earmingol / cell2cell

User-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins
BSD 3-Clause "New" or "Revised" License
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bioinformatics bulk-rna-seq cell-cell-communication cell-cell-interaction computational-biology machine-learning rna-seq rnaseq sc-rna-seq single-cell single-cell-rna-seq

Inferring cell-cell interactions from transcriptomes with cell2cell

PyPI Version Documentation Status Downloads

:book: Getting started

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

:wrench: Installation

Step 1: Install Anaconda :snake: First, [install Anaconda following this tutorial](https://docs.anaconda.com/anaconda/install/)
Step 2: Create and Activate a New Conda Environment :computer: ``` # Create a new conda environment conda create -n cell2cell -y python=3.7 jupyter # Activate the environment conda activate cell2cell ```
Step 3: Install cell2cell :arrow_down: ``` pip install cell2cell ```

:bulb: 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

:link: LIANA & Tensor-cell2cell

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

:question: Common Issues

: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: