",
biodistOpts=list(p=cpList[[n]],pcut=pcut,name=n),
output="json")
})
}
for (i in seq_along(jsonList)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("biodist",nn[i],sep="_"),
type="biodist",
subtype="chromosome",
json=fromJSON(jsonList[[i]]$chromosome)
)
}
for (i in seq_along(jsonList)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("biodist",nn[i],sep="_"),
type="biodist",
subtype="biotype",
json=fromJSON(jsonList[[i]]$biotype)
)
}
}
if ("statvenn" %in% qcPlots) {
disp(" Importing statvenn")
nn <- names(contrastList)
jsonList <- vector("list",length(nn))
names(jsonList) <- nn
geneNames <- geneDataExpr$gene_name
names(geneNames) <- names(geneDataExpr)
for (n in nn) {
disp(" ",n)
jsonList[[n]] <- list(dereg=NULL,up=NULL,down=NULL)
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
# To narrow We take always the first condition versus the reference
# to narrow down a bit the list of DE genes
fc <- fc[,1,drop=FALSE]
fcmat <- matrix(data=apply(fc,2,function(x) {
rep(x,length(colnames(cpList[[n]])))
}),ncol=ncol(fc)*ncol(cpList[[n]]))
colnames(fcmat) <- colnames(cpList[[n]])
jsonList[[n]]$dereg <- makeJVennStatData(cpList[[n]],fcmat=fcmat,
pcut=pcut,direction="dereg",
altNames=geneNames[rownames(cpList[[n]])])
jsonList[[n]]$up <- makeJVennStatData(cpList[[n]],fcmat=fcmat,
pcut=pcut,direction="up",
altNames=geneNames[rownames(cpList[[n]])])
jsonList[[n]]$down <- makeJVennStatData(cpList[[n]],fcmat=fcmat,
pcut=pcut,direction="down",
altNames=geneNames[rownames(cpList[[n]])])
}
for (i in seq_along(jsonList)) {
for (d in names(jsonList[[i]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("statvenn",nn[i],d,sep="_"),
type="statvenn",
subtype=paste0("stat_",d),
json=jsonList[[i]][[d]]
)
}
}
}
if ("foldvenn" %in% qcPlots) {
disp(" Importing foldvenn")
nn <- names(contrastList)
# We need to construct a matrix with p-values for each contrast and
# each statistical algorithm
if (ncol(cpList[[1]]) > 1) {
N <- names(cpList)
prePmat <- lapply(N,function(n,C,S) {
many <- C[[n]]
one <- S[[n]]
mat <- cbind(many,one)
colnames(mat)[ncol(mat)] <- metaP
return(mat)
},cpList,sumpList)
names(prePmat) <- N
}
else
prePmat <- sumpList
# Now we need to reformat prePmat to a list names according to each
# algorithm
algs <- colnames(prePmat[[1]])
pmat <- lapply(algs,function(a,P) {
algMat <- matrix(NA,nrow(P[[1]]),length(P))
colnames(algMat) <- names(P)
rownames(algMat) <- rownames(P[[1]])
for (cnt in names(P))
algMat[,cnt] <- prePmat[[cnt]][,a]
return(algMat)
},prePmat)
names(pmat) <- algs
# We need to construct a matrix with fold changes for each contrast
fList <- vector("list",length(nn))
names(fList) <- nn
for (n in nn) {
fc <- log2(makeFoldChange(n,sampleList,normGenesExpr,1))
fc <- fc[,1,drop=FALSE]
fList[[n]] <- fc
}
fcmat <- do.call("cbind",fList)
fcmat <- fcmat[rownames(pmat[[1]]),]
# Based on pmat and fcmat, we calculate Venns
jsonList <- vector("list",length(pmat))
names(jsonList) <- names(pmat)
geneNames <- geneDataExpr$gene_name
names(geneNames) <- names(geneDataExpr)
for (n in names(jsonList)) {
disp(" ",n)
jsonList[[n]] <- list(dereg=NULL,up=NULL,down=NULL)
for (d in c("dereg","up","down"))
jsonList[[n]][[d]] <- makeJVennFoldData(pmat=pmat[[n]],
fcmat=fcmat,pcut=pcut,direction=d,
altNames=geneNames[rownames(pmat[[n]])])
}
for (n in names(jsonList)) {
for (d in names(jsonList[[n]])) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("foldvenn",n,d,sep="_"),
type="foldvenn",
subtype=paste0("fold_",d),
json=jsonList[[n]][[d]]
)
}
}
}
if ("deregulogram" %in% qcPlots) {
disp(" Importing deregulogram")
cntPairs <- combn(names(contrastList),2)
json <- vector("list",ncol(cntPairs))
namc <- character(ncol(cntPairs))
counter <- 0
for (i in seq_len(ncol(cntPairs))) {
counter <- counter + 1
disp(" ",cntPairs[1,i]," and ",cntPairs[2,i])
namc[counter] <- paste0(cntPairs[1,i],"__",cntPairs[2,i])
fmat <- cbind(
log2(makeFoldChange(cntPairs[1,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE],
log2(makeFoldChange(cntPairs[2,i],sampleList,
normGenesExpr,1))[,1,drop=FALSE]
)
pmat <- do.call("cbind",sumpList[c(cntPairs[1,i],
cntPairs[2,i])])
colnames(pmat) <- colnames(fmat)
json[[counter]] <- diagplotDeregulogram(fmat,pmat,fcut=0.3,
pcut=pcut,output="json",altNames=geneDataExpr$gene_name)
}
for (i in seq_along(json)) {
listIndex <- listIndex + 1
plots[[listIndex]] <- list(
name=paste("deregulogram",namc[i],sep="_"),
type="deregulogram",
subtype="generic",
json=fromJSON(json[[i]])
)
}
}
disp("Writing plot database in ",file.path(PROJECT_PATH$data,
"reportdb.js"))
jsonPlots <- toJSON(plots,auto_unbox=TRUE,null="null",pretty=TRUE)
code <- paste0("var plotData = ",jsonPlots)
cat(code,file=file.path(PROJECT_PATH$data,"reportdb.js"),sep="\n")
}
.initReportDbTables <- function(con) {
queries <- .reportDbTblDef()
rs <- dbSendQuery(con,queries[[1]])
if (dbHasCompleted(rs))
dbClearResult(rs)
for (n in names(queries)) {
rs <- dbSendStatement(con,queries[[n]])
if (dbHasCompleted(rs))
dbClearResult(rs)
}
}
.reportDbTblDef <- function() {
return(list(
enable_fkey="PRAGMA foreign_keys=1;",
plot=paste(
"CREATE TABLE IF NOT EXISTS plot (",
"_id INTEGER PRIMARY KEY AUTOINCREMENT,",
"name TEXT,",
"type TEXT,",
"subtype TEXT,",
"json TEXT",
");"
),
clear="DELETE FROM plot;"
))
}
.dbImportPlot <- function(con,name,type,subtype=NULL,json) {
if (is.null(subtype))
query <- paste("INSERT INTO plot (name, type, subtype, json) ",
"VALUES (","'",name,"', ","'",type,"', NULL, :j)",sep="")
else
query <- paste("INSERT INTO plot (name, type, subtype, json) ",
"VALUES (","'",name,"', ","'",type,"', ","'",
subtype,"', :j)",sep="")
#print(query)
nr <- dbExecute(con,query,params=list(j=json))
return(nr)
}
.downloadJsLibs <- function(prPath,reportDb) {
disp("Downloading required JavaScript libraries...")
if (!file.exists(file.path(prPath$js,"pace.min.js")))
download.file(paste0("https://raw.github.com/HubSpot/pace/",
"v1.0.0/pace.min.js"),
file.path(prPath$js,"pace.min.js"))
if (!file.exists(file.path(prPath$js,"highcharts.js")))
download.file("https://code.highcharts.com/highcharts.js",
file.path(prPath$js,"highcharts.js"))
if (!file.exists(file.path(prPath$js,"highcharts-more.js")))
download.file("https://code.highcharts.com/highcharts-more.js",
file.path(prPath$js,"highcharts-more.js"))
if (!file.exists(file.path(prPath$js,"exporting.js")))
download.file("https://code.highcharts.com/modules/exporting.js",
file.path(prPath$js,"exporting.js"))
if (!file.exists(file.path(prPath$js,"offline-exporting.js")))
download.file(
"https://code.highcharts.com/modules/offline-exporting.js",
file.path(prPath$js,"offline-exporting.js"))
if (!file.exists(file.path(prPath$js,"export-data.js")))
download.file(
"https://code.highcharts.com/modules/export-data.js",
file.path(prPath$js,"export-data.js"))
if (!file.exists(file.path(prPath$js,"canvas2svg.js")))
download.file(
"http://jvenn.toulouse.inrae.fr/app/js/canvas2svg.js",
file.path(prPath$js,"canvas2svg.js"))
if (!file.exists(file.path(prPath$js,"jvenn.min.js")))
download.file(
"http://jvenn.toulouse.inrae.fr/app/js/jvenn.min.js",
file.path(prPath$js,"jvenn.min.js"))
if (reportDb == "sqlite") {
if (!file.exists(file.path(prPath$js,"sql.js")))
download.file(
"https://cdnjs.cloudflare.com/ajax/libs/sql.js/0.5.0/js/sql.js",
file.path(prPath$js,"sql.js"))
}
else if (reportDb == "dexie") {
if (!file.exists(file.path(prPath$js,"dexie.min.js")))
download.file(
"https://unpkg.com/dexie@2.0.4/dist/dexie.min.js",
file.path(prPath$js,"dexie.min.js"))
}
}
.formatForReport <- function(x,o=NULL,r=NULL,v=NULL) {
# Replace seqnames name
colnames(x)[1] <- "chromosome"
# Link to UCSC genome browser with position
if (!is.null(o)) {
x$chromosome <-
paste('<a href="http://genome.ucsc.edu/cgi-bin/hgTracks?db=',
getUcscOrganism(o),'&position=',paste(x$chromosome,":",
x$start,"-",x$end,sep=""),'" target="_blank">',x$chromosome,
'</a>',sep="")
}
# Link to gene pages - only for Ensembl, RefSeq, not custom GTFs! UCSC does
# not offer a related page.
if (!is.null(r)) {
if (r == "ensembl") {
h <- getHost(o,v)
if ("gene_id" %in% names(x)) # transLevel = "gene"
x$gene_id <- paste('<a href="',h,'/Gene/Summary?g=',x$gene_id,
'" target="_blank">',x$gene_id,'</a>',sep="")
# transLevel = "transcript"
else if ("transcript_id" %in% names(x)) {
# Stub
}
else if ("exon_id" %in% names(x)) { # transLevel = "exon"
# Stub
}
}
else if (r == "refseq") {
if ("gene_id" %in% names(x)) # transLevel = "gene"
x$gene_id <- paste('<a href="https://www.ncbi.nlm.nih.gov/',
'nuccore/',x$gene_id,'" target="_blank">',x$gene_id,'</a>',
sep="")
else if ("transcript_id" %in% names(x)) { # transLevel="transcript"
# Stub
}
else if ("exon_id" %in% names(x)) { # transLevel = "exon"
# Stub
}
}
}
# Round rpgm and fold change
tmpi1 <- grep("log2_normalized_fold_change_",colnames(x))
tmpi2 <- grep("rpgm_normalized_mean_counts_",colnames(x))
tmpi <- c(tmpi1,tmpi2)
if (length(tmpi) > 0)
x[,tmpi] <- round(x[,tmpi],digits=4)
# Round p-values and FDR
tmpi1 <- grep("p-value",colnames(x))
tmpi2 <- grep("FDR",colnames(x))
tmpi <- c(tmpi1,tmpi2)
if (length(tmpi) > 0)
x[,tmpi] <- round(x[,tmpi],digits=6)
colnames(x) <- gsub("log2_normalized_fold_change_","",colnames(x))
colnames(x) <- gsub("rpgm_normalized_mean_counts_","",colnames(x))
tmpi <- grep("p-value",colnames(x))
if (length(tmpi) > 0)
colnames(x)[tmpi] <- "p-value"
tmpi <- grep("FDR",colnames(x))
if (length(tmpi) > 0)
colnames(x)[tmpi] <- "FDR"
return(x)
}
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