vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis.
BMC Bioinformatics
; 25(1): 64, 2024 Feb 08.
Article
em En
| MEDLINE
| ID: mdl-38331751
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Perfilação da Expressão Gênica
Tipo de estudo:
Prognostic_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Austrália