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

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://gi...

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Main Authors: Wolf, FA, Hamey, FK, Plass, M, Solana, J, Dahlin, JS, Göttgens, B, Rajewsky, N, Simon, L, Theis, FJ
Format: Journal article
Jezik:English
Izdano: BioMed Central 2019
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author Wolf, FA
Hamey, FK
Plass, M
Solana, J
Dahlin, JS
Göttgens, B
Rajewsky, N
Simon, L
Theis, FJ
author_facet Wolf, FA
Hamey, FK
Plass, M
Solana, J
Dahlin, JS
Göttgens, B
Rajewsky, N
Simon, L
Theis, FJ
author_sort Wolf, FA
collection OXFORD
description 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.
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spelling oxford-uuid:a235105a-47db-4cf5-8b93-aacbca85ddb62022-03-27T02:18:39ZPAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cellsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a235105a-47db-4cf5-8b93-aacbca85ddb6EnglishSymplectic ElementsBioMed Central2019Wolf, FAHamey, FKPlass, MSolana, J Dahlin, JSGöttgens, BRajewsky, NSimon, LTheis, FJSingle-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.
spellingShingle Wolf, FA
Hamey, FK
Plass, M
Solana, J
Dahlin, JS
Göttgens, B
Rajewsky, N
Simon, L
Theis, FJ
PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
title PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
title_full PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
title_fullStr PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
title_full_unstemmed PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
title_short PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
title_sort paga graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
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