Experimental Design and Analysis of Microarray Data

The advent of microarray technology has significantly changed the way we can quantitatively measure and observe gene expression at the mRNA level within a given biological sample of interest, allowing for the monitoring of tens to hundreds of thousands of genes within a single experiment. The two main array platforms are spotted two-colour arrays and one-colour in situ-synthesized arrays. Microarrays are used for a wide range of applications including gene annotation, investigation of gene-gene interactions, elucidation of gene regulatory networks and gene-expression profiling of Saccharomyces cerevisiae and other fungal organisms. Academic researchers and both the pharmaceutical and agricultural industries have an enormous interest in developing microarrays both as diagnostic tools and for use in basic research into how pathogens, such as fungi, interact with their host. Microarray experiments generate vast quantities of raw gene expression data, therefore good experimental design and statistical analysis is required for the extraction of accurate and useful information regarding the expression of genes. In this review we firstly provide an overview of the arrival and development of microarray technology. We then focus on the issues surrounding experimental design and the processing of microarray images, followed by a discussion on methods for cleaning and normalizing raw gene expression data and a final discussion of the importance statistical analysis plays in identifying differentially expressed genes.