Detecting Differentially Variable MicroRNAs via Model-Based Clustering.

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 Genomics

authors

Li X,Fu Y,Wang X,DeMeo DL,Tantisira K,Weiss ST,Qiu W

doi

10.1155/2018/6591634

subject

Has Abstract

pub_date

2018-07-12 00:00:00

pages

6591634

eissn

2314-436X

issn

2314-4378

journal_volume

2018

pub_type

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