require(psychTools) load("~/Downloads/pHeatMap_data/sessionData.RData")
if (is.null(scale)) { heatmapData$scale = "none" } else { heatmapData$scale = "row" } if (length(addColNames) > 0 & moreOptions) { heatmapData$annotation_col <- proje[rownames(heatmapData$annotation_col), addColNames, drop = FALSE] } if (sum(orderColNames %in% colnames(proje)) > 0 & moreOptions) { heatmapData$cluster_cols <- FALSE colN <- rownames(dfOrder(proje, orderColNames)) colN <- colN[colN %in% colnames(heatmapData$mat)] heatmapData$mat <- heatmapData$mat[, colN, drop = FALSE] } do.call(TRONCO::pheatmap, heatmapData)
library("PerformanceAnalytics") chart.Correlation(heatmapData$mat, histogram=TRUE, pch=19)
library("Hmisc") colnames(heatmapData$mat) rownames(proje) colnames(proje) nums <- unlist(lapply(proje, is.numeric)) numProje = proje[,nums] colnames(numProje) numProje <- t(numProje)[,colnames(heatmapData$mat)] rownames(numProje) corrInput <- as.matrix(rbind(numProje,heatmapData$mat)) rownames(corrInput) res2 <- rcorr(t(corrInput)) # res2 sum(rownames(res2$P) %in% colnames(proje[, nums])) # ++++++++++++++++++++++++++++ # flattenCorrMatrix # ++++++++++++++++++++++++++++ # cormat : matrix of the correlation coefficients # pmat : matrix of the correlation p-values flattenCorrMatrix <- function(cormat, pmat) { ut <- upper.tri(cormat) data.frame( row = rownames(cormat)[row(cormat)[ut]], column = rownames(cormat)[col(cormat)[ut]], cor =(cormat)[ut], p = pmat[ut] ) } cormat = res2$r pmat = res2$P ut <- upper.tri(upper.tri(res2$r)) flatMat <- flattenCorrMatrix(res2$r, res2$P) flatMat[flatMat$column %in% colnames(proje[, nums]) & (!flatMat$row %in% colnames(proje[, nums])) , ] flatMat = flatMat[order(flatMat$cor, decreasing = F),] DT::datatable(flatMat[flatMat$row == "PC5" & flatMat$p < 0.005,])
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