# Load the assay matrix (mtx.sub) and transform it 3 ways load("../extdata/ampAD.154genes.mef2cTFs.278samples.RData") mtx.tmp <- mtx.sub - min(mtx.sub) + 0.001 mtx.log2 <- log2(mtx.tmp) mtx.asinh <- asinh(mtx.sub) suppressMessages(library(limma)) mtx.voom <- voom(mtx.sub)$E # Plot them all par(mfrow = c(4,1)) par(family = "sans") hist(mtx.sub, main = "As-is Data") hist(mtx.log2, main = "Log2-Transformed Data") hist(mtx.asinh, main = "Asinh-Transformed Data") hist(mtx.voom, main = "Voom-Transformed Data") cat("As-is :",fivenum(mtx.sub),"\n") cat(" Log2 :",fivenum(mtx.log2),"\n") cat("Asinh :",fivenum(mtx.asinh),"\n") cat(" Voom :",fivenum(mtx.voom),"\n") # Source the desired function and generate the table suppressMessages(source("../utils/evaluateAllSolvers.R")) tbl.all <- assess_ampAD154AllSolversAndDistributions() tbl.all dim(tbl.all) # Plot all pairs par(family = "sans") pairs(tbl.all[2:18], labels = names(tbl.all)[2:18]) head(tbl.all[,c(1,2,3,4,5,18)],10) # For each pairwise comparison, find correlation cor(tbl.all[2:5]) head(tbl.all[,c(1,6:9,18)],10) cor(tbl.all[6:9]) head(tbl.all[,c(1,10:13,18)],10) cor(tbl.all[10:13]) head(tbl.all[,c(1,14:17,18)],10) cor(tbl.all[14:17]) head(tbl.all[,c(1,2,6,10,14,18)],10) cor(tbl.all[c(2,6,10,14,18)]) head(tbl.all[,c(1,3,7,11,15,18)],10) cor(tbl.all[c(3,7,11,15,18)]) head(tbl.all[,c(1,4,8,12,16,18)],10) cor(tbl.all[c(4,8,12,16,18)]) head(tbl.all[,c(1,5,9,13,17,18)],10) cor(tbl.all[c(5,9,13,17,18)])