scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

Significance Single-cell RNA-sequencing (scRNA-seq) profiling has exploded in recent years and enabled new biological knowledge to be discovered at the single-cell level. Successful and flexible integration of scRNA-Seq datasets from multiple sources promises to be an effective avenue to obtain further biological insights. This study presents a comprehensive approach to integration for scRNA-seq data analysis. It addresses the challenges involved in successful integration of scRNA-seq datasets by using the knowledge of genes that appear not to change across all samples and a robust algorithm to infer pseudoreplicates between datasets. This information is then consolidated into a single-factor model that enables tailored incorporation of prior knowledge. The effectiveness of scMerge is demonstrated by extensive comparison with other approaches.