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Related Experiment Video

Updated: May 12, 2026

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
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scFocus: Detecting branching probabilities in single-cell data with SAC.

Chunlin Chen1, Zeyu Fu2, Jiajia Yang1

  • 1Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.

Computational and Structural Biotechnology Journal
|June 16, 2025
PubMed
Summary

scFocus, a new algorithm, precisely maps cell differentiation pathways by analyzing gene expression. This tool enhances the interpretation of single-cell RNA sequencing data for researchers and clinicians.

Keywords:
Biological divisionBranch probabilityReinforcement learningSingle cell

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Single-cell transcriptomics reveals cell differentiation but struggles with precise lineage gene set composition.
  • Existing methods for analyzing gene expression changes in complex systems often lack detail on lineage-specific gene expression.
  • Understanding continuous cell states and differentiation trajectories is crucial in biological research.

Purpose of the Study:

  • To develop a novel analytical algorithm, single-cell (sc)-Focus, for precise cell subpopulation division and lineage tracing in single-cell RNA sequencing data.
  • To improve the characterization of cell differentiation processes and gene expression changes across lineages.
  • To provide a user-friendly tool for the analysis and interpretation of single-cell RNA sequencing data.

Main Methods:

  • scFocus utilizes reinforcement learning and unsupervised branching in a low-dimensional latent space to divide cell subpopulations.
  • The algorithm analyzes lineage component strength and its correlation with hallmark gene expression regions.
  • The method was validated on ten diverse single-cell datasets, including multi-batch experiments.

Main Results:

  • scFocus effectively captures cell differentiation processes by aligning lineage component strength with hallmark gene expression.
  • The algorithm demonstrates superior subpopulation discriminative power compared to standard low-dimensional latent space analysis.
  • scFocus shows broad applicability across various dataset scales and types, including multi-batch data for detecting experimental effects.

Conclusions:

  • scFocus provides a robust and effective method for analyzing cell differentiation trajectories and gene expression in single-cell RNA sequencing data.
  • The developed online analysis tool facilitates streamlined processing, visualization, and interpretation of complex single-cell data for researchers and clinicians.
  • scFocus offers a significant advancement in understanding cellular heterogeneity and developmental processes.