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Informationen zum Autor Ernst Wit is the author of Statistics for Microarrays: Design, Analysis and Inference , published by Wiley. John McClure is the author of Statistics for Microarrays: Design, Analysis and Inference , published by Wiley. Klappentext Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data - from getting good data to obtaining meaningful results.* Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference.* Features many examples throughout using real data from microarray experiments.* Computational techniques are integrated into the text.* Takes a very practical approach, suitable for statistically-minded biologists.* Supported by a Website featuring colour images, software, and data sets.Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics. Zusammenfassung Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data - from getting good data to obtaining meaningful results.* Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference.* Features many examples throughout using real data from microarray experiments.* Computational techniques are integrated into the text.* Takes a very practical approach, suitable for statistically-minded biologists.* Supported by a Website featuring colour images, software, and data sets.Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics. Inhaltsverzeichnis Preface. 1 Preliminaries. 1.1 Using the R Computing Environment. 1.1.1 Installing smida. 1.1.2 Loading smida. 1.2 Data Sets from Biological Experiments. 1.2.1 Arabidopsis experiment: Anna Amtmann. 1.2.2 Skin cancer experiment: Nighean Barr. 1.2.3 Breast cancer experiment: John Bartlett. 1.2.4 Mammary gland experiment: Gusterson group. 1.2.5 Tuberculosis experiment: B µ G@S group. I Getting Good Data. 2 Set-up of a Microarray Experiment. 2.1 Nucleic Acids: DNA and RNA. 2.2 Simple cDNA Spotted Microarray Experiment. 2.2.1 Growing experimental material. 2.2.2 Obtaining RNA. 2.2.3 Adding spiking RNA and poly-T primer. 2.2.4 Preparing the enzyme environment. 2.2.5 Obtaining labelled cDNA. 2.2.6 Preparing cDNA mixture for hybridization. 2.2.7 Slide hybridization. 3 Statistical Design of Microarrays. 3.1 Sources of Variation. 3.2 Repli...
