vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene-set analysis (GSA) is crucial for interpreting omics data but often yields numerous results.
• Effective summarization and visualization tools for GSA are lacking, hindering hypothesis generation.
• Existing webservers lack end-to-end solutions, especially for emerging single-cell and spatial omics data.

Purpose of the Study:

• To present vissE.Cloud, a novel webserver for comprehensive gene-set analysis.
• To provide end-to-end GSA with advanced summarization and interactive visualization capabilities.
• To support diverse omics data types, including gene lists and spatial omics data.

Main Methods:

• Utilizes algorithms from the R package vissE for gene-set summarization by identifying biological themes.
• Accepts gene lists as input and directly analyzes raw single-cell and spatial omics data (e.g., CosMx, Xenium).
• Implements a hierarchical structure for interactive exploration at gene, gene-set, and cluster levels.

Main Results:

• vissE.Cloud offers end-to-end GSA, integrating summarization and interactive visualization.
• It uniquely supports the analysis of sub-cellular localized spatial omics data.
• The hierarchical results structure enables efficient investigation of complex GSA outputs.

Conclusions:

• vissE.Cloud addresses the need for improved GSA result interpretation and visualization.
• It provides a versatile, end-to-end solution for various omics data types, including spatial omics.
• The webserver facilitates hypothesis generation by enabling swift, interactive exploration of GSA findings.