I have to analyze data obtained from Affymetrix miRNA 4.0 microarray. It is the first time I have to analyze such kind of data and I wanted to be sure that I was doing it right. My main concerns are about the normalization of the probe's intensities and the filtering in limma.
# Normalization library(oligo) library(pd.mirna.4.0) celFiles <- list.celfiles("~/Desktop/Affymetrix_miRNA/RawData_miRNA", full.names=TRUE) rawData <- read.celfiles(celFiles, pkgname="pd.mirna.4.0") eset <- rma(rawData) # I did some plots and everything look really great after normalization. # Limma # I directly use the eset data to calculte the miRNAs differentially expressed.
1. Normalization: Should I do something else for the normalization or apply rma is usually enough? Should I do something with the information of the spike-in probes?
2. Filtering: I have seen that among the probes on this micro-array some are unrelevant for my analysis:
should I remove such probes before computing all the statistics from limma?
3. More globally, I found many tutorials talking about normalization of microarrays for genes but not for miRNAs. Are they differences in the processing of those two types of microarrays that I should know?
4. From what I have seen, some miRNAs are represented by several probes on the chip. They seem to be clustered during the normalization step and the creation of the expression dataset. Can someone explain me how it is done or point me to article/post that explain such thing?
Thanks in advance!