Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs
<p>Abstract</p> <p>Background</p> <p>Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate di...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2011-08-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/12/315 |
| _version_ | 1818369374656921600 |
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| author | Ye Zhi-Qiang Liu Bao-Hong Yu Hui Li Chun Li Yi-Xue Li Yuan-Yuan |
| author_facet | Ye Zhi-Qiang Liu Bao-Hong Yu Hui Li Chun Li Yi-Xue Li Yuan-Yuan |
| author_sort | Ye Zhi-Qiang |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance.</p> <p>Results</p> <p>We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links). Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D) expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis.</p> <p>Conclusions</p> <p>This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.</p> |
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| id | doaj.art-9702bb7395c143f8a717b0e7db2b7d5d |
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| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T23:22:50Z |
| publishDate | 2011-08-01 |
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| spelling | doaj.art-9702bb7395c143f8a717b0e7db2b7d5d2022-12-21T23:27:40ZengBMCBMC Bioinformatics1471-21052011-08-0112131510.1186/1471-2105-12-315Link-based quantitative methods to identify differentially coexpressed genes and gene PairsYe Zhi-QiangLiu Bao-HongYu HuiLi ChunLi Yi-XueLi Yuan-Yuan<p>Abstract</p> <p>Background</p> <p>Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance.</p> <p>Results</p> <p>We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links). Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D) expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis.</p> <p>Conclusions</p> <p>This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.</p>http://www.biomedcentral.com/1471-2105/12/315 |
| spellingShingle | Ye Zhi-Qiang Liu Bao-Hong Yu Hui Li Chun Li Yi-Xue Li Yuan-Yuan Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs BMC Bioinformatics |
| title | Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs |
| title_full | Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs |
| title_fullStr | Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs |
| title_full_unstemmed | Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs |
| title_short | Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs |
| title_sort | link based quantitative methods to identify differentially coexpressed genes and gene pairs |
| url | http://www.biomedcentral.com/1471-2105/12/315 |
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