Estimation of Weighted Log Partial Area Under the ROC Curve and Its Application to Micro-RNA Data

• Activity Number: 35
• Type: Contributed
• Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ahmed Hossain*+ and Joseph Beyene
• Companies: McMaster University and McMaster University
• Keywords: Micro RNA ; Receiver Operating Characteristic Curve ; Agilent miRNA expression ; False Discovery Rate

Abstract

Micro RNAs (miRNAs) are short non-coding RNAs that play critical roles in numerous cellular processes through post-transcriptional regulating 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 noise and variance vary dramatically across the different miRNAs expressions. We propose a flexible rank-based procedure for estimating weighted log partial area under the receiver operating characteristic curve (WLpAUC) and we apply the method to generate a list of significant targets and assess differential miRNA expressions. 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 relative performance of different statistical miRNA ranking measures. We conclude that the proposed method offers robust results with large samples for microRNA 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.