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Related Experiment Video

Updated: Sep 15, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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GeneSurfer enables transcriptome-wide exploration and annotation of gene co-expression modules in 3D spatial

Chang Li1, Julian Thijssen1, Thomas Kroes1

  • 1Department of Radiology, Leiden University Medical Center, ZA Leiden 2333, the Netherlands.

Iscience
|July 18, 2025
PubMed
Summary
This summary is machine-generated.

GeneSurfer is a new tool for exploring gene co-expression patterns in 3D spatial data. It helps researchers find and understand localized gene activity in large datasets.

Keywords:
BioinformaticsComplex system biologyTranscriptomics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene co-expression analysis is vital for understanding biological functions.
  • Existing tools lack capabilities for exploring localized co-expression in large datasets.

Purpose of the Study:

  • To introduce GeneSurfer, an interactive interface for exploring localized transcriptome-wide gene co-expression patterns in the 3D spatial domain.
  • To provide a tool for analyzing spatial transcriptomics and single-cell RNA sequencing data.

Main Methods:

  • Developed GeneSurfer, an interactive interface with features for gene filtering, spatial clustering, and 3D rendering.
  • Integrated on-the-fly Gene Ontology (GO) term annotation for co-expressed gene sets.
  • Utilized linked views for investigating gene co-expression in the spatial domain.

Main Results:

  • GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression patterns.
  • Demonstrated utility with spatially resolved transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas.
  • Facilitated biological insights and hypothesis generation.

Conclusions:

  • GeneSurfer addresses the need for exploratory analysis tools for localized gene co-expression.
  • The tool enhances the investigation of spatial gene expression patterns.
  • Enables deeper understanding of biological functions through spatial co-expression analysis.