Which statistical method to use for differential analysis?

Limma is a popular method for differential analysis that was first developed for microarray differential analysis. It addresses the problem of low sample sizes typical to whole-transcriptome studies by using the whole expression profile to make more stable estimates of gene expression variance.

Both edgeR and DESeq2 are both well-established methods developed for RNAseq data analysis. They differ in their method of data normalization and the algorithms used for estimation of dispersion. In general, edgeR is more powerful (detecting more DE features) but also comes with higher false positives. DESeq2 is more robust in estimating the DE features (i.e. low false positive rates). DESeq2 is more computationally intensive and may take a long time for large data sets. For more details about their implementation please refer to DESeq2 and EdgeR papers.

Please note,

  • EdgeR and DESeq2 are only designed for RNAseq data and will be disabled for microarray data.
  • Due to high computational resources required, DESeq2 will be disabled when your dataset contain over 50 samples.