WAD is a linear programming method for L1- minimal reconstruction loss in wavelet-ATAC data deconvolution.
Input
- single-cell ATAC reference with defined cell types
- bulk tissue samples for deconvolution
Output
- deconvolution_results.tsv
Example Execution
WAD \
--scATAC sample1_celltypeA.bw --cell_type celltypeA \
--scATAC sample1_celltypeB.bw --cell_type celltypeB \
--scATAC sample1_celltypeC.bw --cell_type celltypeC \
--scATAC sample2_celltypeA.bw --cell_type celltypeA \
--scATAC sample2_celltypeB.bw --cell_type celltypeB \
--scATAC sample2_celltypeC.bw --cell_type celltypeC \
--bulk_tissue bulk_sample1.bw \
--bulk_tissue bulk_sample2.bw \
--chrom_sizes hg38.chrom.sizes \
--output_dir deconvolution_results
Example Output
BulkSample CellTypeA CellTypeB CellTypeC
bulk_sample1 0.4 0.5 0.1
bulk_sample2 0.35 0.5 0.15
Full API documentation and a detailed usage guide are available here: WAD API Docs
PyPI/Pip
WAD can be installed from PyPI using pip:
pip install WAD
We recommend installing using a conda environment:
conda create -n WAD_env
conda activate WAD_env
conda install pip
pip install WAD
The list of package version requirements is available in setup.py.
- python == 3.12.7
- numpy >2.0, <2.3
- pandas == 2.3.1
- pyBigWig == 0.3.24
- PyWavelets == 1.7.0
- click == 8.1.7
- scipy == 1.13.1
- cvxpy == 1.6.6
- ortools >=9.10, <9.12
- pyarrow == 20.0.0
WAD is released under an MIT license.