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://g...

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Vydáno v:	Genome Biology Ročník 20; číslo 1; s. 59
Hlavní autoři:	Wolf, F. Alexander, Hamey, Fiona K., Plass, Mireya, Solana, Jordi, Dahlin, Joakim S., Göttgens, Berthold, Rajewsky, Nikolaus, Simon, Lukas, Theis, Fabian J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 19.03.2019 Springer Nature B.V BMC
Témata:	adults Algorithms Animal Genetics and Genomics Animals Bioinformatics Biomedical and Life Sciences Cell cycle Clustering Computational Biology - methods Computer Graphics Connectivity Danio rerio Data analysis data collection Data processing Datasets Embryo, Nonmammalian - cytology Embryo, Nonmammalian - metabolism Evolutionary Biology Gene expression Gene Expression Regulation, Developmental genome Graphs Hematopoietic Stem Cells - cytology Hematopoietic Stem Cells - metabolism High-Throughput Nucleotide Sequencing - methods Human Genetics Humans Life Sciences Method Microbial Genetics and Genomics Neighborhoods Neural networks Planarians - cytology Planarians - genetics Plant Genetics and Genomics Reference Standards Ribonucleic acid RNA sequence analysis Sequence Analysis, RNA - methods Single-Cell Analysis - methods Software topology Zebrafish - growth & development Zebrafish - metabolism
ISSN:	1474-760X, 1474-7596, 1474-760X
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1474-760X 1474-7596 1474-760X
DOI:	10.1186/s13059-019-1663-x