An Adaptive Method for the Normalization of MicroRNA Array Data

• Activity Number: 372
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Qing Zhao* and Yuda Zhu and Karin Staflin
• Companies: and Genentech, Inc. and Genentech, Inc.
• Keywords: adaptive ; normalization ; microRNA profiling ; housekeeping genes

Abstract

MicroRNAs (miRNAs) are a class of short, non-coding RNAs that play important regulatory roles in many cellular processes. However, there is currently no consensus in optimal normalization strategy for RT-qPCR miRNA quantification. Despite the fact, rigorous normalization of miRNA data may be more critical since relatively small changes in miRNA expression may be biologically and clinically significant. We propose MiRA-norm (MiR-Adaptive normalization method) to construct an adaptive set of housekeeping miRNAs that are stable in the expression levels and capture the systematic pattern induced by sample preparation and other technical artifacts. We explore the expression patterns among miRNAs with hierarchical clustering and adaptive tree cut and design an iterative algorithm to evaluate the selected miRNAs. We show our proposed method gives a better estimate of the expression levels of differentially expressed miRNAs with low false discovery rate using both simulated data and two different miRNA studies. The new MiRA-norm method is also applicable for custom panels where the majority of the miRNAs is likely to be differentially expressed.