/cell2cell

User-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

Inferring cell-cell interactions from transcriptomes with cell2cell

PyPI Version Documentation Status Downloads

📖 Getting started

For tutorials and documentation, visit cell2cell ReadTheDocs or our cell2cell website.

🔧 Installation

Step 1: Install Anaconda 🐍

First, install Anaconda following this tutorial

Step 2: Create and Activate a New Conda Environment 💻
# Create a new conda environment
conda create -n cell2cell -y python=3.7 jupyter

# Activate the environment
conda activate cell2cell
Step 3: Install cell2cell ⬇️
pip install cell2cell

💡 Examples

cell2cell Examples Tensor-cell2cell Examples
cell2cell Logo Tensor-cell2cell Logo
- Step-by-step Pipeline
- Interaction Pipeline for Bulk Data
- Interaction Pipeline for Single-Cell Data
- Whole Body of C. elegans
- Obtaining patterns of cell-cell communication
- Downstream 1: Factor-specific analyses
- Downstream 2: Patterns to functions (GSEA)
- Tensor-cell2cell in Google Colab (GPU)
- Communication patterns in Spatial Transcriptomics

Reproducible runs of the analyses in the Tensor-cell2cell paper are available at CodeOcean.com

🔗 LIANA & Tensor-cell2cell

Explore our tutorials for using Tensor-cell2cell with LIANA at ccc-protocols.readthedocs.io.

❓ Common Issues

  • Memory Errors with Tensor-cell2cell: If you encounter memory errors when performing tensor factorizations, try replacing init='svd' with init='random'.

🧬 Ligand-Receptor Pairs

Find a curated list of ligand-receptor pairs for your analyses at our GitHub Repository.

📑 Citation

Please cite our work using the following references: