Detecting Differentially Variable MicroRNAs via Model-Based Clustering
Identifying differentially variable (DV) genomic probes is becoming a new approach to detect novel genomic risk factors for complex human diseases. The F test is the standard equal-variance test in statistics. For high-throughput genomic data, the probe-wise F test has been successfully used to dete...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2018/6591634 |
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| author | Xuan Li Yuejiao Fu Xiaogang Wang Dawn L. DeMeo Kelan Tantisira Scott T. Weiss Weiliang Qiu |
| author_facet | Xuan Li Yuejiao Fu Xiaogang Wang Dawn L. DeMeo Kelan Tantisira Scott T. Weiss Weiliang Qiu |
| author_sort | Xuan Li |
| collection | DOAJ |
| description | Identifying differentially variable (DV) genomic probes is becoming a new approach to detect novel genomic risk factors for complex human diseases. The F test is the standard equal-variance test in statistics. For high-throughput genomic data, the probe-wise F test has been successfully used to detect biologically relevant DNA methylation marks that have different variances between two groups of subjects (e.g., cases versus controls). In addition to DNA methylation, microRNA (miRNA) is another important mechanism of epigenetics. However, to the best of our knowledge, no studies have identified DV miRNAs. In this article, we proposed a novel model-based clustering method to improve the power of the probe-wise F test to detect DV miRNAs. We imposed special structures on covariance matrices for each cluster of miRNAs based on the prior information about the relationship between variances in cases and controls and about the independence among them. Simulation studies showed that the proposed method seems promising in detecting DV probes. Based on two real datasets about human hepatocellular carcinoma (HCC), we identified 7 DV-only miRNAs (hsa-miR-1826, hsa-miR-191, hsa-miR-194-star, hsa-miR-222, hsa-miR-502-3p, hsa-miR-93, and hsa-miR-99b) using the proposed method, one (hsa-miR-1826) of which has not yet been reported to be related to HCC in the literature. |
| format | Article |
| id | doaj-art-998cff628b4e426e9ad90afbeb973f4d |
| institution | OA Journals |
| issn | 2314-436X 2314-4378 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-998cff628b4e426e9ad90afbeb973f4d2025-08-20T02:10:19ZengWileyInternational Journal of Genomics2314-436X2314-43782018-01-01201810.1155/2018/65916346591634Detecting Differentially Variable MicroRNAs via Model-Based ClusteringXuan Li0Yuejiao Fu1Xiaogang Wang2Dawn L. DeMeo3Kelan Tantisira4Scott T. Weiss5Weiliang Qiu6Department of Mathematics and Statistics, York University, Toronto, ON, CanadaDepartment of Mathematics and Statistics, York University, Toronto, ON, CanadaDepartment of Mathematics and Statistics, York University, Toronto, ON, CanadaChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USAChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USAChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USAChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USAIdentifying differentially variable (DV) genomic probes is becoming a new approach to detect novel genomic risk factors for complex human diseases. The F test is the standard equal-variance test in statistics. For high-throughput genomic data, the probe-wise F test has been successfully used to detect biologically relevant DNA methylation marks that have different variances between two groups of subjects (e.g., cases versus controls). In addition to DNA methylation, microRNA (miRNA) is another important mechanism of epigenetics. However, to the best of our knowledge, no studies have identified DV miRNAs. In this article, we proposed a novel model-based clustering method to improve the power of the probe-wise F test to detect DV miRNAs. We imposed special structures on covariance matrices for each cluster of miRNAs based on the prior information about the relationship between variances in cases and controls and about the independence among them. Simulation studies showed that the proposed method seems promising in detecting DV probes. Based on two real datasets about human hepatocellular carcinoma (HCC), we identified 7 DV-only miRNAs (hsa-miR-1826, hsa-miR-191, hsa-miR-194-star, hsa-miR-222, hsa-miR-502-3p, hsa-miR-93, and hsa-miR-99b) using the proposed method, one (hsa-miR-1826) of which has not yet been reported to be related to HCC in the literature.http://dx.doi.org/10.1155/2018/6591634 |
| spellingShingle | Xuan Li Yuejiao Fu Xiaogang Wang Dawn L. DeMeo Kelan Tantisira Scott T. Weiss Weiliang Qiu Detecting Differentially Variable MicroRNAs via Model-Based Clustering International Journal of Genomics |
| title | Detecting Differentially Variable MicroRNAs via Model-Based Clustering |
| title_full | Detecting Differentially Variable MicroRNAs via Model-Based Clustering |
| title_fullStr | Detecting Differentially Variable MicroRNAs via Model-Based Clustering |
| title_full_unstemmed | Detecting Differentially Variable MicroRNAs via Model-Based Clustering |
| title_short | Detecting Differentially Variable MicroRNAs via Model-Based Clustering |
| title_sort | detecting differentially variable micrornas via model based clustering |
| url | http://dx.doi.org/10.1155/2018/6591634 |
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