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Products - Books & Software - A Beginner’s Guide to Microarrays for Researchers and Core Facility Managers Including a Foundational Chapter by Arrayit Founder Todd Martinsky
edited by Eric M. Blalock
University of Kentucky Medical Center, Lexington, USA
Microarray technology is more accessible than ever, and an ever-widening field of scientists is using this technology. However, the manufacture, experimental design, and analysis of microarrays are not always straightforward, and researchers new to the field run into technical and theoretical roadblocks that can hinder progress with this powerful new technology.
A Beginner's Guide to Microarrays addresses two audiences - the core facility manager who produces, hybridizes, and scans arrays, and the basic research scientist who will be performing the analysis and interpreting the results. User friendly coverage and detailed protocols are provided for the technical steps and procedures involved in many facets of microarray technology, including:
This book is addressed to researchers using microarrays for the first time. One faces a myriad of problems at the outset of such a task, and there is no need to 'reinvent the wheel' for each scientist that runs into these problems. Knowing the strengths and weaknesses of microarrays before research begins can save time, money, and resources
1: Slide Coating And DNA Immobilization Chemistries; K. Aboytes, J. Humphreys, S. Reis, B. Ward. Introduction. Glass Properties. Glass Cleaning. Slide Coating Chemistries. DNA Immobilization Chemistries. Surface Analysis Methods. Summary. References.
2: Diagnostic Oligonucleotide Microarrays For Microbiology; L. Bodrossy. Introduction. 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. WWW Sites Related To Microarrays. AS Biosensors. Reference List.
3: Printing Technologies And Microarray Manufacturing Techniques: Making The Perfect Microarray; T. Martinsky. Introduction. Microarray Manufacturing. Comparing Printing Technologies. Conclusions. Acknowledgments. Reference List.
4: Arrays For The Masses - Setting Up A Microarray Core Facility; R.P. Searles. Preface. Introduction. 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: Microarray Data Normalization: The Art And Science Of Overcoming Technical Variance To Maximize The Detection Of Biologic Variance; M.A. Sartor, M. Medvedovic, B.J. Aronow. 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; E. Blalock. Introduction. Measuring RNA. Fold Change Significance. Variation. Experimental Design. Variance And Fold-Change. Affymetrix Data. Working With More Than Two Groups. Functional Grouping. Using Excel. Acknowledgements. References.
7: Microarray Experiment Design And Statistical Analysis; Xuejun Peng, A.J. Stromberg. Introduction. Designing A Microarray 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; W. Valdivia Granda. Introduction. 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.
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