This presentation will focus on gene ‐ level analysis of RNA ‐ seq experiments. From a software point of view, the talk will describe an R ‐ based pipeline for the analysis of RNA ‐ seq data using the Rsubread, edgeR and limma packages. The presentation will also describe some of the statistical principles underlying the analysis of RNA ‐ seq expression data.
A key principle for efficient analysis is to estimate the mean ‐ variance relationship correctly for the read counts. Another key is the need to allow for gene ‐ specific variability. The edgeR and limma packages provide access to linear modelling and empirical Bayes methods for analysing complex RNA ‐ seq experiments with only a limited number of replicates. As time permits, examples of downstream pathway analyses may also be given