Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.265298v1?rss=1

Authors: Sokolowski, D. J., Faykoo-Martinez, M., Erdman, L., Hou, H., Chan, C., Zhu, H., Holmes, M. M., Goldenberg, A., Wilson, M. D.

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 integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After 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. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ~100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.

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