Integrative Differential Expression and Gene Set Enrichment Analysis Using Summary Statistics for Scrna-Seq Studies
ARTICLE https://doi.org/10.1038/s41467-020-15298-6 OPEN Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies ✉ Ying Ma 1,7, Shiquan Sun 1,7, Xuequn Shang2, Evan T. Keller 3, Mengjie Chen 4,5 & Xiang Zhou 1,6 Differential expression (DE) analysis and gene set enrichment (GSE) analysis are commonly applied in single cell RNA sequencing (scRNA-seq) studies. Here, we develop an integrative 1234567890():,; and scalable computational method, iDEA, to perform joint DE and GSE analysis through a hierarchical Bayesian framework. By integrating DE and GSE analyses, iDEA can improve the power and consistency of DE analysis and the accuracy of GSE analysis. Importantly, iDEA uses only DE summary statistics as input, enabling effective data modeling through com- plementing and pairing with various existing DE methods. We illustrate the benefits of iDEA with extensive simulations. We also apply iDEA to analyze three scRNA-seq data sets, where iDEA achieves up to five-fold power gain over existing GSE methods and up to 64% power gain over existing DE methods. The power gain brought by iDEA allows us to identify many pathways that would not be identified by existing approaches in these data. 1 Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. 2 School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, P.R. China. 3 Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA. 4 Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. 5 Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
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