Abstract:
: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.
journal_name
Int J Genomicsjournal_title
International journal of genomicsauthors
Li X,Fu Y,Wang X,DeMeo DL,Tantisira K,Weiss ST,Qiu Wdoi
10.1155/2018/6591634subject
Has Abstractpub_date
2018-07-12 00:00:00pages
6591634eissn
2314-436Xissn
2314-4378journal_volume
2018pub_type
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