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Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2
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DOI：10.1038/s41467-023-39923-2.png)
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Abstract
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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. Recent advances in multi-condition single-cell multi-cohort studies enable exploration of diverse cell states. Here, authors present scMerge2, an algorithm that allows integration of a large COVID-19 data collection with over five million cells to uncover distinct signatures of disease progression.
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