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

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.

SUBMITTER: Wolf FA

PROVIDER: S-EPMC6425583 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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

Wolf F Alexander FA Hamey Fiona K FK Plass Mireya M Solana Jordi J Dahlin Joakim S JS Göttgens Berthold B Rajewsky Nikolaus N Simon Lukas L Theis Fabian J FJ

Genome biology 20190319 1

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 ...[more]

PMID: 30890159

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