Installation Guide

Installation Guide#

Note

This guide covers two installation methods:

  • Installing the Published Package (recommended for most users): Use this if you want to use scXpand for analysis or inference.

  • Local Development Setup (for contributors/developers): Use this if you want to contribute to scXpand or work with the latest source code from GitHub.

Installing the Published Package#

scXpand is available in two variants to match your hardware:

If you have an NVIDIA GPU with CUDA support:

With plain pip (add CUDA index):

pip install --upgrade scxpand-cuda --extra-index-url https://download.pytorch.org/whl/cu128

With uv:

uv pip install --upgrade scxpand-cuda --extra-index-url https://download.pytorch.org/whl/cu128 --index-strategy unsafe-best-match

Otherwise (CPU, Apple Silicon, or non-CUDA GPUs):

With plain pip:

pip install --upgrade scxpand

With uv:

uv pip install --upgrade scxpand

Development Setup (from Source)#

To work with the latest version on GitHub (for development or contributions):

git clone https://github.com/yizhak-lab-ccg/scXpand.git
cd scXpand

scXpand uses uv for fast, reliable dependency management. Use the provided install scripts:

macOS/Linux:

./install.sh

Windows Command Prompt:

.\install.bat

These scripts will:

  • Install Python 3.13 via uv

  • Create a virtual environment in .venv/

  • Install all dependencies from the lock file

  • Set up PyTorch with appropriate GPU support

  • Register Jupyter kernel

  • Set up pre-commit hooks

Then activate the environment:

# macOS/Linux:
source .venv/bin/activate

# Window Command Prompt:
.\.venv\Scripts\activate
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