GeneSurfer enables transcriptome-wide exploration and annotation of gene co-expression modules in 3D spatial transcriptomics data

Journal Article (2025)
Author(s)

C. Li (Leiden University Medical Center, TU Delft - Computer Graphics and Visualisation)

Julian Thijssen (Leiden University Medical Center)

Thomas Kroes (Leiden University Medical Center)

Ximaine van der Burg (Leiden University Medical Center)

Louise Van Der Weerd (Leiden University Medical Center)

T. Höllt (TU Delft - Computer Graphics and Visualisation)

B.P.F. Lelieveldt (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)

Research Group
Computer Graphics and Visualisation
DOI related publication
https://doi.org/10.1016/j.isci.2025.112713
More Info
expand_more
Publication Year
2025
Language
English
Research Group
Computer Graphics and Visualisation
Issue number
7
Volume number
28
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Gene co-expression provides crucial insights into biological functions, however, there is a lack of exploratory analysis tools for localized gene co-expression in large-scale datasets. We present GeneSurfer, an interactive interface designed to explore localized transcriptome-wide gene co-expression patterns in the 3D spatial domain. Key features of GeneSurfer include transcriptome-wide gene filtering and gene clustering based on spatial local co-expression within transcriptomically similar cells, multi-slice 3D rendering of average expression of gene clusters, and on-the-fly Gene Ontology term annotation of co-expressed gene sets. Additionally, GeneSurfer offers multiple linked views for investigating individual genes or gene co-expression in the spatial domain at each exploration stage. Demonstrating its utility with both spatially resolved transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas, GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression, providing biological insights and facilitating hypothesis generation and validation.