Use of normalization methods for analysis of microarrays containing a high degree of gene effects

Background: High-throughput microarrays are widely used to study gene expression across tissues and developmental stages. Analysis of gene expression data is challenging in these experiments due to the presence of significant percentages of differentially expressed genes (DEG) observed between tissues and developmental stages. Data normalization methods that are widely used today are not designed for data with a large proportion of tissue or gene effects. Results: In our current study, we describe a novel two-dimensional nonparametric normalization method for analyzing microarray data which functions well in the absence or presence of large numbers of gene effects. Rather than relying on an assumption of low variability among most genes, the method implements a unique peak selection strategy to distinguish DEG from genes that are invariant in expression, prior to nonlinear curve fitting. We compared the method under simulated and experimental conditions with five alternative nonlinear...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Terri T. Ni, William J. Lemon, Yu Shyr, Tao P. Zhong
• Keywords: BMCBI 2008, Gene, Gene Effects, Gene Expression