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:
-Cleaning and coating glass slides,
-Designing oligonucleotide probes,
-Constructing arrays for the detection and quantification of different bacterial species,
-Preparing spotting solutions,
-Troubleshooting spotting problems,
-Setting up and running a core facility,
-Normalizing background signal and controlling for systematic variance,
-Designing experiments for maximum effect,
-Analyzing data with statistical procedures,
-Clustering data with machine-learning protocols.
Rezensionen / Stimmen
From the reviews:
"The book offers a broad coverage of many of the most important aspects of microarray technology . . Despite the wide field broached by this book, one of its chief successes lies with the fact that in each chapter the authors provide a clear review of every step of the technique, including both its potential and pitfalls. This allows all readers, especially beginners, to understand the development and impact of this important technique. . Readers will find this beginner's guide book very informative . ." (Guillermina Lopez-Bendito, Journal of Chemical Neuroanatomy, Vol. 30, 2005)
Auflage
Softcover reprint of the original 1st ed. 2003
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 20 mm
Gewicht
ISBN-13
978-1-4613-4684-5 (9781461346845)
DOI
10.1007/978-1-4419-8760-0
Schweitzer Klassifikation
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.
