scATAnno: Automated Cell Type Annotation for single-cell ATAC Sequencing Data
Abstract Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 27. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jiang, Yijia [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.06.01.543296 |
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| PPN (Katalog-ID): |
XBI039800903 |
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| 520 | |a Abstract Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems. | ||
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| 700 | 1 | |a Lynch, Allen W. |4 aut | |
| 700 | 1 | |a Jiang, Junchen |4 aut | |
| 700 | 1 | |a Zhu, Alexander |4 aut | |
| 700 | 1 | |a Zeng, Ziqi |4 aut | |
| 700 | 1 | |a Zhang, Yi |0 (orcid)0000-0002-7453-6188 |4 aut | |
| 700 | 1 | |a Wu, Gongwei |4 aut | |
| 700 | 1 | |a Xie, Yingtian |4 aut | |
| 700 | 1 | |a Li, Rong |4 aut | |
| 700 | 1 | |a Zhou, Ningxuan |4 aut | |
| 700 | 1 | |a Meyer, Cliff |4 aut | |
| 700 | 1 | |a Cejas, Paloma |4 aut | |
| 700 | 1 | |a Brown, Myles |4 aut | |
| 700 | 1 | |a Long, Henry W. |0 (orcid)0000-0001-6849-6629 |4 aut | |
| 700 | 1 | |a Qiu, Xintao |0 (orcid)0000-0002-8560-7017 |4 aut | |
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