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://gi...
| Hlavní autoři: | , , , , , , , , |
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| Médium: | Journal article |
| Jazyk: | English |
| Vydáno: |
BioMed Central
2019
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| _version_ | 1826288580551507968 |
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| author | Wolf, FA Hamey, FK Plass, M Solana, J Dahlin, JS Göttgens, B Rajewsky, N Simon, L Theis, FJ |
| author_facet | Wolf, FA Hamey, FK Plass, M Solana, J Dahlin, JS Göttgens, B Rajewsky, N Simon, L Theis, FJ |
| author_sort | Wolf, FA |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T02:15:51Z |
| format | Journal article |
| id | oxford-uuid:a235105a-47db-4cf5-8b93-aacbca85ddb6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:15:51Z |
| publishDate | 2019 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | oxford-uuid:a235105a-47db-4cf5-8b93-aacbca85ddb62022-03-27T02:18:39ZPAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cellsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a235105a-47db-4cf5-8b93-aacbca85ddb6EnglishSymplectic ElementsBioMed Central2019Wolf, FAHamey, FKPlass, MSolana, J Dahlin, JSGöttgens, BRajewsky, NSimon, LTheis, FJSingle-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. |
| spellingShingle | Wolf, FA Hamey, FK Plass, M Solana, J Dahlin, JS Göttgens, B Rajewsky, N Simon, L Theis, FJ PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells |
| title | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells |
| title_full | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells |
| title_fullStr | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells |
| title_full_unstemmed | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells |
| title_short | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells |
| title_sort | paga graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells |
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