CellWalker2

About

CellWalker2 is integrated into CellWalkerR package. CellWalker2 integrates different modalities of single-cell data, scRNA-Seq, scATAC-Seq or Multiomic. CellWalker2 annotates cells and compares cell type labels by scRNA-Seq, and identifities cell type-specific regulatory regions or other bulk-derived annotations by multiomic data. CellWalker2 can assign cells or bulk genomic annotations (e.g. TF motif or regulatory regions) to cell type hierarchy and compare different cell type hierarchies from different datasets. It also provides statistical signifiance of the association by permutation and visualization of cell labels and regulatory region mappings on a cell type hierarchy.

Getting Started with CellWalker2 (start)

Usage

Use CellWalker2 for scRNA-Seq Data (human PBMC)

1. load scRNA-Seq data
2. Labeling cells

   

3. Comparing cell types or cell type trees

   

Use CellWalker2 for Multiomic Data (human developing cortex)

1. Load data

2. Process RNASeq data

3. Compute cell-to-cell type edges

4. Construct cell-to-cell graph

5. Labeling region-specific pREs by cell types

   

6. Mapping transcription factors to cell types using motifs

   

7. Mapping transcription factors to cell types using ChIP-Seq peaks

   

Citations

If you use CellWalkR please cite:

1. Przytycki, P.F., Pollard, K.S. “CellWalkR: An R Package for integrating and visualizing single-cell and bulk data to resolve regulatory elements.” Bioinformatics (2022). https://doi.org/10.1093/bioinformatics/btac150

2. Przytycki, P.F., Pollard, K.S. “CellWalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues.” Genome Biology (2021). https://doi.org/10.1186/s13059-021-02279-1

3. Hu, Z., Przytycki, P.F., Pollard, K.S. "CellWalker2: Multi-omic discovery using hierarchical cell type relationships." Cell Genomics (2025). https://www.cell.com/cell-genomics/fulltext/S2666-979X(25)00142-9