Inferring cell-cell interactions from transcriptomes with cell2cell

:book: Getting started

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

:wrench: Installation

# Create a new conda environment
conda create -n cell2cell -y python=3.7 jupyter

# Activate the environment
conda activate cell2cell

pip install cell2cell

:bulb: Examples

cell2cell Examples	Tensor-cell2cell Examples
- 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

:link: LIANA & Tensor-cell2cell

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

:question: Common Issues

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

:dna: Ligand-Receptor Pairs

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

:bookmark_tabs: Citation

Please cite our work using the following references:

• cell2cell: Inferring a spatial code of cell-cell interactions across a whole animal body. PLOS Computational Biology, 2022

• Tensor-cell2cell: Context-aware deconvolution of cell-cell communication with Tensor-cell2cell. Nature Communications, 2022.

• LIANA & Tensor-cell2cell tutorials: Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. Cell Reports Methods, 2024