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Related Experiment Video

Updated: May 11, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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SciGeneX: enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics

Julie Bavais1,2, Jessica Chevallier1,2, Lionel Spinelli1,2

  • 1Aix-Marseille Univ, INSERM, TAGC, Turing Centre for Living systems, 13288 Marseille, France.

NAR Genomics and Bioinformatics
|April 18, 2025
PubMed
Summary

SciGeneX, a new R package, analyzes gene expression by looking at gene groups, not just individual genes. This approach reveals hidden cell populations in single-cell and spatial transcriptomics data, improving the understanding of cellular diversity.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Current single-cell and spatial transcriptomics analysis often uses a gene-centric approach.
  • This overlooks the collective behavior of genes and their role in cellular heterogeneity.
  • Understanding cell populations requires analyzing activated and repressed pathways collectively.

Purpose of the Study:

  • To introduce SciGeneX, an R package for analyzing single-cell and spatial transcriptomics data.
  • To provide a broader view of gene behavior for more accurate insights into cellular heterogeneity.
  • To uncover rare and novel cell populations.

Main Methods:

  • SciGeneX implements a neighborhood analysis and graph partitioning method.
  • It generates co-expression gene modules that reflect cellular heterogeneity.
  • These modules can be shared across or specific to cell populations.

Main Results:

  • SciGeneX successfully uncovers rare and novel cell populations in human thymus spatial transcriptomics data.
  • The package outperforms existing methods on both artificial and experimental datasets.
  • Combinations of gene modules highlight specific cell populations, including rare ones.

Conclusions:

  • SciGeneX offers a novel approach to analyzing single-cell and spatial transcriptomics data.
  • It aids in unraveling cellular and molecular diversity by focusing on gene modules.
  • The package enhances the discovery of cell populations and improves understanding of biological systems.