cell2cell 0.6.6

TBD

Inferring cell-cell interactions from transcriptomes with cell2cell

Getting started

Please refer to the cell2cell website, which includes tutorials and documentation

Installation

First, install Anaconda following this tutorial

Once installed, create a new conda environment:

conda create -n cell2cell -y python=3.7 jupyter

Activate that environment:

conda activate cell2cell

Then, install cell2cell:

pip install cell2cell

Examples

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• 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:
  • Different severities of COVID-19
  • ASD condition of patients
  • Multiple time points of the C. elegans development
• Detailed tutorials for running Tensor-cell2cell and downstream analyses:
  • Obtaining patterns of cell-cell communication with Tensor-cell2cell
  • Downstream analysis 1: Factor-specific analyses
  • Downstream analysis 2: Gene Set Enrichment Analysis
• 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 and ON THE CLOUD! An example to perform the analysis on Google Colab while using a NVIDIA GPU is available here

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Common issues

• When running Tensor-cell2cell (InteractionTensor.compute_tensor_factorization() or InteractionTensor.elbow_rank_selection()), 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 init='svd'. To avoid this issue, just replace it by init='random'.

Ligand-Receptor pairs

• A repository with previously published lists of ligand-receptor pairs is available here. You can use any of these lists as an input of cell2cell.

Citation

• cell2cell should be cited using this research article:

  • 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. Inferring a spatial code of cell-cell interactions across a whole animal body. PLOS Computational Biology 18(11): e1010715, (2022). DOI: 10.1371/journal.pcbi.1010715

• Tensor-cell2cell should be cited using this research article:

  • Armingol E., Baghdassarian H., Martino C., Perez-Lopez A., Aamodt C., Knight R., Lewis N.E. Context-aware deconvolution of cell-cell communication with Tensor-cell2cell Nat. Commun. 13, 3665 (2022). DOI: 10.1038/s41467-022-31369-2