Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

Yingxin Lin, Yue Cao, Elijah Willie, Ellis Patrick and Jean Y. H. Yang ()
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Yingxin Lin: The University of Sydney
Yue Cao: The University of Sydney
Elijah Willie: The University of Sydney
Ellis Patrick: The University of Sydney
Jean Y. H. Yang: The University of Sydney

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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 COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Date: 2023
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DOI: 10.1038/s41467-023-39923-2

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