More and more, array platforms are being used to assess gene expression in a wide range of biological and clinical models. Technologies using arrays have proven to be reliable and affordable for most of the scientific community worldwide. By typing microarrays or proteomics into a search engine such as PubMed, thousands of references can be viewed. Nevertheless, almost everyone in life science research has a story to tell about array experiments that were expensive, did not generate reproducible data, or generated meaningless data. Because considerable resources are required for any experiment using arrays, it is desirable to evaluate the best method and the best design to ask a certain question. Multiple levels of technical problems, such as sample preparation, array spotting, signal acquisition, dye intensity bias, normalization, or sample-contamination, can generate inconsistent results or misleading conclusions. Technical recommendations that offer alternatives and solutions for the most common problems have been discussed extensively in previous work. Less often discussed is the experimental design. A poor design can make array data analysis difficult, even if there are no technical problems. This chapter focuses on experimental design choices in terms of controls such as replicates and comparisons for microarray and proteomics. It also covers data validation and provides examples of studies using diverse experimental designs. The overall emphasis is on design efficiency. Though perhaps obvious, we also emphasize that design choices should be made so that biological questions are answered by clear data analysis.
Experimental Design for Gene Expression Analysis: Answers Are Easy, Is Asking the Right Question Difficult?