Novel methods and strategies for microarray data analysis

Abstract

Microarray technology has been used as a routine high-throughput tool in biological research to characterize gene expression, and overwhelming volumes of data are generated in every microarray experiment as a consequence. However, there are many kinds of non-biological variations and systematic biases in microarray data which can confound the extraction of the true signals of gene expression. Thus comprehensive bioinformatic and statistical analyses are crucially required, typically including normalization, regulated gene identification, clustering and meta-analysis. The main purpose of my study is to develop robust analytical methods and programs for spotted cDNA-type microarray data. First, I established a novel normalization method based on the Generalized Procrustes Analysis (GPA) algorithm. I compared the GPA-based method with six other popular normalization methods, including Global, Lowess, Scale, Quantile, Variance Stabilization Normalization, and one boutique array-specific housekeeping gene method by using several different empirical criteria, and demonstrated that the GPA-based method was consistently better in reducing across-slide variability and removing systematic bias. In particular, being free from the biological assumptions that most genes (95%) are not differentially expressed on the array, the GPA method is therefore more robust, and appropriate for diverse types of array sets, including the boutique array where the majority of genes may be differentially expressed. Second, I utilized statistical analysis to assess the quality of a novel goldfish brain cDNA microarray, which provides statistical validation of microarray data result. Thirdly, I developed a new program suite as a user-friendly analytical pipeline integrating most popular analytical methods for microarray data analysis. Finally, I proposed a novel analytical strategy to extract season-related gene expression information from multiple microarray data sets by using comprehensive data transformation and normalization analysis, differential gene identification, and multivariate analysis.