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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
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ICON: An isoform-aware hierarchical random forest model for cell type classification.

Hettiarachchige Wijewardena1, Siyuan Wu1,2,3, Ulf Schmitz1,3,4

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

Biorxiv : the Preprint Server for Biology
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

We developed ICON, a novel framework for isoform-aware cell classification using single-cell RNA sequencing (scRNA-seq). ICON leverages both gene and isoform expression data to improve cell type annotation accuracy and biological insight from long-read sequencing.

Keywords:
alternative splicingcell type annotationhierarchical random forestisoformssingle-cell long-read RNA sequencing

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
  • Short-read scRNA-seq has limitations in capturing full-length transcripts and isoform diversity.
  • Existing cell annotation methods primarily use gene-level data, overlooking isoform information.

Purpose of the Study:

  • To develop a novel computational framework for cell type classification that incorporates isoform-level information from scRNA-seq data.
  • To enhance the resolution and accuracy of cell type annotation by utilizing isoform profiles.
  • To provide an interpretable method linking cell classification to underlying regulatory mechanisms.

Main Methods:

  • Developed a hierarchical random forest (HRF) framework named ICON.
  • Jointly modeled gene- and isoform-level expression data.
  • Employed a two-stage classification strategy: initial assignment using variable features, followed by targeted reclassification of ambiguous cells based on isoform and gene usage.

Main Results:

  • ICON demonstrated improved cell type classification accuracy compared to gene-based methods on long-read scRNA-seq datasets.
  • The framework successfully captured both gene abundance and isoform usage patterns.
  • ICON identified key genes and isoforms driving cell type discrimination, offering interpretable insights.

Conclusions:

  • ICON provides a robust and interpretable foundation for isoform-aware cell type annotation.
  • The framework enhances resolution and biological insight, particularly with the increasing adoption of long-read sequencing.
  • Leveraging isoform information unlocks a deeper understanding of cellular states and heterogeneity.