Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes. (23rd February 2021)

• Record Type: Journal Article
• Authors: Sokolowski, Dustin J; Faykoo-Martinez, Mariela; Erdman, Lauren; Hou, Huayun; Chan, Cadia; Zhu, Helen; Holmes, Melissa M; Goldenberg, Anna; Wilson, Michael D
• Is Part Of: NAR genomics and bioinformatics. Volume 3:issue 1(2021)
• Publication Date: 2021-02-23
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqab011
• Languages: English
• ISSNs: 2631-9268

Abstract

Abstract: RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.