Journal Article

A modified LOESS normalization applied to microRNA arrays: a comparative evaluation

Davide Risso, Maria Sofia Massa, Monica Chiogna and Chiara Romualdi

in Bioinformatics

Volume 25, issue 20, pages 2685-2691
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI:
A modified LOESS normalization applied to microRNA arrays: a comparative evaluation

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Motivation: Microarray normalization is a fundamental step in removing systematic bias and noise variability caused by technical and experimental artefacts. Several approaches, suitable for large-scale genome arrays, have been proposed and shown to be effective in the reduction of systematic errors. Most of these methodologies are based on specific assumptions that are reasonable for whole-genome arrays, but possibly unsuitable for small microRNA (miRNA) platforms. In this work, we propose a novel normalization (loessM), and we investigate, through simulated and real datasets, the influence that normalizations for two-colour miRNA arrays have on the identification of differentially expressed genes.

Results: We show that normalizations usually applied to large-scale arrays, in several cases, modify the actual structure of miRNA data, leading to large portions of false positives and false negatives. Nevertheless, loessM is able to outperform other techniques in most experimental scenarios. Moreover, when usual assumptions on differential expression distribution are missed, channel effect has a strikingly negative influence on small arrays, bias that cannot be removed by normalizations but rather by an appropriate experimental design. We find that the combination of loessM with eCADS, an experimental design based on biological replicates dye-swap recently proposed for channel-effect reduction, gives better results in most of the experimental conditions in terms of specificity/sensitivity both on simulated and real data.

Availability: LoessM R function is freely available at


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5422 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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