A Beginner’s Guide to Microarrays

31.07.2003

Englisch

9781402074721

Springer US

Nein

347

1

Gebundene Ausgabe

DNA, Nucleotide, RNA, clustering, data analysis, database, gene expression, genes, performance, Tree Biology

PSB - Biochemie PSAJ - Evolution PSA - Biowissenschaften, allgemein UMB - Algorithmen und Datenstrukturen GPF - Informationstheorie PSAF - Ökologie, Biosphäre TVR - Forstwirtschaft und Waldbau: Praxis und Techniken

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.

235 mm

15.5 cm