Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for many biological and medical applications as it allows users to measure gene expression levels in a cell type?specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq data sets with cell types that may overlap only partially and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering and in terms of avoiding overcorrection. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell type?specific differential gene expression comparisons across biopsy sites and disease conditions and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online.

Automated summary

Dmatch is a method for aligning multiple scRNA-seq experiments, leveraging an external expression atlas and kernel density matching to integrate diverse datasets and extract biological insights. The method performs well in simulations and shows great potential when applied to clinical samples, enabling cell type-specific differential gene expression comparisons and revealing shared cell populations.