Single-cell mapper (scMappR): using scRNA-seq to infer the 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 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.

The genetic source tracking of human urinary exosomes

“…We next investigated the source of differentially expressed genes (DEGs) of cancers and the functionality of exosomal RNAs for cancer diagnosis. Initially, we used a single-cell mapper (scMappR) ( 7 ) to identify the cell type traced for differentiating cancer and control based on both exosomal RNAs and scRNA-seq datasets. We then assigned cell types contributed by the DEGs and determined the cell types with the highest cell-weighted fold (cwFold) change.…”

Time series transcriptome analysis uncovers regulatory networks and a role for the circadian clock in theDrosophila melanogasterfemale’s response to Sex Peptide

“…We identified 25 clusters within group 1, 16 clusters within group 2, and 21 clusters within group 3. On each of these clusters, we ran a motif enrichment (Aibar et al 2017) and GO enrichment analysis, and we made use of publicly available single-cell RNA-seq data of the female head to predict in which cell types these expression changes might occur (Sokolowski et al 2021; Li et al 2021).…”

“…We normalized and scaled the data and used the FindMarkers function to compare each of the annotated cell types with all other cell types in the dataset. The resulting marker genes, log 2 fold changes and p-values were used to create a signature matrix for the R package scMappR (Sokolowski et al 2021). scMappR uses this signature matrix to estimate cell type proportions in bulk RNA-seq data.…”

Integrative transcriptomic profiling of a mouse model of hypertension-accelerated diabetic kidney disease

“…Although recent developments in transcriptomics enable analysis at the single-cell or single-nucleus level to identify and characterize cell types, these techniques have a low gene detection per cell and a high cost, resulting in limited sensitivity to identify differentially expressed genes (DEGs) at low expression levels ( Conesa et al, 2016 ; Wang et al, 2019 ). Therefore, an integrative approach leveraging the cell-level resolution of single-nucleus (sn)RNAseq with the sensitivity of bulk RNAseq has proven to be instrumental for gaining detailed insight into the underlying molecular mechanisms involved in specific cell types ( Barwinska et al, 2021 ; Sokolowski et al, 2021 ).…”