Impact of different normalization strategies on miRNA profiling experiments

Swanhild Meyer1, Sebastian Kaiser2, Carola Wagner3, Christian Thirion4, Michael Pfaffl1
1Physiology Weihenstephan, Technische Universität München, Germany; 2Department of Statistics, Ludwig-Maximilians-Universität München, Germany; 3IMGM Laboratories GmbH, Martinsried, Germany; 4Friedrich-Baur-Institute and Department of Neurology, Ludwig-Maximilians-Universität München, Germany

Selection of the normalization strategy has significant impact on the detection of differentially expressed microRNAs (miRNAs) from profiling experiments. Normalization techniques currently in use for miRNA profiling analyses are in analogy to mRNA data processing or are specifically modified and developed for miRNA data. Studies evaluating the impact of normalization on miRNA profiling experiments have focused on different profiling platforms or the comparison of normalization techniques within one platform. Here, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of a one-colour hybridization-based platform (AGL array) and a multiplex RT-qPCR platform (TLDA) and validated results by singleplex RT-qPCR assays.

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