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Inferring cell-cell interactions from transcriptomes with cell2cell

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: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

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
- Identifying a spatial code of ligand-receptor pairs (Genetic Algorithm)
    - Reproducing the C. elegans spatial code
    - Customizing the objective function
- 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
- Multi-modal communication patterns with Coupled Tensor Component Analysis

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:

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