Abstract

The recent emergence of multi-sample multi-condition single-cell multi cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large data collection with over five million cells from 1000+ individuals, we demonstrate that the integration of multi-sample multi-condition scRNAseq from multiple cohorts reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression.