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Genome Biol. 2019 Mar 19;20(1):59. doi: 10.1186/s13059-019-1663-x.

PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.

Author information

1
Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
2
Department of Haematology and Wellcome and Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
3
Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany.
4
Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
5
Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany. fabian.theis@helmholtz-muenchen.de.
6
Department of Mathematics, Technische Universität München, Munich, Germany. fabian.theis@helmholtz-muenchen.de.

Abstract

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

PMID:
30890159
PMCID:
PMC6425583
DOI:
10.1186/s13059-019-1663-x
[Indexed for MEDLINE]
Free PMC Article

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