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

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.

Conserved transcriptional programming across sex and species after peripheral nerve injury predicts treatments for neuropathic pain

“…Transcription Factor enrichment analysis was performed using ChEA3, a comprehensive curated library of transcription factor targets that combines results from ENCODE and literature-based ChIP-seq experiments (Keenan et al, 2019). Deconvolution of bulk RNA seq into immune cell types was evaluated using scMappR (Sokolowski et al, 2021). The Drug Gene Interaction Database (DGIdb v4.1.0, www.dgidb.org) has been used to predict potential therapy for pain interactome (Freshour et al, 2021) The integrated value of influence (IVI) was calculated by Influential R package (Salavaty et al, 2020).…”

“…The predominant cellular components defined by GO analysis were related to membranes and those of GO molecular function were related to protein binding and G-protein coupled purinergic nucleotide receptor activity (Figure 3c). To interrogate cell-type specificity for the common DEGs, we deconvolved the bulk RNA-seq dataset with scMappR (Sokolowski et al, 2021), which uses publicly available single cell-RNAseq data from the Panglao database (Franzén et al, 2019). Deconvolution analysis (Figure 3d and Table S3) revealed five cell types with FDR less than 0.05 with the top three being microglia cells (FDR = 6.7 * 10 À19 , Odds Ratio = 20.1), macrophages (FDR = 5.1 * 10 À12 , Odds Ratio = 13.9) and monocytes (FDR = 15.8 * 10 À5 , Odds Ratio = 8.56).…”