normalization of qpcr array data: a novel method based on procrustes superimposition

micrornas (mirnas) are short, endogenous non-coding rnas that function as guide molecules to regulate transcription of their target messenger rnas. several methods including low-density qpcr arrays are being increasingly used to profile the expression of these molecules in a variety of different biological conditions. reliable analysis of expression profiles demands removal of technical variations in data, which is achieved via applying normalization techniques. most normalization techniques have been developed for mrna microarrays and new and modified methods should be used or mirna studies in general and rt-qpcr mirna arrays in particular, because of low number of mirnas. here, we introduce a new method based on procrustes superimposition of arrays to be normalized on a reference array. to assess the performance of our normalization method, we compared this method to the common mirna normalization methods. removal of technical variation was assessed by robust modeling of mean square error (mse) in different subsets of real mirna datasets before and after applying normalization. we show that our method outperforms the other normalization methods in concurrent reduction of technical variation and retention of biological variability.