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- Publisher Website: 10.1186/s13059-023-02980-3
- Scopus: eid_2-s2.0-85163368267
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Article: DCATS: differential composition analysis for flexible single-cell experimental designs
Title | DCATS: differential composition analysis for flexible single-cell experimental designs |
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Authors | |
Issue Date | 26-Jun-2023 |
Publisher | BioMed Central |
Citation | Genome Biology, 2023, v. 24, n. 1 How to Cite? |
Abstract | Differential composition analysis - the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions - is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods. |
Persistent Identifier | http://hdl.handle.net/10722/331960 |
ISSN | 2023 Impact Factor: 10.1 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lin, Xinyi | - |
dc.contributor.author | Chau, Chuen | - |
dc.contributor.author | Ma, Kun | - |
dc.contributor.author | Huang, Yuanhua | - |
dc.contributor.author | Ho, Joshua W K | - |
dc.date.accessioned | 2023-09-28T04:59:53Z | - |
dc.date.available | 2023-09-28T04:59:53Z | - |
dc.date.issued | 2023-06-26 | - |
dc.identifier.citation | Genome Biology, 2023, v. 24, n. 1 | - |
dc.identifier.issn | 1474-760X | - |
dc.identifier.uri | http://hdl.handle.net/10722/331960 | - |
dc.description.abstract | <p>Differential composition analysis - the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions - is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods.<br></p> | - |
dc.language | eng | - |
dc.publisher | BioMed Central | - |
dc.relation.ispartof | Genome Biology | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | DCATS: differential composition analysis for flexible single-cell experimental designs | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1186/s13059-023-02980-3 | - |
dc.identifier.scopus | eid_2-s2.0-85163368267 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 1 | - |
dc.identifier.eissn | 1465-6906 | - |
dc.identifier.isi | WOS:001020591900002 | - |
dc.identifier.issnl | 1474-7596 | - |