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Issue 5, 2015
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Optimal consistency in microRNA expression analysis using reference-gene-based normalization

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Abstract

Normalization of high-throughput molecular expression profiles secures differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches. While the same general principles apply to microRNA (miRNA) normalization, there is mounting evidence that global shifts in their expression patterns occur in specific circumstances, which pose a challenge for normalizing miRNA expression data. As an alternative to global normalization, which has the propensity to flatten large trends, normalization against constitutively expressed reference genes presents an advantage through their relative independence. Here we investigated the performance of reference-gene-based (RGB) normalization for differential miRNA expression analysis of microarray expression data, and compared the results with other normalization methods, including: quantile, variance stabilization, robust spline, simple scaling, rank invariant, and Loess regression. The comparative analyses were executed using miRNA expression in tissue samples derived from subjects with schizophrenia and non-psychiatric controls. We proposed a consistency criterion for evaluating methods by examining the overlapping of differentially expressed miRNAs detected using different partitions of the whole data. Based on this criterion, we found that RGB normalization generally outperformed global normalization methods. Thus we recommend the application of RGB normalization for miRNA expression data sets, and believe that this will yield a more consistent and useful readout of differentially expressed miRNAs, particularly in biological conditions characterized by large shifts in miRNA expression.

Graphical abstract: Optimal consistency in microRNA expression analysis using reference-gene-based normalization

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Publication details

The article was received on 13 Dec 2014, accepted on 16 Mar 2015 and first published on 16 Mar 2015


Article type: Method
DOI: 10.1039/C4MB00711E
Citation: Mol. BioSyst., 2015,11, 1235-1240
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    Optimal consistency in microRNA expression analysis using reference-gene-based normalization

    X. Wang, E. J. Gardiner and M. J. Cairns, Mol. BioSyst., 2015, 11, 1235
    DOI: 10.1039/C4MB00711E

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