Journal Article

vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Liu, Ning, Papachristos, Nicholas, Whitfield, Holly J., Lee, Samuel C., Kharbanda, Malvika, Davis, Melissa J., Mohamed, Ahmed, Bhuva, Dharmesh D., Tan, Chin Wee

2024

Overview

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE’s versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Adaptability / Algorithms / Bioinformatics / Biological systems / Biologists / Biology / Biomedical and Life Sciences / Breast cancer / Cancer associated fibroblasts / Clustering / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Datasets / Epithelial-mesenchymal transition / Experiments / Factor analysis / Functional analysis / Gene set enrichment analysis / Genes / Genetic aspects / Genetic research / Genomics / Knowledge bases (artificial intelligence) / Life Sciences / Microarrays / Ontology / Phenotype / Phenotypes / Production methods / Semantics / Single-cell RNA-seq / Spatial data / Spatial transcriptomics / Stem cells / Transcriptomics / Visualization / Visualization (Computers)