In ayurovsky/rnaGinesis: Simulate gene expression data

load("~/rnaGinesis/data/datasetGSE19830_LiverLungBrainRaw.RData")
tab = parsedGSE19830
w <- as.matrix(tab$sampleInfo@data$source_name_ch1)
h = matrix(nrow = 42, ncol = 3)
for (i in 1:42) {
  h[i,] <- (as.numeric(strsplit(as.character(w[i][1]), split=" ")[[1]][c(1, 5, 9)]))
}
h <- h/100
h 
w[1]

x <- row.names(tab$expression)
liver <- vector()
for(i in 1:31099) {
  liver <- c(liver, mean(tab$expression[i,1:3]))
}

brain <- vector()
for(i in 1:31099) {
  brain <- c(brain, mean(tab$expression[i,4:6]))
}

lung <- vector()
for(i in 1:31099) {
  lung <- c(lung, mean(tab$expression[i,7:9]))
}

vec <- cbind(liver, brain, lung)
row.names(vec) <- x
Dataset_LiverLungBrain <- list()
Dataset_LiverLungBrain$ex <- expm1(as.matrix(parsedGSE19830$expression))
Dataset_LiverLungBrain$unmixed <- expm1(as.data.frame(vec))
Dataset_LiverLungBrain$trueH <- h
Dataset_LiverLungBrain$trueW <- Dataset_LiverLungBrain$unmixed

save(Dataset_LiverLungBrain, file = "~/rnaGinesis/data/Dataset_LiverLungBrain.RData")