xxXXX Selection and Normalization Sample Clauses

xxXXX Selection and Normalization. The Affymetrix platform contained information on 7815 miRNAs, of which 847 (10.83%) were human, whereas Agilent platform content was of 961 miRNAs (851 human, 88.55%), and on the Illumina array 1145 miRNAs were detected, of which 858 (74.93%) were human miRNAs. Human miRNAs common to all platforms were selected according to their name and confirmed by a search on miRBase (Release 18, November 2011). Unlike other published works, miRNAs were not filtered on a detection basis, because such an approach could possibly introduce a bias in the results. In fact, some of the miRNAs that are filtered out because they are “switched-off” could show patterns of within- and/or between-platform disagreement in another experiment, where they are “turned-on”. This could possibly lead to an over-estimate of the level of reliability. Considering only human miRNAs should circumvent this issue and, at the same time, provide relevant information, since human miRNAs are commonly those that are of major interest in biomolecular investigation. Moreover, no data normalization was performed. Almost all works that focused on comparing microarray platforms normalized their data (for instance, [15,16,24]), but this is a non-trivial issue that has to be carefully evaluated. As a matter of fact, to date, normalization for xxXXX microarray has been largely debated, with results that have been somehow discordant [25–29], so that no “gold-standard” methods exists. Additionally, normalizing data in the context of assessing platform agreement poses other relevant problems. If data on two different platforms are normalized and then compared, then there is no way to discriminate between platform and normalization on the results of concordance/agreement/reproducibility assessment. A high level of between-platforms agreement, due not to the platforms themselves, but to the normalization used, might be found. On the other hand, the same normalization on different platforms could highlight patterns of discordance that cannot be ascribed to the platforms. Nonetheless, comparing un-normalized data exposes the risk of finding poor concordance, because of incidental batch effects occurring in the experiment, which may lead to an underestimate of the “true” agreement between platforms. In this paper, we have chosen to use non normalized data, so that we could assess the performance of different platforms “per se”. For the sake of comparison, data were also normalized with the quantile and loess algorit...