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  1. Home
  3. Determining Gene Specificity From Multivariate Single-cell Rna Sequencing Data.
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  3. Determining Gene Specificity From Multivariate Single-cell Rna Sequencing Data.

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Determining gene specificity from multivariate single-cell RNA sequencing data.

Nikhila P Swarna1, A Sina Booeshaghi2, Elisabeth Rebboah3,4

  • 1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.

Biorxiv : the Preprint Server for Biology
|December 3, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We developed ember, a novel method for identifying gene specificity in single-cell genomics. Ember reveals complex gene expression patterns across mouse tissues and human cells, highlighting context-dependent gene function.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Identifying genes specific to biological categories or experimental conditions is crucial in single-cell genomics.
  • Existing methods for gene specificity analysis lack a standardized axiomatic approach.

Purpose of the Study:

  • To develop a robust and axiomatic method for measuring gene specificity.
  • To apply this method to uncover novel gene expression patterns in mouse and human datasets.

Main Methods:

  • Developed ember (Entropy Metrics for Biological ExploRation), an axiomatic approach for gene specificity measurement.
  • Applied ember to single-cell RNA sequencing data from eight mouse tissues and founder strains.
  • Utilized unsupervised learning on entropy metrics to identify specialized genes.
  • Extended ember analysis to a human peripheral blood mononuclear cell (PBMC) dataset.

    • Main Results:

    • Ember is the only method satisfying four key properties for gene specificity.
    • Mouse gene specificity is often unintuitive, with context-dependent housekeeping genes and strain-driven cell type switching.
    • Uncovered shared genes specific to male gonads and kidneys, and stage-specific kidney genes.
    • Human PBMC variation is concentrated in classical monocytes, with sex, age, and ancestry-specific genes identified.

    Conclusions:

    • Ember provides a powerful, axiomatic tool for gene specificity analysis in single-cell genomics.
    • The study reveals complex and context-dependent gene expression patterns in mice and humans.
    • Findings offer insights into genetic variation and provide a roadmap for future research using the murine model.