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Updated: Nov 17, 2025

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IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis.

Yuzhou Chang1, Carter Allen1, Changlin Wan2

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.

Bioinformatics (Oxford, England)
|February 17, 2021
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Summary

IRIS-FGM is a new R package that enhances the analysis of single-cell RNA sequencing (scRNA-Seq) data. It identifies functional gene modules (FGMs) and aids in cell clustering for complex disease research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-Seq) reveals cellular heterogeneity and signature genes crucial for understanding complex diseases.
  • Identifying condition-specific functional gene modules (FGMs) is key to deciphering interactive gene networks and biological processes within cell clusters.
  • Existing tools like QUBIC2 are effective for FGM identification but have limited downstream analysis capabilities due to their C implementation.

Purpose of the Study:

  • To develop an R package, IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis), for comprehensive scRNA-Seq data analysis.
  • To enable efficient identification of condition-specific FGMs and facilitate cell clustering.
  • To provide seamless integration with existing scRNA-Seq analysis pipelines, including those using Seurat objects.

Main Methods:

  • Development of the IRIS-FGM R package, leveraging the QUBIC2 biclustering algorithm.
  • Implementation of functionalities for FGM identification, cell type/cluster prediction, differential gene expression analysis, and pathway enrichment analysis.
  • Ensuring compatibility with Seurat objects for streamlined integration into established workflows.

Main Results:

  • IRIS-FGM effectively identifies condition-specific FGMs from scRNA-Seq data.
  • The package supports accurate cell type/cluster prediction and uncovers differentially expressed genes.
  • Pathway enrichment analysis is integrated, providing deeper biological insights.
  • IRIS-FGM accepts Seurat objects, enhancing its utility within common analysis pipelines.

Conclusions:

  • IRIS-FGM provides a powerful and integrated platform for analyzing scRNA-Seq data.
  • The package overcomes limitations of previous tools by offering extensive downstream analysis capabilities.
  • IRIS-FGM facilitates a deeper understanding of cellular heterogeneity and gene regulatory networks in complex diseases.