Wang, TongxinZhang, JieHuang, Kun2019-08-272019-08-272019-05-01Wang, 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/s12859-019-2733-5https://hdl.handle.net/1805/20620BACKGROUND: 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-USAttribution-NonCommercial-NoDerivs 3.0 United StatesGene co-expression network analysisLow-rank representationSubspace clusteringArticle