Network Visualization and Analysis of Spatially Aware Gene Expression Data with InsituNet

Area of Science:

• Spatial transcriptomics
• Bioinformatics
• Computational biology

Background:

• In situ sequencing methods provide high-resolution spatial RNA data.
• Analyzing and visualizing large-scale transcriptomic data from tissues remains challenging.
• Existing tools lack comprehensive network-based visualization for spatial RNA data.

Purpose of the Study:

• To develop InsituNet, a novel application for analyzing and visualizing in situ sequencing data.
• To create interactive network-based visualizations of transcript localization and expression.
• To enable the exploration of spatial co-expression relationships between transcripts.

Main Methods:

• InsituNet converts in situ sequencing data into network graphs.
• Transcripts are represented as nodes, and co-expression relationships as edges.
• The application is integrated into the Cytoscape platform for interactive analysis.

Main Results:

• InsituNet generates network visualizations of spatial transcriptomic data.
• It facilitates the identification of spatial co-expression patterns.
• The tool allows for the comparison of co-expression profiles across different tissue regions or sections.

Conclusions:

• InsituNet provides a powerful new method for analyzing complex spatial RNA sequencing data.
• The network-based approach enhances understanding of transcript relationships in tissues.
• This tool can uncover how spatial co-expression dynamics vary within and between tissue samples.