Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays

Background: Several aspects of microarray data analysis are dependent on identification of genes expressed at or near the limits of detection. For example, regression-based normalization methods rely on the premise that most genes in compared samples are expressed at similar levels and therefore require accurate identification of nonexpressed genes (additive noise) so that they can be excluded from the normalization procedure. Moreover, key regulatory genes can maintain stringent control of a given response at low expression levels. If arbitrary cutoffs are used for distinguishing expressed from nonexpressed genes, some of these key regulatory genes may be unnecessarily excluded from the analysis. Unfortunately, no accurate method for differentiating additive noise from genes expressed at low levels is currently available. Results: We developed a multistep procedure for analysis of mRNA expression data that robustly identifies the additive noise in a microarray experiment. This analys...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Igor Dozmorov, Nicholas Knowlton, Yuhong Tang, Michael Centola
• Keywords: Additive Noise, Analysis, BMCBI 2004, Key Regulatory Genes