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


BACKGROUND
LoessM R function is freely available at http://gefu.cribi.unipd.it/papers/miRNA-simulation/
Risso
Davide
D
Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy.
Massa
Maria Sofia
MS
Chiogna
Monica
M
Romualdi
Chiara
C
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D035683:MicroRNAs / Q000737:chemistry*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
D035683:MicroRNAs