ChrAccR
ChrAccR is an R package for the comprehensive analysis of chromatin accessibility data from bulk and single-cell experiments. It supports quality control, exploratory analysis, dimension reduction, clustering, transcription factor activity estimation, and the identification and characterization of differentially accessible regions. ChrAccR is designed to scale from bulk datasets with hundreds of samples to single-cell datasets containing tens or hundreds of thousands of cells.
With only a limited number of R commands, ChrAccR generates interactive analysis reports that provide a structured overview of the dataset and can easily be shared with collaborators. In addition, the package includes utility functions for customized and more advanced R-based analyses.
Key benefits
Comprehensive workflow for bulk and single-cell chromatin accessibility analysis
Supports quality control, exploratory analysis, clustering, and dimension reduction
Includes transcription factor activity estimation
Identifies and characterizes differentially accessible regions
Scales to large datasets with hundreds of samples or many thousands of cells
Generates interactive and shareable analysis reports
Requires only a limited number of R commands for standard analyses
Provides utility functions for advanced custom scripting
Applications
Analysis of bulk ATAC-seq datasets
Analysis of single-cell chromatin accessibility data
Quality assessment of chromatin accessibility experiments
Unsupervised dimension reduction and clustering
Estimation of transcription factor activities
Identification of differentially accessible regions
Generation of interactive reports for collaborative projects
Development of customized chromatin accessibility analysis workflows in R
Intended use
ChrAccR is intended for researchers working with chromatin accessibility data, bioinformaticians, and institutional core facilities providing ATAC-seq services. It is particularly suited for users with limited bioinformatics experience, researchers new to chromatin accessibility analysis, and advanced users who want to extend standard analyses with custom R scripts.
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