Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS
Abstract Single cell ATAC-seq (scATAC-seq) experimental designs have become increasingly complex, with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing m...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55580-5 |
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| author | Zhen Miao Jianqiao Wang Kernyu Park Da Kuang Junhyong Kim |
| author_facet | Zhen Miao Jianqiao Wang Kernyu Park Da Kuang Junhyong Kim |
| author_sort | Zhen Miao |
| collection | DOAJ |
| description | Abstract Single cell ATAC-seq (scATAC-seq) experimental designs have become increasingly complex, with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current scATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that allows complex hypothesis testing of accessibility-modulating factors while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves a 17% to 122% higher power on average than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks, including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to datasets from various tissues and show its ability to reveal previously undiscovered insights in scATAC-seq data. |
| format | Article |
| id | doaj-art-0726faf4c5d14a7c831724a3d0e83a18 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-0726faf4c5d14a7c831724a3d0e83a182025-01-05T12:40:28ZengNature PortfolioNature Communications2041-17232025-01-0116111510.1038/s41467-024-55580-5Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACSZhen Miao0Jianqiao Wang1Kernyu Park2Da Kuang3Junhyong Kim4Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of PennsylvaniaDepartment of Biostatistics, Harvard T.H. Chan School of HealthDepartment of Biology, University of PennsylvaniaDeptartment Computer and Information Science, University of PennsylvaniaGraduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of PennsylvaniaAbstract Single cell ATAC-seq (scATAC-seq) experimental designs have become increasingly complex, with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current scATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that allows complex hypothesis testing of accessibility-modulating factors while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves a 17% to 122% higher power on average than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks, including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to datasets from various tissues and show its ability to reveal previously undiscovered insights in scATAC-seq data.https://doi.org/10.1038/s41467-024-55580-5 |
| spellingShingle | Zhen Miao Jianqiao Wang Kernyu Park Da Kuang Junhyong Kim Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS Nature Communications |
| title | Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS |
| title_full | Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS |
| title_fullStr | Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS |
| title_full_unstemmed | Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS |
| title_short | Depth-corrected multi-factor dissection of chromatin accessibility for scATAC-seq data with PACS |
| title_sort | depth corrected multi factor dissection of chromatin accessibility for scatac seq data with pacs |
| url | https://doi.org/10.1038/s41467-024-55580-5 |
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