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<li class="toctree-l1"><a class="reference internal" href="/documentation/">API Documentation</a>
</li>
</ul>
<p class="caption"><span class="caption-text">cell2cell Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="/tutorials/Toy-Example-BulkPipeline/">Cell-cell communication from bulk dataset</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="/tutorials/Toy-Example-SingleCellPipeline/">Cell-cell communication from single-cell dataset</a>
</li>
</ul>
<p class="caption"><span class="caption-text">Tensor-cell2cell Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="/tutorials/ASD/01-Tensor-Factorization-ASD/">Obtaining patterns of cell-cell communication with Tensor-cell2cell</a>
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</li>
<li class="toctree-l1"><a class="reference internal" href="/tutorials/ASD/03-GSEA-ASD/">Downstream analysis 2: Gene Set Enrichment Analysis</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="/tutorials/Tensor-cell2cell-Spatial/">Inspecting CCC patterns from spatial transcriptomics</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="/tutorials/GPU-Example/">Running Tensor-cell2cell on your own GPU or on Google Colab's GPU</a>
</li>
</ul>
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Below, all the inputs, parameters (including their different options), and outputs are detailed. Source code of the functions is also included.


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</li>
<li class="toctree-l2"><a class="reference internal" href="#liana-tensor-cell2cell">LIANA &amp; Tensor-cell2cell</a>
</li>
<li class="toctree-l2"><a class="reference internal" href="#common-issues">Common issues</a>
<li class="toctree-l2"><a class="reference internal" href="#common-issues">Common Issues</a>
</li>
<li class="toctree-l2"><a class="reference internal" href="#ligand-receptor-pairs">Ligand-Receptor pairs</a>
<li class="toctree-l2"><a class="reference internal" href="#ligand-receptor-pairs">Ligand-Receptor Pairs</a>
</li>
<li class="toctree-l2"><a class="reference internal" href="#citation">Citation</a>
</li>
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<li class="toctree-l1"><a class="reference internal" href="documentation/">API Documentation</a>
</li>
</ul>
<p class="caption"><span class="caption-text">cell2cell Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="tutorials/Toy-Example-BulkPipeline/">Cell-cell communication from bulk dataset</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/Toy-Example-SingleCellPipeline/">Cell-cell communication from single-cell dataset</a>
</li>
</ul>
<p class="caption"><span class="caption-text">Tensor-cell2cell Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="tutorials/ASD/01-Tensor-Factorization-ASD/">Obtaining patterns of cell-cell communication with Tensor-cell2cell</a>
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</li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/ASD/03-GSEA-ASD/">Downstream analysis 2: Gene Set Enrichment Analysis</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/Tensor-cell2cell-Spatial/">Inspecting CCC patterns from spatial transcriptomics</a>
</li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/GPU-Example/">Running Tensor-cell2cell on your own GPU or on Google Colab's GPU</a>
</li>
</ul>
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<h1 id="inferring-cell-cell-interactions-from-transcriptomes-with-cell2cell">Inferring cell-cell interactions from transcriptomes with <em>cell2cell</em></h1>
<p><a href="https://pypi.org/project/cell2cell/"><img alt="PyPI Version" src="https://badge.fury.io/py/cell2cell.svg" /></a>
<a href="https://cell2cell.readthedocs.io/en/latest/?badge=latest"><img alt="Documentation Status" src="https://readthedocs.org/projects/cell2cell/badge/?version=latest" /></a>
<a href="https://pepy.tech/project/cell2cell"><img alt="Downloads" src="https://pepy.tech/badge/cell2cell/month" /></a></p>
<h2 id="getting-started">Getting started</h2>
<p>Please refer to the <a href="https://cell2cell.readthedocs.org">cell2cell website</a>,
which includes tutorials and documentation</p>
<p>For tutorials and documentation, visit <a href="https://cell2cell.readthedocs.org/"><strong>cell2cell ReadTheDocs</strong></a> or our <a href="https://earmingol.github.io/cell2cell"><strong>cell2cell website</strong></a>.</p>
<h2 id="installation">Installation</h2>
<p><strong>First, <a href="https://docs.anaconda.com/anaconda/install/">install Anaconda following this tutorial</a></strong></p>
<p>Once installed, create a new conda environment:
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>conda create -n cell2cell -y python=3.7 jupyter
</code></pre></div></p>
<p>Activate that environment:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>conda activate cell2cell
<p><b>Step 1: Install Anaconda</b></p>
<p>First, <a href="https://docs.anaconda.com/anaconda/install/">install Anaconda following this tutorial</a>.</p>
<p><b>Step 2: Create and Activate a New Conda Environment</b></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a># Create a new conda environment
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>conda create -n cell2cell -y python=3.7 jupyter
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a># Activate the environment
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>conda activate cell2cell
</code></pre></div>
<p><b>Step 3: Install cell2cell</b></p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>pip install cell2cell
</code></pre></div>
<p>Then, install cell2cell:
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>pip install cell2cell
</code></pre></div></p>
<h2 id="examples">Examples</h2>
<hr />
<p><img alt="plot" src="https://github.com/earmingol/cell2cell/blob/master/Logo.png?raw=true" /></p>
<ul>
<li>A toy example using the <strong>under-the-hood methods of cell2cell</strong> is
<a href="https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example.ipynb">available here</a>.
This case allows personalizing the analyses in a higher level, but it may result <strong>harder to use</strong>.</li>
<li>A toy example using an Interaction Pipeline for <strong>bulk data</strong> is
<a href="https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example-BulkPipeline.ipynb">available here</a>.
An Interaction Pipeline makes cell2cell <strong>easier to use</strong>.</li>
<li>A toy example using an Interaction Pipeline for <strong>single-cell data</strong> is
<a href="https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example-SingleCellPipeline.ipynb">available here</a>.
An Interaction Pipeline makes cell2cell <strong>easier to use</strong>. </li>
<li>An example of using <em>cell2cell</em> to infer cell-cell interactions across the <strong>whole
body of <em>C. elegans</em></strong> is <a href="https://github.com/LewisLabUCSD/Celegans-cell2cell">available here</a></li>
</ul>
<hr />
<p><img alt="plot" src="https://github.com/earmingol/cell2cell/blob/master/LogoTensor.png?raw=true" /></p>
<ul>
<li>Jupyter notebooks for reproducing the results in the manuscript of Tensor-cell2cell
<a href="https://doi.org/10.24433/CO.0051950.v2">are available and can be run online in codeocean.com</a>.
It specifically contains analyses on datasets of <strong>COVID-19, Autism Spectrum Disorders (ASD) and the embryonic development
of <em>C. elegans</em></strong>. These analyses evaluate changes in
cell-cell communication dependent on: <ul>
<li><a href="https://files.codeocean.com/files/verified/bffc457e-caa6-4c39-b869-f52330804db0_v2.0/results.5afea95c-aec4-455d-b06e-b0c12ef10df1/06-BALF-Tensor-Factorization.html">Different severities of COVID-19</a></li>
<li><a href="https://files.codeocean.com/files/verified/bffc457e-caa6-4c39-b869-f52330804db0_v2.0/results.5afea95c-aec4-455d-b06e-b0c12ef10df1/11-Brain-ASD-Tensor-Factorization.html">ASD condition of patients</a></li>
<li><a href="https://files.codeocean.com/files/verified/bffc457e-caa6-4c39-b869-f52330804db0_v2.0/results.5afea95c-aec4-455d-b06e-b0c12ef10df1/08-Celegans-Tensor-Factorization.html">Multiple time points of the <em>C. elegans</em> development</a></li>
</ul>
</li>
<li><strong>Detailed tutorials for running Tensor-cell2cell and downstream analyses:</strong><ul>
<li><a href="https://earmingol.github.io/cell2cell/tutorials/ASD/01-Tensor-Factorization-ASD/">Obtaining patterns of cell-cell communication with Tensor-cell2cell</a></li>
<li><a href="https://earmingol.github.io/cell2cell/tutorials/ASD/02-Factor-Specific-ASD/">Downstream analysis 1: Factor-specific analyses</a></li>
<li><a href="https://earmingol.github.io/cell2cell/tutorials/ASD/03-GSEA-ASD/">Downstream analysis 2: Gene Set Enrichment Analysis</a></li>
</ul>
</li>
<li><strong>Do you have precomputed communication scores?</strong> Re-use them as a prebuilt tensor as <a href="https://github.com/earmingol/cell2cell/blob/master/examples/tensor_cell2cell/Loading-PreBuiltTensor.ipynb">exemplified here</a>.
This allows reusing previous tensors you built or even plugging in communication scores from other tools.</li>
<li><strong>Run Tensor-cell2cell MUCH FASTER and ON THE CLOUD!</strong> An example to perform the analysis on
<strong>Google Colab while using a NVIDIA GPU</strong> is <a href="https://colab.research.google.com/drive/1xE6Pm1u-XoSWV8a3oYpixUFj64FIDtl0?usp=sharing">available here</a></li>
</ul>
<hr />
<table>
<thead>
<tr>
<th>cell2cell Examples</th>
<th>Tensor-cell2cell Examples</th>
</tr>
</thead>
<tbody>
<tr>
<td><img alt="cell2cell Logo" src="https://github.com/earmingol/cell2cell/blob/master/Logo.png?raw=true" /></td>
<td><img alt="Tensor-cell2cell Logo" src="https://github.com/earmingol/cell2cell/blob/master/LogoTensor.png?raw=true" /></td>
</tr>
<tr>
<td>- <a href="https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example.ipynb">Step-by-step Pipeline</a><br>- <a href="https://earmingol.github.io/cell2cell/tutorials/Toy-Example-BulkPipeline">Interaction Pipeline for Bulk Data</a><br>- <a href="https://earmingol.github.io/cell2cell/tutorials/Toy-Example-SingleCellPipeline">Interaction Pipeline for Single-Cell Data</a><br>- <a href="https://github.com/LewisLabUCSD/Celegans-cell2cell">Whole Body of <em>C. elegans</em></a></td>
<td>- <a href="https://earmingol.github.io/cell2cell/tutorials/ASD/01-Tensor-Factorization-ASD/">Obtaining patterns of cell-cell communication</a><br>- <a href="https://earmingol.github.io/cell2cell/tutorials/ASD/02-Factor-Specific-ASD/">Downstream 1: Factor-specific analyses</a><br>- <a href="https://earmingol.github.io/cell2cell/tutorials/ASD/03-GSEA-ASD/">Downstream 2: Patterns to functions (GSEA)</a><br>- <a href="https://colab.research.google.com/drive/1T6MUoxafTHYhjvenDbEtQoveIlHT2U6_?usp=sharing">Tensor-cell2cell in Google Colab (<strong>GPU</strong>)</a><br>- <a href="https://earmingol.github.io/cell2cell/tutorials/Tensor-cell2cell-Spatial/">Communication patterns in <strong>Spatial Transcriptomics</strong></a></td>
</tr>
</tbody>
</table>
<p>Reproducible runs of the analyses in the <a href="https://doi.org/10.1038/s41467-022-31369-2">Tensor-cell2cell paper</a> are available at <a href="https://doi.org/10.24433/CO.0051950.v2">CodeOcean.com</a></p>
<h2 id="liana-tensor-cell2cell">LIANA &amp; Tensor-cell2cell</h2>
<p>Quickstart and extended tutorials are available for <a href="https://ccc-protocols.readthedocs.io/">using Tensor-cell2cell in combination with LIANA</a></p>
<p>These tutorials include the use of multiple LR-based tools running on LIANA, different databases of ligand-receptor interactions,
downstream analyses, and the use of spatial transcriptomics.</p>
<hr />
<h2 id="common-issues">Common issues</h2>
<p>Explore our tutorials for using Tensor-cell2cell with <a href="https://github.com/saezlab/liana-py">LIANA</a> at <a href="https://ccc-protocols.readthedocs.io/">ccc-protocols.readthedocs.io</a>.</p>
<h2 id="common-issues">Common Issues</h2>
<ul>
<li>When running Tensor-cell2cell (<code>InteractionTensor.compute_tensor_factorization()</code> or <code>InteractionTensor.elbow_rank_selection()</code>), a common error is
associated with Memory. This may happen when the tensor is big enough to make the computer run out of memory when the input of the functions in the parentheses is
<code>init='svd'</code>. To avoid this issue, just replace it by <code>init='random'</code>.</li>
</ul>
<h2 id="ligand-receptor-pairs">Ligand-Receptor pairs</h2>
<ul>
<li>A repository with previously published lists of ligand-receptor pairs <a href="https://github.com/LewisLabUCSD/Ligand-Receptor-Pairs">is available here</a>.
You can use any of these lists as an input of cell2cell.</li>
<li><strong>Memory Errors with Tensor-cell2cell:</strong> If you encounter memory errors when performing tensor factorizations, try replacing <code>init='svd'</code> with <code>init='random'</code>.</li>
</ul>
<h2 id="ligand-receptor-pairs">Ligand-Receptor Pairs</h2>
<p>Find a curated list of ligand-receptor pairs for your analyses at our <a href="https://github.com/LewisLabUCSD/Ligand-Receptor-Pairs">GitHub Repository</a>.</p>
<h2 id="citation">Citation</h2>
<p>Please cite our work using the following references:</p>
<ul>
<li>
<p><strong>cell2cell</strong> should be cited using this research article:</p>
<ul>
<li>Armingol E., Ghaddar A., Joshi C.J., Baghdassarian H., Shamie I., Chan J.,
Her H.L., Berhanu S., Dar A., Rodriguez-Armstrong F., Yang O., O’Rourke E.J., Lewis N.E.
<a href="https://doi.org/10.1371/journal.pcbi.1010715">Inferring a spatial code of cell-cell interactions across a whole animal body</a>.
<em>PLOS Computational Biology *</em>18(11)<strong>: e1010715<em>, (2022). *</em>DOI: 10.1371/journal.pcbi.1010715</strong></li>
</ul>
<p><strong>cell2cell</strong>: <a href="https://doi.org/10.1371/journal.pcbi.1010715">Inferring a spatial code of cell-cell interactions across a whole animal body</a>.
<em>PLOS Computational Biology, 2022</em></p>
</li>
<li>
<p><strong>Tensor-cell2cell</strong> should be cited using this research article:</p>
<ul>
<li>Armingol E., Baghdassarian H., Martino C., Perez-Lopez A., Aamodt C., Knight R., Lewis N.E.
<a href="https://doi.org/10.1038/s41467-022-31369-2">Context-aware deconvolution of cell-cell communication with Tensor-cell2cell</a>
<em>Nat. Commun.</em> <strong>13</strong>, 3665 (2022). <strong>DOI: 10.1038/s41467-022-31369-2</strong></li>
</ul>
<p><strong>Tensor-cell2cell</strong>: <a href="https://doi.org/10.1038/s41467-022-31369-2">Context-aware deconvolution of cell-cell communication with Tensor-cell2cell</a>.
<em>Nature Communications, 2022.</em></p>
</li>
<li>
<p><strong>LIANA &amp; Tensor-cell2cell tutorials</strong> should be cited unsing this pre-print article:</p>
<ul>
<li>Baghdassarian H., Dimitrov D., Armingol E., Saez-Rodriguez J., Lewis N.E.
<a href="https://doi.org/10.1101/2023.04.28.538731">Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples</a>
<em>bioRxiv</em> (2023) <strong>DOI: 10.1101/2023.04.28.538731</strong></li>
</ul>
<p><strong>LIANA &amp; Tensor-cell2cell tutorials</strong>: <a href="https://doi.org/10.1101/2023.04.28.538731">Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples</a>.
<em>bioRxiv, 2023</em></p>
</li>
</ul>

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