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Normalizing Bead-Based MicroRNA Expression Data: A Measurement Error Model-Based Approach
Bioinformatics (2011)
  • Bin Wang, University of South Alabama
  • Xiao-Feng Wang, Cleveland Clinic Lerner Research Institute
  • Yaguang Xi, University of South Alabama
Motivation: Compared with complementary DNA (cDNA) or messenger RNA (mRNA) microarray data, microRNA (miRNA) microarray data are harder to normalize due to the facts that the total number of miRNAs is small, and that the majority of miRNAs usually have low expression levels. In bead-based microarrays, the hybridization is completed in several pools. As a result, the number of miRNAs tested in each pool is even smaller, which poses extra difficulty to intrasample normalization and ultimately affects the quality of the final profiles assembled from various pools. In this article, we consider a measurement error model-based method for bead-based microarray intrasample normalization. Results: In this study, results from quantitative real-time PCR (qRT-PCR) assays are used as ‘gold standards’ for validation. The performance of the proposed measurement error model-based method is evaluated via a simulation study and real bead-based miRNA expression data. Simulation results show that the new method performs well to assemble complete profiles from subprofiles from various pools. Compared with two intrasample normalization methods recommended by the manufacturer, the proposed approach produces more robust final complete profiles and results in better agreement with the qRT-PCR results in identifying differentially expressed miRNAs, and hence improves the reproducibility between the two microarray platforms. Meaningful results are obtained by the proposed intrasample normalization method, together with quantile normalization as a subsequent complemental intersample normalization method.
  • measurement error models; Normalization; microRNA
Publication Date
June, 2011
Citation Information
Bin Wang, Xiao-Feng Wang and Yaguang Xi. "Normalizing Bead-Based MicroRNA Expression Data: A Measurement Error Model-Based Approach" Bioinformatics Vol. 27 Iss. 11 (2011)
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