CellWalkR

About

CellWalkR is an R package that integrates single-cell open chromatin (scATAC-seq) data with cell type labels and bulk epigenetic data to identify cell type-specific regulatory regions. A GPU implementation and downsampling strategies enable thousands of cells to be processed in seconds. CellWalkR’s user-friendly interface provides interactive analysis and visualization of cell labels and regulatory region mappings.

Installation

Install CellWalkR for R using devtools as follows:

$ R
> install.packages("devtools")
> devtools::install_github("PFPrzytycki/CellWalkR")

Usage

For a guide to using CellWalkR, see the provided vignette, which covers the following:

1. Data Pre-processing
2. Getting Started with CellWalkR
   1. Loading scATAC-seq Data
   2. Defining Label Nodes
3. Building a Network
   1. Computing Cell-Cell Edges
   2. Computing Label-Cell Edges
4. Tuning Label Edges
5. Making a cellWalk Object
6. Adding Filters
7. Downstream Analysis
   1. Cell Labels
   2. Confusion Matrix
   3. Hierarchical Clustering of Labels
   4. Plotting Cells
   5. Bulk Data Mapping
8. Interactive Visualizaiton
9. Adding a Second Set of Labels
10. Detecting Doublets

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

AWS + TensorFlow

CellWalkR can also be run on AWS which vastly simplifies the process of running on GPUs using TensorFlow. Using GPUs allows the code to run more than 15 times faster. For a guide to running CellWalkR on AWS using GPUs see this vignette.