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CellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell

Jade Young1, Jun Inamo1,2, Zachary Caterer3

  • 1Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.

Biorxiv : the Preprint Server for Biology
|February 20, 2025
PubMed
Summary
This summary is machine-generated.

CellPhenoX, an explainable AI tool, links cell phenotypes to disease outcomes. It identifies cell populations associated with clinical conditions, aiding in disease research and clinical translation.

Keywords:
Single-cell multi-omicsclinical associationdifferential abundanceexplainable machine learning

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Area of Science:

  • Computational biology
  • Machine learning in healthcare
  • Single-cell genomics

Background:

  • Single-cell technologies reveal cellular heterogeneity in disease.
  • Linking cell phenotypes to clinical outcomes is challenging with large datasets.

Purpose of the Study:

  • Introduce CellPhenoX, an explainable machine learning method.
  • Identify cell-specific phenotypes influencing clinical outcomes.
  • Generate interpretable, cell-specific scores for clinical relevance.

Main Methods:

  • Integration of classification models and explainable AI.
  • Statistical framework for cell-specific scores.
  • Application to simulations, binary, and multi-class single-cell studies.

Main Results:

  • CellPhenoX successfully identified cell populations linked to clinical phenotypes.
  • Detected an activated monocyte phenotype in COVID-19 correlated with severity.
  • Uncovered an inflammation-associated fibroblast gradient in ulcerative colitis.

Conclusions:

  • CellPhenoX effectively detects clinically relevant phenotypic changes in complex single-cell data.
  • Potential to translate single-cell findings into clinical impact.
  • Applicable to single-cell data with multiple sources of variation.