Installation

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

  1. 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")