PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.
Theis, Fabian J
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Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Gottgens, B., Rajewsky, N., et al. (2019). PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.. Genome biology, 20 (1), 59. https://doi.org/10.1186/s13059-019-1663-x
Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graphlike 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 di erent resolutions and result in much higher computational e ciency 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.
Hematopoietic Stem Cells, Embryo, Nonmammalian, Animals, Zebrafish, Humans, Planarians, Sequence Analysis, RNA, Computational Biology, Gene Expression Regulation, Developmental, Algorithms, Reference Standards, Computer Graphics, Software, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
Wellcome Trust, MRC, CRUK, Bloodwise, Swedish Research Council, Helmholtz Association, German Center for Cardiovascular Research, German Research Foundation
Leukaemia & Lymphoma Research (12029)
National Institutes of Health (NIH) (via Pennsylvania State University) (R24DK106766)
Wellcome Trust (206328/Z/17/Z)
Wellcome Trust (203151/Z/16/Z)
Wellcome Trust (079895/Z/06/Z)
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External DOI: https://doi.org/10.1186/s13059-019-1663-x
This record's URL: https://www.repository.cam.ac.uk/handle/1810/289622