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ScIsoX: a multidimensional framework for measuring isoform-level transcriptomic complexity in single cells.

Siyuan Wu1,2,3, Ulf Schmitz4,5,6

  • 1Computational Biomedicine Lab, College of Science and Engineering, James Cook University, Townsville, QLD, Australia.

Genome Biology
|September 22, 2025
PubMed
Summary
This summary is machine-generated.

ScIsoX is a new computational framework for single-cell isoform analysis. It reveals complex transcriptomic patterns missed by gene-level methods, advancing single-cell transcriptomics.

Keywords:
Alternative splicingIsoform analysisIsoform-resolved transcriptomicsSingle-cell isoform sequencing

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Area of Science:

  • Computational biology
  • Genomics
  • Molecular biology

Background:

  • Single-cell analysis offers high-resolution transcript expression insights.
  • Existing analytical frameworks lack systematic measurement of transcriptomic complexity.

Purpose of the Study:

  • Introduce ScIsoX, a computational framework for systematic single-cell isoform analysis.
  • Enable comprehensive characterization of transcriptomic complexity.

Main Methods:

  • Developed ScIsoX with a novel hierarchical data structure.
  • Integrated complexity metrics and visualization tools for isoform-level analysis.
  • Applied ScIsoX to diverse single-cell isoform sequencing datasets.

Main Results:

  • ScIsoX facilitates exploration of global and cell-type-specific isoform expression.
  • Identified multidimensional complexity signatures missed by gene-level approaches.
  • Demonstrated ScIsoX utility across multiple real-world datasets.

Conclusions:

  • ScIsoX provides a robust framework for single-cell transcriptomic complexity analysis.
  • Enhances understanding of alternative splicing and isoform dynamics.
  • Offers a valuable tool for advancing single-cell genomics research.