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Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2
Yingxin Lin; Yue Cao; Elijah Willie; Ellis Patrick; Jean Yee Hwa Yang.
Afiliación
  • 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 Yee Hwa Yang; University of Sydney
Preprint en En | PREPRINT-BIORXIV | ID: ppbiorxiv-519588
ABSTRACT
The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allow 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 generalised 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.
Licencia
cc_by_nc_nd
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Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Idioma: En Año: 2022 Tipo del documento: Preprint
Texto completo
Añadir a Mi BVS
Imprimir
XML
Buscar en Google
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Idioma: En Año: 2022 Tipo del documento: Preprint