vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis
- Author(s)
- Bhuva, DD; Tan, CW; Liu, N; Whitfield, HJ; Papachristos, N; Lee, SC; Kharbanda, M; Mohamed, A; Davis, MJ;
- Details
- Publication Year 2024-02-08,Volume 25,Issue #1,Page 64
- Journal Title
- BMC Bioinformatics
- 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.
- Publisher
- BMC
- Keywords
- Breast cancer; Cancer associated fibroblasts; Epithelial-mesenchymal transition; Gene-set enrichment analysis; Single-cell RNA-seq; Spatial transcriptomics
- Research Division(s)
- Bioinformatics
- PubMed ID
- 38331751
- Publisher's Version
- https://doi.org/10.1186/s12859-024-05676-y
- Open Access at Publisher's Site
https://doi.org/ 10.1186/s12859-024-05676-y - Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-02-29 09:01:49
Last Modified: 2024-02-29 09:09:54