Erick Armingol | Computational suite for studying cell-cell interactions and communication

Computational suite for studying cell-cell interactions and communication

The regular cell2cell, which includes the main approaches described in our review.
Tensor-cell2cell, a novel method that uses tensor factorization to deconvolve patterns of cell-cell communication across multiple cellular contexts (e.g., time points, disease states, cellular location, patients, etc.), as described in our research article.

A toy example using the under-the-hood methods of cell2cell is available here. This case allows personalizing the analyses in a higher level, but it may result harder to use.
A toy example using an Interaction Pipeline for bulk data is available here. An Interaction Pipeline makes cell2cell easier to use.
A toy example using an Interaction Pipeline for single-cell data is available here. An Interaction Pipeline makes cell2cell easier to use.
An example of using cell2cell to infer cell-cell interactions across the whole body of C. elegans is available here

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Jupyter notebooks for reproducing the results in the manuscript of Tensor-cell2cell are available and can be run online in codeocean.com. It specifically contains analyses on datasets of COVID-19, Autism Spectrum Disorders (ASD) and the embryonic development of C. elegans. These analyses evaluate changes in cell-cell communication dependent on:
Detailed tutorials for running Tensor-cell2cell and downstream analyses:
Do you have precomputed communication scores? Re-use them as a prebuilt tensor as exemplified here. This allows reusing previous tensors you built or even plugging in communication scores from other tools.
Run Tensor-cell2cell much faster! An example to perform the analysis using a Nvidia GPU is available here