Generalized gene co-expression analysis via subspace clustering using low-rank representation
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 ide...
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| Format: | Article |
| Language: | English |
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BMC
2019-05-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2733-5 |
| _version_ | 1811286665776332800 |
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| author | Tongxin Wang Jie Zhang Kun Huang |
| author_facet | Tongxin Wang Jie Zhang Kun Huang |
| author_sort | Tongxin Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T03:04:00Z |
| format | Article |
| id | doaj.art-3c05a8f8603a46fcaa57729a271728d6 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T03:04:00Z |
| publishDate | 2019-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-3c05a8f8603a46fcaa57729a271728d62022-12-22T03:05:19ZengBMCBMC Bioinformatics1471-21052019-05-0120S7172710.1186/s12859-019-2733-5Generalized gene co-expression analysis via subspace clustering using low-rank representationTongxin Wang0Jie Zhang1Kun Huang2Department of Computer Science, Indiana University BloomingtonDepartment of Medical and Molecular Genetics, Indiana University School of MedicineDepartment of Medicine, Indiana University School of MedicineAbstract 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.http://link.springer.com/article/10.1186/s12859-019-2733-5Gene co-expression network analysisSubspace clusteringLow-rank representation |
| spellingShingle | Tongxin Wang Jie Zhang Kun Huang Generalized gene co-expression analysis via subspace clustering using low-rank representation BMC Bioinformatics Gene co-expression network analysis Subspace clustering Low-rank representation |
| title | Generalized gene co-expression analysis via subspace clustering using low-rank representation |
| title_full | Generalized gene co-expression analysis via subspace clustering using low-rank representation |
| title_fullStr | Generalized gene co-expression analysis via subspace clustering using low-rank representation |
| title_full_unstemmed | Generalized gene co-expression analysis via subspace clustering using low-rank representation |
| title_short | Generalized gene co-expression analysis via subspace clustering using low-rank representation |
| title_sort | generalized gene co expression analysis via subspace clustering using low rank representation |
| topic | Gene co-expression network analysis Subspace clustering Low-rank representation |
| url | http://link.springer.com/article/10.1186/s12859-019-2733-5 |
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