celldisect 0.1.0

Cell DISentangled Experts for Covariate counTerfactuals (CellDISECT). Causal generative model designed to disentangle known covariate variations from unknown ones at test time while simultaneously learning to make counterfactual predictions.

Cell DISentangled Experts for Covariate counTerfactuals (CellDISECT)

Causal generative model designed to disentangle known covariate variations from unknown ones at test time while simultaneously learning to make counterfactual predictions.

Installation

Prerequisites

Conda Environment

We recommend using Anaconda/Miniconda to create a conda environment for using CellDISECT. You can create a python environment using the following command:

conda create -n CellDISECT python=3.9

Then, you can activate the environment using:

conda activate CellDISECT

• Install pytorch (This version of CellDISECT is tested with pytorch 2.1.2 and cuda 12, install the appropriate version of pytorch for your system.)

conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia

• (Optional) if you plan to use RAPIDS/rapids-singlecell:

pip install \
    --extra-index-url=https://pypi.nvidia.com \
    cudf-cu12==24.4.* dask-cudf-cu12==24.4.* cuml-cu12==24.4.* \
    cugraph-cu12==24.4.* cuspatial-cu12==24.4.* cuproj-cu12==24.4.* \
    cuxfilter-cu12==24.4.* cucim-cu12==24.4.* pylibraft-cu12==24.4.* \
    raft-dask-cu12==24.4.* cuvs-cu12==24.4.*

pip install rapids-singlecell

• Install the latest version of CellDISECT

pip install git+https://github.com/Lotfollahi-lab/CellDISECT

• (Optional) to install cuda enabled jax:

pip install -U "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html