Modeling fragment counts improves single-cell ATAC-seq analysis
Single-cell ATAC-sequencing (scATAC-seq) coverage in regulatory regions is typically binarized as an indicator of open chromatin. However, the implications of scATAC-seq data binarization have not systematically been assessed. Here, we show that the goodness-of-fit of existing models and their applications, including clustering, cell type identification, and batch integration, are improved by a quantitative treatment of the fragment counts. These results have immediate implications for scATAC-seq analysis..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 23. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Martens, Laura D. [VerfasserIn] |
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| doi: |
10.1101/2022.05.04.490536 |
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| PPN (Katalog-ID): |
XBI035923032 |
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| 520 | |a Single-cell ATAC-sequencing (scATAC-seq) coverage in regulatory regions is typically binarized as an indicator of open chromatin. However, the implications of scATAC-seq data binarization have not systematically been assessed. Here, we show that the goodness-of-fit of existing models and their applications, including clustering, cell type identification, and batch integration, are improved by a quantitative treatment of the fragment counts. These results have immediate implications for scATAC-seq analysis. | ||
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