RoPE: A robust profile likelihood method for differential gene expression analysis

Variation in RNA-Seq data creates modeling challenges for differential gene expression (DE) analysis. Statistical approaches address conventional small sample sizes and implement empirical Bayes or non-parametric tests, but frequently produce different conclusions. Increasing sample sizes enable proposal of alternative DE paradigms. Here we develop RoPE, which uses a data-driven adjustment for variation and a robust profile likelihood ratio DE test. Simulation studies show RoPE can have improved performance over existing tools as sample size increases and has the most reliable control of error rates. Application of RoPE demonstrates that an active Pseudomonas aeruginosa infection downregulates the SLC9A3 Cystic Fibrosis modifier gene.

Automated summary

Variation in RNA-Seq data presents modeling challenges for DE analysis. Statistical methods address small sample sizes but often produce different conclusions. RoPE, a new approach, adjusts for variation and uses a robust DE test, showing improved performance and reliable error rate control.