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Journal of Translational Medicine

, 8:69

First Online: 21 July 2010Received: 17 March 2010Accepted: 21 July 2010DOI: 10.1186-1479-5876-8-69

Cite this article as: Zhao, Y., Wang, E., Liu, H. et al. J Transl Med 2010 8: 69. doi:10.1186-1479-5876-8-69


BackgroundMiR arrays distinguish themselves from gene expression arrays by their more limited number of probes, and the shorter and less flexible sequence in probe design. Robust data processing and analysis methods tailored to the unique characteristics of miR arrays are greatly needed. Assumptions underlying commonly used normalization methods for gene expression microarrays containing tens of thousands or more probes may not hold for miR microarrays. Findings from previous studies have sometimes been inconclusive or contradictory. Further studies to determine optimal normalization methods for miR microarrays are needed.

MethodsWe evaluated many different normalization methods for data generated with a custom-made two channel miR microarray using two data sets that have technical replicates from several different cell lines. The impact of each normalization method was examined on both within miR error variance between replicate arrays and between miR variance to determine which normalization methods minimized differences between replicate samples while preserving differences between biologically distinct miRs.

ResultsLowess normalization generally did not perform as well as the other methods, and quantile normalization based on an invariant set showed the best performance in many cases unless restricted to a very small invariant set. Global median and global mean methods performed reasonably well in both data sets and have the advantage of computational simplicity.

ConclusionsResearchers need to consider carefully which assumptions underlying the different normalization methods appear most reasonable for their experimental setting and possibly consider more than one normalization approach to determine the sensitivity of their results to normalization method used.

Electronic supplementary materialThe online version of this article doi:10.1186-1479-5876-8-69 contains supplementary material, which is available to authorized users.

Yingdong Zhao, Ena Wang contributed equally to this work.

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Autor: Yingdong Zhao - Ena Wang - Hui Liu - Melissa Rotunno - Jill Koshiol - Francesco M Marincola - Maria Teresa Landi - Lisa 

Fuente: https://link.springer.com/

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