This webpage is created for sharing the datasets and analysis codes for the project that evaluates statistical approaches for differential expression (DE) in metatranscriptomics. We simulated large-scale synthetic microbial communities, comprising a broad range of settings representing different scenarios (e.g. spiked vs. null associations; confounded associations arising from species abundance, gene loss/gain or sequencing depth; paired MTX and MGX vs. MTX only, etc.)

We further provide the statistical results of the simulated datasets by applying six statistical models for identifying DE that make different assumptions about MTX normalization, MGX availability, and the relationship between DNA and RNA copy numbers.