Comparison of normalization methods with MicroRNA microarray

Bioinformatics Center, The Center of Functional Genomics, Key Lab of System Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.
Genomics (Impact Factor: 2.28). 09/2008; 92(2):122-8. DOI: 10.1016/j.ygeno.2008.04.002
Source: PubMed


MicroRNAs (miRNAs) are a group of RNAs that play important roles in regulating gene expression and protein translation. In a previous study, we established an oligonucleotide microarray platform to detect miRNA expression. Because it contained only hundreds of probes, data normalization was difficult. In this study, the microarray data for eight miRNAs extracted from inflamed rat dorsal root ganglion (DRG) tissue were normalized using 15 methods and compared with the results of real-time polymerase chain reaction. It was found that the miRNA microarray data normalized by the print-tip loess method were the most consistent with results from real-time polymerase chain reaction. Moreover, the same pattern was also observed in 14 different types of rat tissue. This study compares a variety of normalization methods and will be helpful in the preprocessing of miRNA microarray data.

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    • "Recently, several studies compared the performance of mRNA normalization methods on miRNA microarray data [8,15-17]. Rao et al. [8] and Zhao et al. [16] demonstrated that quantile normalization [18] ( outperformed the other normalization methods they evaluated. In their studies, the primary objective was to compare the effect of normalization methods in reducing the variation among technical replicates. "
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    ABSTRACT: MicroRNAs (miRNAs) are small endogenous ssRNAs that regulate target gene expression post-transcriptionally through the RNAi pathway. A critical pre-processing procedure for detecting differentially expressed miRNAs is normalization, aiming at removing the between-array systematic bias. Most normalization methods adopted for miRNA data are the same methods used to normalize mRNA data; but miRNA data are very different from mRNA data mainly because of possibly larger proportion of differentially expressed miRNA probes, and much larger percentage of left-censored miRNA probes below detection limit (DL). Taking the unique characteristics of miRNA data into account, we present a hierarchical Bayesian approach that integrates normalization, missing data imputation, and feature selection in the same model. Results from both simulation and real data seem to suggest the superiority of performance of Bayesian method over other widely used normalization methods in detecting truly differentially expressed miRNAs. In addition, our findings clearly demonstrate the necessity of miRNA data normalization, and the robustness of our Bayesian approach against the violation of standard assumptions adopted in mRNA normalization methods. Our study indicates that normalization procedures can have a profound impact on the detection of truly differentially expressed miRNAs. Although the proposed Bayesian method was formulated to handle normalization issues in miRNA data, we expect that biomarker discovery with other high-dimensional profiling techniques where there are a significant proportion of left-censored data points (e.g., proteomics) might also benefit from this approach.
    BMC Genomics 07/2013; 14(1):507. DOI:10.1186/1471-2164-14-507 · 3.99 Impact Factor
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    • "As with whole-genome microarrays, miRNA microarray analyses can be strongly biased by hybridization, labeling, or batch-to-batch variations. Recent reports suggest that background correction and normalization procedures are beneficial for the identification of differentially regulated miRNAs (Hua et al. 2008; Rao et al. 2008; Pradervand et al. 2009; Risso et al. 2009; Meyer et al. 2010, 2012). However, all normalization procedures do not equate, and Risso et al. recently demonstrated that the choice of normalization procedure used could strongly impact on the overall identification of miRNAs as up-or down-regulated (Risso et al. 2009). "
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    ABSTRACT: Recent studies have established that mutations or deletions in microRNA (miRNA) processing enzymes resulting in a global decrease of miRNA expression are frequent across cancers and can be associated with a poorer prognosis. While very popular in miRNA profiling studies, it remains unclear whether miRNA microarrays are suited or not to accurately detecting global miRNA decreases seen in cancers. In this work, we analyzed the miRNA profiles of samples with global miRNA decreases using Affymetrix miRNA microarrays following the inducible genetic deletion of Dicer1. Surprisingly, up to a third of deregulated miRNAs identified upon Dicer1 depletion were found to be up-regulated following standard robust multichip average (RMA) background correction and quantile normalization, indicative of normalization bias. Our comparisons of five preprocess steps performed at the probe level demonstrated that the use of cyclic loess relying on non-miRNA small RNAs present on the Affymetrix platform significantly improved specificity and sensitivity of detection of decreased miRNAs. These findings were validated in samples from patients with prostate cancer, where conjugation of robust normal-exponential background correction with cyclic loess normalization and array weights correctly identified the greatest number of decreased miRNAs, and the lowest amount of false-positive up-regulated miRNAs. These findings highlight the importance of miRNA microarray normalization for the detection of miRNAs that are truly differentially expressed and suggest that the use of cyclic loess based on non-miRNA small RNAs can help to improve the sensitivity and specificity of miRNA profiling in cancer samples with global miRNA decrease.
    RNA 05/2013; 19(7). DOI:10.1261/rna.035055.112 · 4.94 Impact Factor
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    • "Local regression via LOESS uses a quadratic polynomial weighted regression function with Tukey's biweight function (reviewed in Steinhoff and Vingron 2006) of the log ratios Cy3/Cy5 on overall spot intensity Cy3*Cy5 (the LOESS smoother for the so called MA-plots) (Risso et al. 2009). Hua et al. (2008) demonstrated that for twochannel technology print-tip LOESS performed most consistent of all the 15 normalization methods which they compared illustrating that miRNA microarrays manufactured by print technology have, in addition, systematic spatial bias with respect to each block. Print-tip LOESS normalizes each M value by subtracting the corresponding value on the tip-group LOESS curve from the raw data (Hua et al. 2008). "

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