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Nat Methods. 2019 Aug;16(8):695-698. doi: 10.1038/s41592-019-0466-z. Epub 2019 Jul 15.

Joint analysis of heterogeneous single-cell RNA-seq dataset collections.

Author information

1
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
2
Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
3
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter_kharchenko@hms.harvard.edu.
4
Harvard Stem Cell Institute, Cambridge, MA, USA. peter_kharchenko@hms.harvard.edu.

Abstract

Single-cell RNA sequencing is often applied in study designs that include multiple individuals, conditions or tissues. To identify recurrent cell subpopulations in such heterogeneous collections, we developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.

PMID:
31308548
PMCID:
PMC6684315
[Available on 2020-01-15]
DOI:
10.1038/s41592-019-0466-z
[Indexed for MEDLINE]

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