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

Abstract

Differential gene expression analysis is a powerful technique to characterize biological systems. Differentially expressed genes (DEGs) are commonly identified by analysis of RNA sequencing datasets or mass spectrometry proteomics datasets. Increasingly, multi-omic studies include both RNA sequencing and proteomics from the same biospecimens. However, the common multi-omic DEG analysis practice involves separate DEG analysis by RNA and by protein with limited comparison by fold change correlations or Venn Diagrams. This disjoint practice may not utilize all of the available signal from both datatypes as transcripts and proteins have correlated expression levels. To integrate RNA and proteomics, we developed a new method, RNA protein meta-analysis called rpMeta. rpMeta takes RNA and protein expression data and calculates DEGs by RNA, by protein, and by meta-analysis models, following user-specified options. Spike-in, downsampling and cross-cohort benchmarks revealed that the meta-analysis model had greater sensitivity than RNA and protein models. In a real-world use case, the meta-analysis model discovered new genes and biological processes in lung cancer subtypes that were not detectable by the RNA or protein models. To streamline interpretation, rpMeta produces publication-ready data visualizations of the integrated DEGs. rpMeta is an open-source R package, available at https://github.com/zhangx8/rpMeta.