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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

F. Alexander Wolf, Fiona K. Hamey, Mireya Plass, Jordi Solana, Joakim S. Dahlin, Berthold Göttgens, Nikolaus Rajewsky, Lukas Simon, Fabian J. Theis

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. More... »

PAGES

59

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13059-019-1663-x

DOI

http://dx.doi.org/10.1186/s13059-019-1663-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112873269

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30890159


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