We have discussed differential gene expression analysis in one of our previous recipes. Another popular pipeline for the purpose is provided by the
limma software package, which we saw in Chapter 5, Analyzing Microarray Data with R. It can handle multiple experiments via Empirical Bayes statistical methods and uses normalized read counts for each gene. This recipe will explain the use of the
limma package from Bioconductor for differential gene analysis with NGS data.
Besides using the
limma library, we will use the
Pasilla dataset here. The dataset can be obtained from Bioconductor and consists of sequence counts from a perturbation experiment in Drosophila. To know more about the ...