## This code will be one complete example of reading in the raw data,
## looking at it, computing TICs and SNRs and MNFs etc. I will then do
## a package.skeleton at the end to create a package of all the parts.
if(FALSE) {
tmp <- ReadSet("path to top level directory") ## edit this line
obj <- toMatrix(tmp)
rm(tmp)
## Pre-processing - replace NAs by 0 and add 1, robust regression and log transform
obj$data[is.na(obj$data)] <- 0
obj$data <- obj$data + 1
obj <- BGcorrect(obj)
## Compute TIC
obj$TIC <- TIC(obj)
## plotTIC
plotTIC(obj,log="")
## Display intensities at point of highest TIC value
ss <- which.max(obj$TIC)
library(colorspace,lib.loc="/home/cli065/Rpackages")
with(obj,display(data[,ss],X,Y))
## Compute SNR
obj$SNR <- SNRobj(obj)
## plotSNR
plotSNR(obj)
## Display intensities with highest SNR
ss <- which.max(obj$SNR)
with(obj,display(data[,ss],X,Y))
## Compute Noise
obj$noise <- with(obj,apply(data, MARGIN = 2, FUN = computeNoise, x = X, y = Y))
## Display raw data, spatial smooth and noise at one mz value
ss <- which.max(obj$SNR)
par(mfrow=c(1,2))
with(obj,display(data[,ss],X,Y))
with(obj,display(noise[,ss],X,Y))
## Compute MNF transform - first 12 bands computed
obj$MNF <- computeMNF(obj)
## plot first 4 bands of MNF
plotMNF(obj,n=4)
}
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