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https://github.com/earmingol/cell2cell

User-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins
https://github.com/earmingol/cell2cell

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

Last synced: 4 months ago
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User-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins

Host: GitHub
URL: https://github.com/earmingol/cell2cell
Owner: earmingol
License: bsd-3-clause
Created: 2019-04-22T04:37:54.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2023-12-01T14:42:13.000Z (7 months ago)
Last Synced: 2024-02-03T17:33:01.918Z (4 months ago)
Topics: 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
Language: Python
Homepage:
Size: 97.8 MB
Stars: 45
Watchers: 3
Forks: 11
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE.txt

Lists

awesome-cell-cell-communication - cell2cell - [python]- Tensor-cell2cell is an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. (Uncategorized / Uncategorized)

README

        # Inferring cell-cell interactions from transcriptomes with *cell2cell*

[![PyPI Version][pb]][pypi]

[![Documentation Status](https://readthedocs.org/projects/cell2cell/badge/?version=latest)](https://cell2cell.readthedocs.io/en/latest/?badge=latest)

[![Downloads](https://pepy.tech/badge/cell2cell/month)](https://pepy.tech/project/cell2cell)

[pb]: https://badge.fury.io/py/cell2cell.svg

[pypi]: https://pypi.org/project/cell2cell/

## :book: Getting started

For tutorials and documentation, visit [**cell2cell ReadTheDocs**](https://cell2cell.readthedocs.org/) or our [**cell2cell website**](https://earmingol.github.io/cell2cell).

## :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](https://github.com/earmingol/cell2cell/blob/master/Logo.png?raw=true) | ![Tensor-cell2cell Logo](https://github.com/earmingol/cell2cell/blob/master/LogoTensor.png?raw=true) |

| - [Step-by-step Pipeline](https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example.ipynb)
- [Interaction Pipeline for Bulk Data](https://earmingol.github.io/cell2cell/tutorials/Toy-Example-BulkPipeline)
- [Interaction Pipeline for Single-Cell Data](https://earmingol.github.io/cell2cell/tutorials/Toy-Example-SingleCellPipeline)
- [Whole Body of *C. elegans*](https://github.com/LewisLabUCSD/Celegans-cell2cell) | - [Obtaining patterns of cell-cell communication](https://earmingol.github.io/cell2cell/tutorials/ASD/01-Tensor-Factorization-ASD/)
- [Downstream 1: Factor-specific analyses](https://earmingol.github.io/cell2cell/tutorials/ASD/02-Factor-Specific-ASD/)
- [Downstream 2: Patterns to functions (GSEA)](https://earmingol.github.io/cell2cell/tutorials/ASD/03-GSEA-ASD/)
- [Tensor-cell2cell in Google Colab (**GPU**)](https://colab.research.google.com/drive/1T6MUoxafTHYhjvenDbEtQoveIlHT2U6_?usp=sharing)
- [Communication patterns in **Spatial Transcriptomics**](https://earmingol.github.io/cell2cell/tutorials/Tensor-cell2cell-Spatial/) |

Reproducible runs of the analyses in the [Tensor-cell2cell paper](https://doi.org/10.1038/s41467-022-31369-2) are available at [CodeOcean.com](https://doi.org/10.24433/CO.0051950.v2)

## :link: LIANA & Tensor-cell2cell

Explore our tutorials for using Tensor-cell2cell with [LIANA](https://github.com/saezlab/liana-py) at [ccc-protocols.readthedocs.io](https://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](https://github.com/LewisLabUCSD/Ligand-Receptor-Pairs).

## :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](https://doi.org/10.1371/journal.pcbi.1010715).

  *PLOS Computational Biology, 2022*

- **Tensor-cell2cell**: [Context-aware deconvolution of cell-cell communication with Tensor-cell2cell](https://doi.org/10.1038/s41467-022-31369-2).

  *Nature Communications, 2022.*

- **LIANA & Tensor-cell2cell tutorials**: [Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples](https://doi.org/10.1101/2023.04.28.538731).

  *bioRxiv, 2023*