Best practices for differential accessibility analysis in single-cell epigenomics

Alan Yue Yang Teo; Jordan W. Squair; Gregoire Courtine; Michael A. Skinnider

doi:10.1038/s41467-024-53089-5

Best practices for differential accessibility analysis in single-cell epigenomics

Alan Yue Yang Teo
, Jordan W. Squair
, Gregoire Courtine
, Michael A. Skinnider

Lewis-Sigler Institute for Integrative Genomics

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

The authors conduct a comprehensive assessment of statistical methods for identifying differentially accessible (DA) regions in scATAC-seq data, using a compendium of scATAC-seq experiments paired with bulk ATAC-seq or scRNA-seq from the same cells to evaluate accuracy, bias, robustness, and scalability.

Original language	English (US)
Article number	8805
Journal	Nature communications
Volume	15
Issue number	1
DOIs	https://doi.org/10.1038/s41467-024-53089-5
State	Published - Dec 2024

All Science Journal Classification (ASJC) codes

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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10.1038/s41467-024-53089-5

Cite this

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abstract = "The authors conduct a comprehensive assessment of statistical methods for identifying differentially accessible (DA) regions in scATAC-seq data, using a compendium of scATAC-seq experiments paired with bulk ATAC-seq or scRNA-seq from the same cells to evaluate accuracy, bias, robustness, and scalability.",

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AU - Courtine, Gregoire

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AB - The authors conduct a comprehensive assessment of statistical methods for identifying differentially accessible (DA) regions in scATAC-seq data, using a compendium of scATAC-seq experiments paired with bulk ATAC-seq or scRNA-seq from the same cells to evaluate accuracy, bias, robustness, and scalability.

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Best practices for differential accessibility analysis in single-cell epigenomics

Abstract

All Science Journal Classification (ASJC) codes

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