Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data.

ABSTRACT: Benchmarking RNA-seq differential expression analysis methods using spike-in and simulated RNA-seq data has often yielded inconsistent results. The spike-in data, which were generated from the same bulk RNA sample, only represent technical variability, making the test results less reliable. We compared the performance of 12 differential expression analysis methods for RNA-seq data, including recent variants in widely used software packages, using both RNA spike-in and simulation data for negative binomial (NB) model. Performance of edgeR, DESeq2, and ROTS was particularly different between the two benchmark tests. Then, each method was tested under most extensive simulation conditions especially demonstrating the large impacts of proportion, dispersion, and balance of differentially expres

SUBMITTER: Baik B 

PROVIDER: S-EPMC7192453 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature