Dear Bioconductor Community,
as I'm currently writting a report about my gene expression analysis on two affymetrix microarray datasets, regarding differential expression, i found an paper (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012336) mentioning the necessary assumptions and "possible requirements" in order to use different categories of statistical tests. In my case, i performed paired limma moderated t-test to check for gene expression alterations between cancer and control samples in each patient comprized the 2 above mentioned datasets, but as im "fresh in R" i have checked the normality of my data(boxplots,histograms,Q-Q plots after normalization), but i havent cheched for equal variance !! As i have read from the above paper limma is a homoscedastic test(thus makes the assumption for equal variances between the groups of interest) could i have violated my results regarding the false positive rate ? Or due to the paired nature of my analysis(and thus not generally two group comparison) this does not affect my study ?
Finally, if this pinpoint a great concern, how could i check in R and in both datasets prior of using limma the homoscedasticity of my data(test for unequal variances) regarding the two groups(cancer and control samples) ?
Any advice or consideration on this matter would be grateful !!