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Single-cell Mapper (scMappR): Using ScRNA-seq to Infer the Cell-type Specificities of Differentially Expressed Genes

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Specialty Biology
Date 2021 Mar 3
PMID 33655208
Citations 17
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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.

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References
1.
Monroy M, Fang J, Li S, Ferrer L, Birkenbach M, Lee I . Chronic kidney disease alters vascular smooth muscle cell phenotype. Front Biosci (Landmark Ed). 2015; 20(4):784-95. PMC: 4331020. DOI: 10.2741/4337. View

2.
Leek J, Johnson W, Parker H, Jaffe A, Storey J . The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28(6):882-3. PMC: 3307112. DOI: 10.1093/bioinformatics/bts034. View

3.
Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

4.
Hui T, Cao Q, Wegrzyn-Woltosz J, ONeill K, Hammond C, Knapp D . High-Resolution Single-Cell DNA Methylation Measurements Reveal Epigenetically Distinct Hematopoietic Stem Cell Subpopulations. Stem Cell Reports. 2018; 11(2):578-592. PMC: 6093082. DOI: 10.1016/j.stemcr.2018.07.003. View

5.
Frishberg A, Peshes-Yaloz N, Cohn O, Rosentul D, Steuerman Y, Valadarsky L . Cell composition analysis of bulk genomics using single-cell data. Nat Methods. 2019; 16(4):327-332. PMC: 6443043. DOI: 10.1038/s41592-019-0355-5. View