The Statistical Design and Interpretation of Microarray Experiments
This chapter reviews major issues related to design and interpretation of microarray experiments. Important aspects of design covered include identification of experimental objectives, treatment of batch effects, selection of replication and pooling levels, determining sample size for class comparison and class prediction, and optimal allocation of samples to arrays and labels in dual label experiments. Aspects of interpretation focus on class prediction issues, including case selection, external vs. internal validation, and pitfalls in cross-validation.
KeywordsMicroarrays Experimental design Sample size Prediction Validation
- Simon RM, Dobbin K. Experimental design of DNA microarray experiments. Biotechniques 2003;34:516–21Google Scholar