AnanseScanpy package: implementation of scANANSE for Scanpy objects in Python

Installation

The most straightforward way to install the most recent version of AnanseScanpy is via conda using PyPI.

Install package through Conda

If you have not used Bioconda before, first set up the necessary channels (in this order!). You only have to do this once.

$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forge

Then install AnanseScanpy with:

$ conda install anansescanpy

Install package through PyPI

$ pip install anansescanpy

Install package through GitHub

$ git clone https://github.com/Arts-of-coding/AnanseScanpy.git
$ cd AnanseScanpy
$ conda env create -f requirements.yaml
$ conda activate AnanseScanpy
$ pip install -e .

Install Jupyter Notebook

$ pip install jupyter

Start using the package

Run the package either in the console

$ python3

Or run the package in jupyter notebook

$ jupyter notebook

For extended documentation see our ipynb vignette with PBMC sample data

Of which the sample data can be downloaded

$ wget https://zenodo.org/records/7575107/files/rna_PBMC.h5ad?download=1 -O scANANSE/rna_PBMC.h5ad
$ wget https://zenodo.org/records/7575107/files/atac_PBMC.h5ad?download=1 -O scANANSE/atac_PBMC.h5ad

installing and running anansnake

Follow the instructions its respective github page, https://github.com/vanheeringen-lab/anansnake Next automatically use the generated files to run GRN analysis using your single cell cluster data:

snakemake --use-conda --conda-frontend mamba \
--configfile scANANSE/analysis/config.yaml \
--snakefile scANANSE/anansnake/Snakefile \
--resources mem_mb=48_000 --cores 12

Thanks to:

• Jos Smits and his Seurat equivalent of this package https://github.com/JGASmits/AnanseSeurat
• Siebren Frohlich and his anansnake implementation https://github.com/vanheeringen-lab/anansnake

How to cite this software:

Smits JGA, Arts JA, Frölich S et al. scANANSE gene regulatory network and motif analysis of single-cell clusters [version 1; peer review: awaiting peer review]. F1000Research 2023, 12:243 (https://doi.org/10.12688/f1000research.130530.1)