DOTools-py 0.0.3

Convenient functions to streamline sc/snRNAseq data analysis workflow

DOTools_py

Convenient and user-friendly package to streamline common workflows in single-cell RNA sequencing data analysis using the scverse ecosystem. It provides high-level wrappers and visualization functions to help efficiently preprocess, analyze, and interpret single-cell data.

Getting started

Please refer to the documentation, in particular, the API documentation.

Installation

You need to have Python 3.10 or newer installed on your system. We recommend creating a dedicated conda environment.

conda create -n scrna_py11 python=3.11 -y
conda activate scrna_py11

There are several alternative options to install DOTools_py:

1. Install the latest release of DOTools_py from PyPI:

pip install uv
uv pip install dotools-py

2. Install the latest development version:

pip install git+https://github.com/davidrm-bio/DOTools_py.git@main

Finally, to use this environment in jupyter notebook, add jupyter kernel for this environment:

python -m ipykernel install --user --name=scrna_py11 --display-name=scrna_py11

Requirements

This package has been tested on macOS, Linux and Windows System. For a standard dataset (e.g., 6 samples with 10k cells each) we suggest 16GB of RAM and at least 5 CPUs.

Some methods are run through R and require additional dependencies including: Seurat, MAST, scDblFinder, zellkonverter, data.table and optparse.

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

install.packages("optparse", Ncpus=8)
install.packages('remotes', Ncpus=8)
install.packages('data.table', Ncpus = 8)
remotes::install_github("satijalab/seurat", "seurat5", quiet = TRUE)  # Seurat
BiocManager::install("MAST")
BiocManager::install("scDblFinder")
BiocManager::install("zellkonverter")
BiocManager::install('glmGamPoi')

For old CPU architectures there can be problems with polars making the kernel die when importing the package. In this case run

pip install --no-cache polars-lts-cpu

R version

We also have an R implementation of the DOTools. This can be installed from Bioconductor:

if (!requireNamespace("BiocManager", quietly=TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("DOtools")
devtools::install_github("MarianoRuzJurado/DOtools")

The developmental version can be downloaded using devtools:

devtools::install_github("MarianoRuzJurado/DOtools", ref="devel")

Release notes

See the changelog.

Contact

Raising up an issue in this GitHub repository might be the fastest way of submitting suggestions and bugs. Alternatively you can write to my email: [e-mail].

Citation

t.b.a