Zhang A. Advanced Analysis of Gene Expression Microarray Data

World Scientific, 2006. — 339 p. — ISBN: 9812566457.
Series: Science, Engineering, and Biology Informatics (Book 1).This book focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for the identification of interesting, significant, and novel patterns in gene expression microarray data. Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data. Specifically, the coverage includes the following state-of-the-art methods: gene-based analysis — the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets; sample-based analysis — supervised and unsupervised methods for the reduction of the gene dimensionality to select significant genes. A series of approaches to disease classification and discovery are also described; pattern-based analysis — methods for ascertaining the relationship between (subsets of) genes and (subsets of) samples. Various novel pattern-based clustering algorithms to find the coherent patterns embedded in the sub-attribute spaces are discussed; and visualization tools — various methods for gene expression data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more easily understood two- or three-dimensional space.The Microarray: Key to Functional Genomics and Systems Biology, Applications of Microarray,
Framework of Microarray Data Analysis.
Basic Concepts of Molecular Biology.
Cells, Proteins, Nucleic Acids: DNA, RNA; Central Dogma of Molecular Biology:
Genes and the Genetic Code, Transcription and Gene Expression, Translation and Protein Synthesis;
Genotype and Phenotype.
Overview of Microarray Experiments.
Microarray Chip Manufacture, Steps of Microarray Experiments:
Sample Preparation and Labeling, Hybridization, Image Scanning;
Image Processing, Microarray Data Cleaning and Preprocessing, Data Normalization.
Analysis of Differentially-Expressed Genes.
Basic Concepts in Statistics: Statistical Inference, Hypothesis Test;
Fold Change Methods: k-fold Change, Unusual Ratios, Model-Based Methods;
Parametric Tests, Non-Parametric Tests, Multiple Testing, ANOVA: Analysis of Variance.
Gene-Based Analysis.
Proximity Measurement for Gene Expression Data, Partition-Based Approaches, Hierarchical Approaches,
Density-Based Approaches, GPX: Gene Pattern eXplorer, Cluster Validation.
Sample-Based Analysis.
Selection of Informative Genes, Class Prediction:
Linear Discriminant Analysis, KNN: k-Nearest Neighbor, Weighted voting, Decision Trees, Support Vector Machines;
Class Discovery, Classification Validation.
Pattern-Based Analysis.
Mining Association Rules, Mining Pattern-Based Clusters in Microarray Data, Mining Gene-Sample-Time Microarray Data.
Visualization of Microarray Data.
Single-Array Visualization: Box Plot, Histogram, Scatter Plot, Gene Pies;
Multi-Array Visualization: Global Visualizations, Optimal Visualizations, Projection Visualization; VizStruct.
New Trends in Mining Gene Expression Microarray Data.
Meta-Analysis of Microarray Data, Semi-Supervised Clustering, Integration of Gene Expression Data with Other Data.