SegmentPlots.R
From Organic Design wiki
library(limma) library(NutriTools)
type <- list(GenePix = "GPR") platform <- "GenePix" typeplatform
sessionInfo() auxillaryDir <- "/Volumes/HD2/Data/Nutrigenomics/MultipleScans/Auxillary" dataDir <- file.path("/Volumes/HD2/Data/Nutrigenomics/MultipleScans/", typeplatform) targets <- readTargets(file.path(auxillaryDir, "targets.txt"))
- 1) ------------------------ No background correction ------------------------ #
RG <- readGPR(dataDir=dataDir) colnames(RG) <- paste(targets$Filetype, targets$Scan, sep="") MA <- MA.RG(RG, bc.method="none")
slides <- colnames(RG)
plotDir <- "DiagnosticImages/ArrowPlots" dir.create(plotDir, recursive=TRUE)
slides <- colnames(RG)
for(i in seq(1,32, by=3)){
for(j in 0:1) {
print(paste("I:",i, " J:",j, sep=""))
scanType <- c("Low","Medium","High")
# Calculate MA scale slope coefficients
coefs <- rep(NA, length=nrow(MA))
for(k in seq(nrow(MA))) {
x <- cbind(1, MA$A[k, (i+j):(i+j+1)])
y <- MA$M[k, (i+j):(i+j+1)]
if(!is.na(c(x[,2])) %*% is.na(y)) {
coefs[k] <- lm.fit(x,y)$coef[2]
}
}
# Create colour lookup table
cols <- rev(topo.colors(length(coefs)))
pdf(file.path(plotDir, paste(platform, "-RGplot-", targets[i, "Filetype"], "-", scanType[j+2],"vs",scanType[j+1], ".pdf", sep="")), version = "1.4")
plot(5:16, 5:16, type="n", xlab=expression(log[2](G)), ylab=expression(log[2](R)))
counter <- 1
for( k in rev(order(abs(coefs)))) {
rgbCol <- col2rgb(cols[counter])
counter <- counter+1
arrows(log2(RG$G[k,slides[i+j]]), log2(RG$R[k, slides[i+j]]), log2(RG$G[k,slides[i+j+1]]), log2(RG$R[k, slides[i+j+1]]), col=rgb(rgbCol[1], rgbCol[2], rgbCol[3], 0.5*255, maxColorValue=255), length=0.05)
}
# Overlay poor quality spots based on weight from either scan
for(k in seq(nrow(RG))) {
if(RG$weights[k, slides[i]]==0 | RG$weights[k, slides[i+j]]==0) {
arrows(log2(RG$G[k,slides[i+j]]), log2(RG$R[k, slides[i+j]]), log2(RG$G[k,slides[i+j+1]]), log2(RG$R[k, slides[i+j+1]]), col=rgb(0,0,0, 0.5), length=0.05)
}
}
dev.off()
pdf(file.path(plotDir, paste(platform,"-MAplot-", targets[i, "Filetype"], "-", scanType[j+2],"vs",scanType[j+1], ".pdf", sep="")), version = "1.4")
plot(5:16, -5:6, type="n", xlab=expression(A==over(log[2](R)-log[2](G),2)), ylab=expression(M==log[2](R)-log[2](G)))
counter <- 1
for( k in rev(order(abs(coefs)))) {
rgbCol <- col2rgb(cols[counter])
counter <- counter+1
arrows((MA$A[k,slides[i+j]]),(MA$M[k, slides[i+j]]), (MA$A[k,slides[i+j+1]]), (MA$M[k, slides[i+j+1]]), col=rgb(rgbCol[1], rgbCol[2], rgbCol[3], 0.5*255, maxColorValue=255), length=0.05)
}
# Overlay poor quality spots based on weight from either scan
for(k in seq(nrow(MA))) {
if(MA$weights[k, slides[i]]==0 | MA$weights[k, slides[i+j]]==0) {
arrows((MA$A[k,slides[i+j]]),(MA$M[k, slides[i+j]]), (MA$A[k,slides[i+j+1]]), (MA$M[k, slides[i+j+1]]), col=rgb(0,0,0, 0.2), length=0.05)
}
}
dev.off()
}
}
- 2) ------------------------- Background correction -------------------------- #
RGbc <- backgroundCorrect(RG, method="subtract") MAbc <- MA.RG(RGbc)
slides <- colnames(RGbc) for(i in seq(1,32, by=3)){
for(j in 0:1) {
print(paste("I:",i, " J:",j, sep=""))
scanType <- c("Low","Medium","High")
# Calculate MA scale slope coefficients
coefs <- rep(NA, length=nrow(MA))
for(k in seq(nrow(MA))) {
x <- cbind(1, MA$A[k, (i+j):(i+j+1)])
y <- MA$M[k, (i+j):(i+j+1)]
if(!is.na(c(x[,2])) %*% is.na(y)) {
coefs[k] <- lm.fit(x,y)$coef[2]
}
}
# Create colour lookup table
cols <- rev(topo.colors(length(coefs)))
pdf(file.path(plotDir, paste(platform,"-RGplot-bc-", targets[i, "Filetype"], "-", scanType[j+2],"vs",scanType[j+1], ".pdf", sep="")), version = "1.4")
plot(c(0,16), c(0,16), type="n", xlab=expression(log[2](G)), ylab=expression(log[2](R)))
counter <- 1
for(k in seq(nrow(MAbc))) {
arrows(log2(RGbc$G[k,slides[i+j]]), log2(RGbc$R[k, slides[i+j]]), log2(RGbc$G[k,slides[i+j+1]]), log2(RGbc$R[k, slides[i+j+1]]), col=rgb(rgbCol[1], rgbCol[2], rgbCol[3], 0.5*255, maxColorValue=255), length=0.05)
}
# Overlay poor quality spots based on weight from either scan
for(k in seq(nrow(MA))) {
if(RG$weights[k, slides[i]]==0 | RG$weights[k, slides[i+j]]==0) {
arrows(log2(RG$G[k,slides[i+j]]), log2(RG$R[k, slides[i+j]]), log2(RG$G[k,slides[i+j+1]]), log2(RG$R[k, slides[i+j+1]]), col=rgb(0,0,0,0.5), length=0.05)
}
}
dev.off()
pdf(file.path(plotDir, paste(platform,"-MAplot-bc-", targets[i, "Filetype"], "-", scanType[j+2],"vs",scanType[j+1], ".pdf", sep="")), version = "1.4")
plot(c(0,16), c(-8,8), type="n", xlab=expression(A==over(log[2](R)-log[2](G),2)), ylab=expression(M==log[2](R)-log[2](G)))
coefs <- rep(0, length=nrow(MAbc)) # NA's will not evaluate a slope so plot these first by assigning 0
for(k in seq(nrow(MAbc))) {
x <- cbind(1, MAbc$A[k, (i+j):(i+j+1)])
y <- MAbc$M[k, (i+j):(i+j+1)]
if(!is.na(c(x[,2])) %*% is.na(y)) {
coefs[k] <- lm.fit(x,y)$coef[2]
}
}
counter <- 1
for( k in rev(order(abs(coefs)))) { # Reverse or forward ordering is informative here
rgbCol <- col2rgb(cols[counter])
counter <- counter+1
arrows((MAbc$A[k,slides[i+j]]),(MAbc$M[k, slides[i+j]]), (MAbc$A[k,slides[i+j+1]]), (MAbc$M[k, slides[i+j+1]]), col=rgb(rgbCol[1], rgbCol[2], rgbCol[3], maxColorValue=255), length=0.05) # 0.5*255,
}
# Overlay poor quality spots based on weight from either scan
for(k in seq(nrow(MA))) {
if(MA$weights[k, slides[i]]==0 | MA$weights[k, slides[i+j]]==0) {
arrows((MA$A[k,slides[i+j]]),(MA$M[k, slides[i+j]]), (MA$A[k,slides[i+j+1]]), (MA$M[k, slides[i+j+1]]), col=rgb(0,0,0,0.5), length=0.05)
}
}
dev.off()
}
}
