With contributions from distinguished experts in the field, DNA Microarrays and Related Genomics Techniques parallels the steps in conducting and analyzing a microarray experiment from conception to interpretation. This book explores critical aspects of designing a microarray experiment, including power and sample size. It assesses the validity of statistical methods and describes how to ensure the quality and integrity of data. In addition, the text discusses new methods for interpreting microarray data and examines the various issues associated with analysis. It also addresses the movement to make large high-dimensional datasets public for further analysis, and the associated methods.

Microarray Platforms and Blood Samples. Normalization of Microarray Data. Microarray Quality Control and Assessment. Epistemological Foundations of Statistical Methods for High-Dimensional Biology. The Role of Sample Size on Measures of Uncertainty and Power. Pooling Biological Samples in Microarray Experiments. Designing Microarrays for the Analysis of Gene Expressions. Overview of Standard Clustering Approaches for Gene Microarray Data Analysis. Cluster Stability. Dimensionality Reduction and Discrimination. Modeling Affymetrix Data at the Probe Level. Parametric Linear Models. The Use of Nonparametric Procedures in the Statistical Analysis of Microarray Data. Bayesian Analysis of Microarray Data. False Discovery Rate and Multiple Comparison Procedures. Using Standards to Facilitate Interoperation of Heterogeneous Microarray Databases and Analytic Tools. Post-Analysis Interpretation: What Do I Do with This Gene List? Combining High Dimensional Biological Data to Study Complex Diseases and Quantitative Traits.