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Related Experiment Video

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Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.

Hratch M Baghdassarian1, Daniel Dimitrov2, Erick Armingol1

  • 1Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.

Cell Reports Methods
|April 17, 2024
PubMed
Summary

This study integrates LIANA and Tensor-cell2cell tools for robust cell-cell communication inference. The combined approach offers flexible method selection and unsupervised deconvolution for biological insights from multi-sample datasets.

Keywords:
CP: Cell biologyCP: Systems biologycell-cell communicationcontext dependentligand-receptor interactionsmultiple conditionssingle-cell RNA sequencingtensor decomposition

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Area of Science:

  • Computational Biology
  • Single-cell Genomics
  • Systems Biology

Background:

  • Cell-cell communication is crucial for understanding coordinated biological processes.
  • Data-driven inference methods are increasingly used to identify communication pathways.
  • Existing tools may lack flexibility or require specific methodological choices.

Purpose of the Study:

  • To integrate LIANA and Tensor-cell2cell for enhanced cell-cell communication analysis.
  • To provide a flexible and robust workflow for identifying communication programs across multiple samples.
  • To facilitate method selection and unsupervised deconvolution for biological insights.

Main Methods:

  • Integration of LIANA and Tensor-cell2cell computational tools.
  • Deployment of multiple existing methods and resources for cell-cell communication inference.
  • Step-by-step analysis protocols provided in Python and R with online tutorials.
  • Unsupervised deconvolution for summarizing biological insights.

Main Results:

  • Demonstration of a combined workflow for robust and flexible cell-cell communication identification.
  • Facilitation of informed method selection for inferring cell-cell communication.
  • Successful unsupervised deconvolution to extract and summarize biological insights.
  • A complete workflow from installation to visualization typically completed in ~1.5 hours for large datasets.

Conclusions:

  • The integrated LIANA and Tensor-cell2cell workflow enhances the identification of cell-cell communication programs.
  • This approach provides a flexible, robust, and efficient solution for multi-sample single-cell data analysis.
  • The provided tutorials and protocols enable wider adoption and application of these computational tools.