Single-cell mapper (scMappR) – using scRNA-seq to infer the cell-type specificities of differentially expressed genes

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.

University of Toronto researchers have developed 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, the researchers 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, the researchers 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.

Schematic of the data required to run scMappR
and the primary functionalities that scMappR provides

Schematic of the data required to run scMappR and the primary functionalities that scMappR provides. scMappR requires input RNA-seq count data, a list of differentially expressed genes, and a signature matrix (provided by the user or scMappR). For each gene, scMappR then makes cell-type expression independent of estimated cell-type proportions. scMappR then integrates cell-type expression, cell-type proportion, and the ratio of cell-type proportions between biological conditions to generate cell-weighted Fold-changes (cwFold-changes). These cwFold-changes are then visualized (bottom left) and re-ranked before scMappR computes and plots cell-type specific pathway analyses (bottom right).

scMappR requires input RNA-seq count data, a list of differentially expressed genes, and a signature matrix (provided by the user or scMappR). For each gene, scMappR then makes cell-type expression independent of estimated cell-type proportions. scMappR then integrates cell-type expression, cell-type proportion, and the ratio of cell-type proportions between biological conditions to generate cell-weighted Fold-changes (cwFold-changes). These cwFold-changes are then visualized (bottom left) and re-ranked before scMappR computes and plots cell-type specific pathway analyses (bottom right).

Sokolowski DJ, Faykoo-Martinez M, Erdman L, Hou H, Chan C, Zhu H, Holmes MM, Goldenberg A, Wilson MD. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics 3(1): lqab011. [article]

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