CellWalkR

CellWalkR integrates single-cell open chromatin sequencing (scATAC-seq) data with bulk epigenetic datasets within an R package framework using a network-based random walk model to mitigate scATAC-seq sparsity and identify cell type–specific regulatory regions.

Key Features:

• Integration of Data Types: Combines scATAC-seq with bulk epigenetic datasets and external labeling to improve detection of regulatory signals despite scATAC-seq sparsity.
• Network-Based Random Walk Model: Employs a network representation of regulatory elements and a random walk algorithm to propagate labels and prioritize regulatory regions.
• Outputs: Produces cell type labels for individual cells and identified cell type–specific regulatory regions.

Scientific Applications:

• Cell-type regulatory mapping: Resolves cell type–specific regulatory elements from sparse single-cell chromatin accessibility data.
• Developmental biology: Enables analysis of regulatory dynamics across differentiating cell populations.
• Disease modeling and personalized medicine: Supports identification of cell type–specific regulatory mechanisms relevant to disease and individualized studies.

Methodology:

Combine scATAC-seq data with bulk epigenetic datasets, construct a network representing relationships among regulatory elements, and apply a network-based random walk algorithm to identify regulatory regions and assign cell type labels.