Benchmark of Differential Gene Expression Analysis Methods for Inter-species RNA-Seq Data using a Phylogenetic Simulation Framework

Inter-species RNA-Seq datasets are increasingly common, and have the potential to answer new questions on gene expression patterns across the evolution. Single species differential expression analysis is a now well studied problem, that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available tools in various settings. Such benchmarks are still missing in the inter-species gene expression context. In this work, we take a first step in this direction by developing and implementing a new simulation framework. This tool builds on both the RNA-Seq and the Phylogenetic Comparative Methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the features of this new framework through a targeted simulation study, that reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for inter-species differential expression analysis. The tool has been integrated in the R package compcodeR freely available on Bioconductor.