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04_ProcessResults.R
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04_ProcessResults.R
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#' Retrieve significant gene-metabolite pairs, based on adjusted p-values.
#' For each gene-metabolite pair that is statistically significant, calculate the
#' correlation within group1 (e.g. cancer) and the correlation within group2 (e.g.
#' non-cancer). Users can then remove pairs with a difference in correlations between
#' groups 1 and 2 less than a user-defined threshold.
#'
#' @include internalfunctions.R
#'
#' @param inputResults IntLimResults object with model results (output of RunIntLim())
#' @param inputData MultiDataSet object (output of ReadData()) with gene expression,
#' metabolite abundances, and associated meta-data
#' @param pvalcutoff cutoff of FDR-adjusted p-value for filtering (default 0.05)
#' @param diffcorr cutoff of differences in correlations for filtering (default 0.5)
#' @param corrtype spearman or pearson or other parameters allowed by cor() function (default
#' spearman)
#' @param treecuts user-selected number of clusters (of gene-metabolite pairs) to cut the tree into
#' @return IntResults object with model results (now includes correlations)
#'
#' @examples
#' \dontrun{
#' dir <- system.file("extdata", package="IntLIM", mustWork=TRUE)
#' csvfile <- file.path(dir, "NCItestinput.csv")
#' mydata <- ReadData(csvfile,metabid='id',geneid='id')
#' myres <- RunIntLim(mydata,stype="PBO_vs_Leukemia")
#' myres <- ProcessResults(myres,mydata,treecuts=2)
#' }
#' @export
ProcessResults <- function(inputResults,
inputData,
pvalcutoff=0.05,
diffcorr=0.5,
corrtype="spearman",
treecuts = 0
){
if(inputResults@outcome == "metabolite") {
mydat <-inputResults@interaction.adj.pvalues}
#mydat <- reshape2::melt(inputResults@interaction.adj.pvalues)}
else if (inputResults@outcome == "gene") {
mydat <-t(inputResults@interaction.adj.pvalues)}
#mydat <- reshape2::melt(t(inputResults@interaction.adj.pvalues))}
incommon <- getCommon(inputData,inputResults@stype)
p <- incommon$p
gene <- incommon$gene
metab <- incommon$metab
if(length(unique(p)) !=2) {
stop(paste("IntLim currently requires only two categories. Make sure the column",inputResults@stype,"only has two unique values"))
}
gp1 <- which(p == unique(p)[1])
cor1.m <- stats::cor(t(gene[rownames(mydat),gp1]),t(metab[colnames(mydat),gp1]),method=corrtype)
gp2 <- which(p == unique(p)[2])
cor2.m <- stats::cor(t(gene[rownames(mydat),gp2]),t(metab[colnames(mydat),gp2]),method=corrtype)
if(pvalcutoff == 1) { #(no filtering)
temp <- reshape2::melt(cor1.m)
fincor1 <- as.numeric(temp[,"value"])
temp <- reshape2::melt(cor2.m)
fincor2 <- as.numeric(temp[,"value"])
genenames <- as.character(temp[,1])
metabnames <- as.character(temp[,2])
} else {
keepers <- which(mydat <= pvalcutoff, arr.ind=T)
fincor1 <- as.numeric(apply(keepers,1,function(x)
cor1.m[x[1],x[2]]))
fincor2 <- as.numeric(apply(keepers,1,function(x)
cor2.m[x[1],x[2]]))
genenames <- as.character(rownames(cor1.m)[keepers[,1]])
metabnames <- as.character(colnames(cor1.m)[keepers[,2]])
}
mydiffcor = abs(fincor1-fincor2)
keepers2 <- which(mydiffcor >= diffcorr)
inputResults@filt.results <- data.frame(metab=metabnames[keepers2],
gene=genenames[keepers2])
inputResults@filt.results <- cbind(inputResults@filt.results,fincor1[keepers2],fincor2[keepers2])
colnames(inputResults@filt.results)[3:4]=paste0(setdiff(as.character(unlist(unique(p))),""),"_cor")
diff.corr <- inputResults@filt.results[,4] - inputResults@filt.results[,3]
inputResults@filt.results <- cbind(inputResults@filt.results, diff.corr)
if(inputResults@outcome == "metabolite") {
adjp <- reshape2::melt(inputResults@interaction.adj.pvalues)
p <- reshape2::melt(inputResults@interaction.pvalues)
} else if (inputResults@outcome == "gene") {
adjp <- reshape2::melt(t(inputResults@interaction.adj.pvalues))
p <- reshape2::melt(t(inputResults@interaction.pvalues))
}
cornames <- paste(as.character(inputResults@filt.results[,"metab"]),as.character(inputResults@filt.results[,"gene"]))
rownames(p) <- paste(as.character(p[,2]),as.character(p[,1]))
rownames(adjp) <- paste(as.character(adjp[,2]),as.character(adjp[,1]))
outp <- p[cornames,]
outpadj <- adjp[cornames,]
inputResults@filt.results = cbind(inputResults@filt.results,outp$value, outpadj$value)
colnames(inputResults@filt.results)[6:7]=c("Pval","FDRadjPval")
if (treecuts > 0){
hc.rows<- stats::hclust(stats::dist(inputResults@filt.results[,c(3,4)]))
cluster <- stats::cutree(hc.rows, k = treecuts)
inputResults@filt.results = cbind(inputResults@filt.results, cluster)
}
print(paste(nrow(inputResults@filt.results), 'gene-metabolite pairs found given cutoffs'))
return(inputResults)
}
#' Create results table, which includes significant gene:metabolite pairs, associated p-values,
#' and correlations in each category evaluated.
#'
#' @param inputResults IntLimResults object with model results (output of ProcessResults())
#'
#' @examples
#' \dontrun{
#' dir <- system.file("extdata", package="IntLIM", mustWork=TRUE)
#' csvfile <- file.path(dir, "NCItestinput.csv")
#' mydata <- ReadData(csvfile,metabid='id',geneid='id')
#' myres <- RunIntLim(mydata,stype="PBO_vs_Leukemia")
#' myres <- ProcessResults(myres,mydata)
#' mytable <- CreateResultsTable(myres)
#' }
#' @export
# CreateResultsTable <- function(inputResults) {
# a<-inputResults@corr
# a$cordiff<-round(abs(a[,3]-a[,4]),3)
# a[,3]<-round(a[,3],2)
# a[,4]<-round(a[,4],2)
# p <- padj <- c()
# if(inputResults@outcome=="metabolite") {
# for (i in 1:nrow(a)) {
# g <- which(rownames(inputResults@interaction.pvalues) == a$gene[i])
# m <- which(colnames(inputResults@interaction.pvalues) == a$metab[i])
# if(length(g)==0 || length(m)==0) {p<-c(p,NA);padj<-c(padj,NA)} else {
# p <- c(p,inputResults@interaction.pvalues[g,m])
# padj <- c(padj,inputResults@interaction.adj.pvalues[g,m])
# padj <- c(padj,inputResults@interaction.adj.pvalues[a$gene[i],a$metab[i]])
# }
# }
# } else if (inputResults@outcome=="gene") {
# for (i in 1:nrow(a)) {
# g <- which(rownames(inputResults@interaction.pvalues) == a$gene[i])
# m <- which(colnames(inputResults@interaction.pvalues) == a$metab[i])
# if(length(g)==0 || length(m)==0) {p<-c(p,NA)} else {
# p <- c(p,inputResults@interaction.pvalues[a$metab[i],a$gene[i]])
# p <- c(p,inputResults@interaction.pvalues[g,m])
# padj <- c(padj,inputResults@interaction.adj.pvalues[m,g])
# }
# }
# }
# else {stop("Outcome should be either 'metabolite' or 'gene'")}
# a$pval <- p
# a$adjpval <- padj
# table<-a[order(a$adjpval,decreasing = TRUE),]
# rownames(table) <- NULL
# return(table)
# }
#
#