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Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data.

Giulia Cesaro1, Giacomo Baruzzo1, Gaia Tussardi1

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Summary
This summary is machine-generated.

This study introduces scSeqCommDiff, a computational framework for analyzing cell-cell communication differences in single-cell transcriptomics data. It enables efficient and flexible analysis of altered cellular cross-talk across experimental conditions.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Single-cell transcriptomics is crucial for understanding cell-cell communication in biological processes.
  • Quantifying intercellular and intracellular communication variations across conditions is challenging.
  • Existing computational tools lack flexibility and user-friendly visualization for differential communication analysis.

Purpose of the Study:

  • To present a generalizable computational framework for inferring and analyzing differential cell-cell communication from single-cell transcriptomics data.
  • To address limitations in flexibility and interpretability of existing methods.
  • To enable fast and memory-efficient analysis of altered cellular cross-talk.

Main Methods:

  • Developed a statistical and network-based computational framework, scSeqCommDiff.
  • Integrated CClens, a user-friendly Shiny app for interactive analysis.
  • Employed validation using spatial transcriptomics and comparison with other tools.

Main Results:

  • Demonstrated the reliability, scalability, and efficiency of the scSeqCommDiff framework on large-scale datasets.
  • Successfully applied the workflow to a single-nucleus transcriptomics dataset.
  • Unraveled alterations in cell-cell interactions in amyotrophic lateral sclerosis patients compared to healthy subjects.

Conclusions:

  • The scSeqCommDiff framework provides a robust and efficient solution for differential cell-cell communication analysis.
  • The integrated CClens app enhances interpretability and user-friendliness.
  • The framework is valuable for studying cell-cell interactions in various biological contexts, including disease.