A biologist's guide to analysis of DNA microarray data / Steen Knudsen.

Includes bibliographical references (pages 104-122).

Includes bibliographical references and index.

Hybridization -- Affymetrix GeneChip Technology -- Spotted Arrays -- Serial Analysis of Gene Expression (SAGE) -- Example: Affymetrix vs. Spotted Arrays -- Overview of Data Analysis -- Basic Data Analysis -- Absolute Measurements -- Scaling -- Example: Linear and Nonlinear Scaling -- Detection of Outliers -- Fold Change -- Significance -- Nonparametric Tests -- Correction for Multiple Testing -- Example I: t-Test and ANOVA -- Example II: Number of Replicates -- Visualization by Reduction of Dimensionality -- Principal Component Analysis -- Example 1: PCA on Small Data Matrix -- Example 2: PCA on Real Data -- Cluster Analysis -- Hierarchical Clustering -- K-means Clustering -- Self-Organizing Maps -- Distance Measures -- Example: Comparison of Distance Measures -- Normalization -- Visualization of Clusters -- Example: Visualization of Gene Clusters in Bladder Cancer -- Beyond Cluster Analysis -- Function Prediction -- Discovery of Regulatory Elements in Promoter Regions -- Example 1: Discovery of Proteasomal Element -- Example 2: Rediscovery of Mlu Cell Cycle Box (MCB) -- Integration of data -- Reverse Engineering of Regulatory Networks -- The Time-Series Approach -- The Steady-State Approach -- Limitations of Network Modeling -- Example 1: Steady-State Model -- Example 2: Steady-State Model on Real Data -- Example 3: Steady-State Model on Real Data -- Example 4: Linear Time-Series Model -- Molecular Classifiers -- Classification Schemes -- Nearest Neighbor -- Neural Networks -- Support Vector Machine.

A Biologist's Guide to Analysis of DNA Microarray Data Steen Knudsen Microarrays are low-density arrays of DNA molecules that permit many hybridization experiments to be performed in parallel. A Biologist's Guide to Analysis of DNA Microarray Data is the first authoritative text to focus on analysis (as opposed to technology) in diverse biological and medical applications of microarrays. Written for biologists without special training in data analysis and statistics, the Guide takes over where the image analysis software that accompanies DNA array equipment typically leaves off with a file of signal intensities and fold changes compared to a control. The broad spectrum of established analysis approaches is covered, including cluster analysis, function prediction, and principal component analysis. A thorough, critical review of software programs is presented, and criteria for selecting programs are discussed. Each chapter contains a Further Reading section categorized by topic as well as a summary, and highlights numerous real examples to illustrate the key concepts.; Chapter topics include: Basic data analysis Visualization by reduction of dimensionality Cluster analysis of expression data Molecular classifiers Genotyping chips Software issues and data formats A Biologist's Guide to Analysis of DNA Microarray Data meets the needs of research professionals, students, and trainees alike for a compact yet thorough guide to analyzing microarray data.

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