Combining RNA-seq and proteomics boosts sensitivity to detect differentially expressed genes by rpMeta

Meta-analysis of RNA and proteomics data with rpMeta enhances sensitivity to detect differentially expressed genes, outperforming individual RNA or protein models in lung cancer subtypes.

• Why it matters: Integrating RNA and proteomics data can reveal biological insights missed by separate analyses, but current methods often underutilize the combined signal, limiting discovery potential.
• What they did: The authors developed rpMeta, an open-source R package that calculates DEGs through RNA, protein, and meta-analysis models, validated with benchmarks including spike-in and downsampling tests.
• The result: The meta-analysis model identified new genes and biological processes in lung cancer subtypes that were not detectable by individual models, enabling more comprehensive biological interpretation.