A Beginner’s Guide to Microarrays

1 Slide Coating And DNA immobilization Chemistries.- Glass Properties.- Glass Cleaning.- Slide Coating Chemistries.- Dna Immobilization Chemistries.- Surface Analysis Methods.- Summary.- References.- 2 Diagnostic Oligonucleotide Micro Arrays For Microbiology.- Scheme of The Experimental Approach.- Sources of Variation.- Establishment of a Sequence Database.- Oligo Length And Melting Temperature (Tm); Designing Oligo Sets Tuned To Work Together.- Oligo Set Design.- Choice Of Oligo/Surface Binding Chemistry.- Array Printing.- Target Preparation.- Hybridisation.- Scanning.- Data Analysis.- Applications In Microbial Identification.- Acknowledgements.- Links Related To Dlagnostic Microbial Microarrays.- Reference List.- 3 Printing Technologies And Microarray Manufacturing Techniques: Making The Perfect Microarray.- Microarray Manufacturing.- Comparing Printing Technologies.- Conclusions.- Acknowledgments.- Reference List.- 4 Arrays For The Masses — Setting Up a Microarray Core Facility.- Preface.- Hedco/Oregon Cancer Institute Spotted Microarray Core at Ohsu.- Core Set-Up.- Array Printing.- The Printer.- Slides.- Amplification.- Printing The Array.- Hybridization.- Scanner.- Other Equipment.- Conclusion.- References.- 5 Microarraydatanormalization: The Art And Science Of Overcoming Technical Variance To Maximize The Detection Of Biologic Variance.- Normalization: Correcting For Technical Variance In Order To Study Biological Variation.- Single Channel Data Normalizations.- Normalizations Of Two-Channel Data.- The Role Of Experimental Design In The Removal Of Technical Variance.- Gene-Specific Normalizations And Clustering.- References.- 6 Experimental Design And Data Analysis.- Measuring RNA.- Fold Change Significance.- Variance.- Experimental Design.- Variance andFold-Change.- Affymetrix Data.- Working With More Than Two Groups.- Functional Grouping.- Using Excel.- Acknowledgements.- References.- 7 Microarray Experiment Design And Statistic Analysis.- Designing Amicroarray Experiment.- General Procedures For Statistical Analysis Of Microarray Data.- Multiple Hypothesis Testing In Microarray Experiments.- Methods Based On P Value Adjustment.- Analysis Of Variance.- Summary Of The Chapter.- Some Useful Online Sources For Microarray Analysis.- References.- 8 Strategies For Clustering, Classifying Integrating, Standardizing And Visualizing Microarray Gene Expression Data.- Microarray Gene Expression Matrix.- Distance Functions.- Unsupervised Analysis And Clustering Of Microarray Data.- Methods For Validating Unsupervised Analysis.- Supervised Microarray Data Analysis.- Nearest Neighbors.- Support Vector Machines.- Methods To Improve Classifier Performance.- Genetic and Biochemical Networks.- Additional Methods For Microarray Data Analysis.- Microarray Data Visualization.- Microarray Data Standardization And Integration.- Microarray Gene Expression Markup Language (MAGE-ML).- Microarray Data Repositories.- Challenges In Microarray Gene Expression Data Analysis.- Conclusions.- References.