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

Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS, Gottgens B, Rajewsky N, Simon L, Theis FJ (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Genome Biology

Book Volume: 20

Pages Range: 1-9

Article Number: 59

Journal Issue: 1

DOI: 10.1186/s13059-019-1663-x

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) University of Cambridge GB United Kingdom (GB) Max-Delbrück-Centrum für Molekulare Medizin / Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch DE Germany (DE)

How to cite

APA:

Wolf, F.A., Hamey, F.K., Plass, M., Solana, J., Dahlin, J.S., Gottgens, B.,... Theis, F.J. (2019). PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biology, 20(1), 1-9. https://doi.org/10.1186/s13059-019-1663-x

MLA:

Wolf, F. Alexander, et al. "PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells." Genome Biology 20.1 (2019): 1-9.

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