Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data

Hossain Ahmed () and Beyene Joseph
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Hossain Ahmed: Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S4K1, Canada
Beyene Joseph: Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S4K1, Canada

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 6, 743-755

Abstract: MicroRNAs (miRNAs) are short non-coding RNAs that play critical roles in numerous cellular processes through post-transcriptional functions. The aberrant role of miRNAs has been reported in a number of diseases. A robust computational method is vital to discover novel miRNAs where level of noise varies dramatically across the different miRNAs. In this paper, we propose a flexible rank-based procedure for estimating a weighted log partial area under the receiver operating characteristic (ROC) curve statistic for selecting differentially expressed miRNAs. The statistic combines results taking partial area under the curve (pAUC) and their corresponding variance. The proposed method does not involve complicated formulas and does not require advanced programming skills. Two real datasets are analyzed to illustrate the method and a simulation study is carried out to assess the performance of different miRNA ranking statistics. We conclude that the proposed method offers robust results with large samples for miRNA expression data, and the method can be used as an alternative analytical tool for identifying a list of target miRNAs for further biological and clinical investigation.

Keywords: microRNA; receiver operating characteristic curve; differential expression; false discovery rate (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/sagmb-2013-0035

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