A comparison of Methods for Differential Expression Analysis of RNA-seq Data

Few days back, we posted a large table with list of various RNAseq analysis programs. Before we got a chance to go through the programs to see how well they work, another interesting paper came out claiming to do the same.

Results

We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data.

Conclusions

Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

Please note that above paper only compares the differential expression aspect, whereas the earlier table also included tools for finding alternatively spliced genes, de novo transcriptome assembly, etc.