MC-normalization: a novel method for dye-normalization of two-channel microarray data
2009 (English)In: Statistical Applications in Genetics and Molecular Biology, ISSN 1544-6115, Vol. 8, no 1, 42- p.Article in journal (Refereed) Published
Motivation: Pre-processing plays a vital role in two-color microarray data analysis. An analysis is characterized by its ability to identify differentially expressed genes (its sensitivity) and its ability to provide unbiased estimators of the true regulation (its bias). It has been shown that microarray experiments regularly underestimate the true regulation of differentially expressed genes. We introduce the MC-normalization, where C stands for channel-wise normalization, with considerably lower bias than the commonly used standard methods.
Methods: The idea behind the MC-normalization is that the channels’ individual intensities determine the correction, rather than the average intensity which is the case for the widely used MA-normalization. The two methods were evaluated using spike-in data from an in-house produced cDNA-experiment and a public available Agilent-experiment. The methods were applied on background corrected and non-background corrected data. For the cDNA-experiment the methods were either applied separately on data from each of the print-tips or applied on the complete array data. Altogether 24 analyses were evaluated. For each analysis the sensitivity, the bias and two variance measures were estimated.
Results: We prove that the MC-normalization has lower bias than the MA-normalization. The spike-in data confirmed the theoretical result and suggest that the difference is significant. Furthermore, the empirical data suggest that the MC-and MA-normalization have similar sensitivity. A striking result is that print-tip normalizations did have considerably higher sensitivity than analyses using the complete array data.
Place, publisher, year, edition, pages
Berkeley: The Berkeley Electronic Press (bepress) , 2009. Vol. 8, no 1, 42- p.
microarray analysis, dye-normalization, background correction, gene expression, spike-in data, agilent
Bioinformatics (Computational Biology)
Research subject Mathematical Statistics; Statistics
IdentifiersURN: urn:nbn:se:umu:diva-26566DOI: 10.2202/1544-6115.1459OAI: oai:DiVA.org:umu-26566DiVA: diva2:272575