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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Available from: Hua-Sheng Xiao
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    • "Different normalization approaches have been extensively studied for data generated from various high-throughput platforms, e.g., gene expression arrays, miRNA arrays, protein microarrays, and RNA-sequencing experiments. For each highthroughput platform, comparisons have been made between the different normalization methods, such as global normalization, Lowess normalization, quantile normalization or conditional quantile normalization, variance stabilizing normalization, Z-Score normalization and robust linear model normalization231232233234235236237238239240241. For epigenomic data such as the epigenetic regulation of immune cells, data preprocessing and normalization includes inverse normal transformation or Z-score normalization. "
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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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    • "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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