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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Bibliographic Details
Published in:Genome Biology Vol. 20; no. 1; p. 59
Main Authors: Wolf, F. Alexander, Hamey, Fiona K., Plass, Mireya, Solana, Jordi, Dahlin, Joakim S., Göttgens, Berthold, Rajewsky, Nikolaus, Simon, Lukas, Theis, Fabian J.
Format: Journal Article
Language:English
Published: London BioMed Central 19.03.2019
Springer Nature B.V
BMC
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1663-x