I am using limma package to analyze Illumina iscan micro array data. I am following the manual but I have some question regarding the normalization method and the arrayweight function. My design is quite simple, my samples coming from human patients, I have 3 experimental groups + normal group. Each group has at least 3 replicates. The data was extracted without background correction and without normalization.
I read the data with read.ilm function. As normalization method I am using the neqc function. I do not have the control probe file but, as explained in the manual page, this would not be a problem because the negative control probes are inferred from detection p.values.
fileNameProbe = "SampleProbe_noNorm_noSubBack_30-10-12.txt" x <- read.ilmn(files=fileNameProbe,other.columns = "Detection") y <- neqc(x)
Is this correct or is it better to use normalizeWithinArrays?
As suggested in the manual, for human samples and for samples that have different array qualities, I am using arrayWeights function. As explained in this post:
the array weight function affects the logFC values. So my question is: how the array weighted logFC has to be interpreted? In what way is it different from standard logFC?
Any suggestion is really appreciated.
sessionInfo() R version 3.2.4 (2016-03-10) Platform: x86_64-apple-darwin13.4.0 (64-bit) Running under: OS X 10.11.4 (El Capitan) locale:  it_IT.UTF-8/it_IT.UTF-8/it_IT.UTF-8/C/it_IT.UTF-8/it_IT.UTF-8 attached base packages:  stats graphics grDevices utils datasets methods base other attached packages:  gplots_3.0.1 limma_3.26.9 loaded via a namespace (and not attached):  tools_3.2.4 KernSmooth_2.23-15 gdata_2.17.0 caTools_1.17.1 bitops_1.0-6 gtools_3.5.0