Generalized gene co-expression analysis via subspace clustering using low-rank representation
dc.contributor.author | Wang, Tongxin | |
dc.contributor.author | Zhang, Jie | |
dc.contributor.author | Huang, Kun | |
dc.contributor.department | Medical and Molecular Genetics, School of Medicine | en_US |
dc.date.accessioned | 2019-08-27T17:16:59Z | |
dc.date.available | 2019-08-27T17:16:59Z | |
dc.date.issued | 2019-05-01 | |
dc.description.abstract | BACKGROUND: Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules. RESULTS: We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values. CONCLUSIONS: The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms. | en_US |
dc.identifier.citation | Wang, T., Zhang, J., & Huang, K. (2019). Generalized gene co-expression analysis via subspace clustering using low-rank representation. BMC bioinformatics, 20(Suppl 7), 196. doi:10.1186/s12859-019-2733-5 | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/20620 | |
dc.language.iso | en_US | en_US |
dc.publisher | Biomed Central | en_US |
dc.relation.isversionof | 10.1186/s12859-019-2733-5 | en_US |
dc.relation.journal | BMC Bioinformatics | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.source | PMC | en_US |
dc.subject | Gene co-expression network analysis | en_US |
dc.subject | Low-rank representation | en_US |
dc.subject | Subspace clustering | en_US |
dc.title | Generalized gene co-expression analysis via subspace clustering using low-rank representation | en_US |
dc.type | Article | en_US |