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

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.