Optimal consistency in microRNA expression analysis using reference-gene-based normalization.

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.

journal_name

Mol Biosyst

journal_title

Molecular bioSystems

authors

Wang X,Gardiner EJ,Cairns MJ

doi

10.1039/c4mb00711e

subject

Has Abstract

pub_date

2015-05-01 00:00:00

pages

1235-40

issue

5

eissn

1742-206X

issn

1742-2051

journal_volume

11

pub_type

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