EpiCarousel is available on PyPI and can be installed via

pip install epicarousel

You can also install epicarousel from GitHub via

git clone git://github.com/BioX-NKU/EpiCarousel.git
cd EpiCarousel
python setup.py install

• data_name: Name of the dataset.
• data_dir: Path of an h5ad file where the scCAS data count matrix is stored in the compressed sparse row format. AnnData object of shape n_obs × n_vars. Rows correspond to cells and columns to peaks/regions.
• if_bi: Whether to binarize the scCAS data count matrix.
• if_mc_bi: Whether to binarize the metacell-by-region matrix.
• threshold: Threshold for binarizing metacell-by-region matrix.
• filter_rate: Proportion for feature selection.
• chunk_size: Number of cells in each chunk.
• carousel_resolution: Ratio of the number of cells to that of metacells.
• base: Export path for EpiCarousel.
• step: Length of Walktrap community detection.
• threads: Number of parallel processes.
• mc_mode: Mode of calculating metacell-by-region matrix.
• index: (Optional) Ground truth cell type label of single cells for downstream analysis and evaluation.

• adata: Metacell AnnData object of shape n_obs × n_vars stored in an h5ad file. Rows correspond to metacells and columns to features.

EpiCarousel can also be seamlessly integrated with EpiScanpy, a widely-used Python library for epigenomics single cell analysis:

import episcanpy as epi
import epicarousel

# Run EpiCarousel
carousel = epicarousel.core.Carousel(data_name,
                                     data_dir,
                                     if_bi,
                                     if_mc_bi,
                                     threshold,
                                     filter_rate,
                                     chunk_size,
                                     carousel_resolution,
                                     base,
                                     step,
                                     threads,
                                     mc_mode,
                                     index
                                    )
carousel.make_dirs()
carousel.data_split()
carousel.identify_metacells()
carousel.merge_metacells()
carousel.metacell_preprocess()
carousel.metacell_data_clustering()
carousel.result_comparison()
carousel.delete_dirs()

# Load the metacell data as an AnnData object (adata).
carousel.mc_adata