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

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Item Type:Article
Title:PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
Creators Name:Wolf, F.A. and Hamey, F.K. and Plass, M. and Solana, J. and Dahlin, J.S. and Göttgens, B. and Rajewsky, N. and Simon, L. and Theis, F.J.
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.
Keywords:Algorithms, Computational Biology, Computer Graphics, Developmental Gene Expression Regulation, Hematopoietic Stem Cells, High-Throughput Nucleotide Sequencing, Nonmammalian Embryo, Planarians, Reference Standards, RNA Sequence Analysis, Single-Cell Analysis, Software, Animals, Zebrafish
Source:Genome Biology
ISSN:1474-760X
Publisher:BioMed Central (U.K.)
Volume:20
Number:1
Page Range:59
Date:19 March 2019
Official Publication:https://doi.org/10.1186/s13059-019-1663-x
PubMed:View item in PubMed
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https://edoc.mdc-berlin.de/16900/Preprint version

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