Hello,

I have a historic microarray dataset from an illumina bead based technology. For my data I only have an dataframe where the columns include, for each sample:

"AVG_Signal"
"NARRAYS"
"ARRAY_STDEV"
"Detection Pval"

I would like to run a comparison analysis using limma to get p adjusted values, however I am not sure what my data corresponds to based on instructions in the limma users guide.

I am more familiar with RNA-seq analysis using DESeq2. However have read posts which suggest Deseq2 is not applicable for microarray - is this correct?

What should I do regarding normalisation and comparison?

The within array normalisation function does not seem applicable.

I have proceeded straight to model.matrix(), lmFit() and makeContrasts() - without any normalisation - however I have only a very small number of differentially expressed genes as a result.

Any advice would be very helpful.

Kind Regards, Alex

The limma pipeline for Illumina Beadchips is described in Sections 4.6 and 17.3 of the limma User's Guide. Briefly, you use read.ilmn to read the file and neqc to background correct and normalize.

limma will read the intensities (AVE_Signal) and the detection p-values, and will use the detection p-values to infer the intensities of the background probes as part of the background correction step. Usually the NARRAYS entries will be all 1.

If you just have averages, you can't do much with those data. You need the raw data.