Modern DNA microarray technologies have evolved over the past 25 years to the point where it is now possible to take many million measurements from a single experiment. These two volumes, Parts A & B in the Methods in Enzymology series provide methods that will shepard any molecular biologist through the process of planning, performing, and publishing microarray results.
Part A starts with an overview of a number of microarray platforms, both commercial and academically produced and includes wet bench protocols for performing traditional expression analysis and derivative techniques such as detection of transcription factor occupancy and chromatin status. Wet-bench protocols and troubleshooting techniques continue into Part B. These techniques are well rooted in traditional molecular biology and while they require traditional care, a researcher that can reproducibly generate beautiful Northern or Southern blots should have no difficulty generating beautiful array hybridizations.
Data management is a more recent problem for most biologists. The bulk of Part B provides a range of techniques for data handling. This includes critical issues, from normalization within and between arrays, to uploading your results to the public repositories for array data, and how to integrate data from multiple sources. There are chapters in Part B for both the debutant and the expert bioinformatician.
Section I. Databases and statistics. Chapter 1: The Use of External Controls in Microarray Experiments. Chapter 2: Standards in gene expression microarray experiments. Chapter 3: Scanning Microarrays: Current Methods and Future Directions. Chapter 4: BioArray software environment. Chapter 5: Bioconductor: An open source framework for bioinformatics and computational biology. Chapter 6: TM4 microarray software suite. Chaper 7: Clustering microarray data. Chapter 8: Analysis of variance of microarray data. Chapter 9: Microarray quality control. Chapter 10: Principle component and ANOVA analysis of array data using Partek (R) Genomics SolutionTM. Chapter 11: Statistics for ChIP-chip and DNase hypersensitivity experiments on NimbleGen arrays. Chapter 12: Extrapolating traditional DNA microarray statistics to the tiling and protein microarray technologies. Chapter 13: Random dataset generation to support microarray analysis. Chapter 14: Using ontologies to annotate microarray experiments Chapter 15: Interpreting experimental results using gene ontologies. Chapter 16: Gene Expression Omnibus (GEO): Microarray data storage, submission, retrieval and analysis. Chapter 17: Data storage and analysis in ArrayExpress. Chapter 18: Clustering methods for analyzing large datasets: gonad development, a study case. Chapter 19: GeneSprings. Chapter 20: Visualizing networks. Chapter 21: Random forests for array data. Chapter 22: Hybridization troubleshooting. Chapter 23: RNA extraction and handling for microarray analysis. Chapter 24: Analyzing MicroRNA expression using microarrays.