Installation

The SMART package is developed based on the PyTorch framework and supports execution on both CPU and GPU.

Using a GPU is strongly recommended, as it can significantly accelerate the integration process.

To enable GPU acceleration, please ensure that PyTorch and CUDA (including cuDNN) are properly installed, along with the appropriate GPU drivers for your system.

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Software dependencies

To run the SMART package, make sure all dependencies listed in requirements.txt are installed:

muon==0.1.6
scanpy==1.10.2
scikit-learn==1.5.1
anndata==0.10.8
matplotlib==3.9.2
tqdm==4.66.5
numba==0.60.0
rpy2==3.5.12
torch==2.4.1
torch_geometric==2.3.0
harmony-pytorch==0.1.8
scikit-misc==0.3.1

Environment setup

We recommend creating a dedicated conda environment for SMART:

# 1. create conda environment
conda create -n smart python=3.9.23 -y

# 2. activate conda environment
conda activate smart

# 3. Install R
conda install -c conda-forge r-base=4.3.0 -y

# 4. Install Python dependencies
pip install -r requirements.txt

In addition, you need to install the mclust R package:

# 1. Activate the conda environment
conda activate smart
# 2. Start the R console
R
# 3. Install the mclust package
install.packages("mclust", repos = "https://cloud.r-project.org")
# 4. Exit the R console
q()

Install SMART via pip

You can install SMART directly from PyPI:

pip install bio-SMART

After installation, verify it by importing SMART and checking its version:

import smart
print(smart.__version__)  # check installation

If the installation is successful, the version number of SMART will be printed.