Advanced analysis of gene expression microarray data 1st Edition by Aidong Zhang ISBN 978-9812566454 9812566454
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ISBN 10: 9812566454
ISBN 13: 978-9812566454
Author: Aidong Zhang
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 * 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.
Table of contents:
1. Introduction
1.1 The Microarray: Key to Functional Genomics and Systems
Biology
1.2 Applications of Microarray
1.2.1 Gene Expression Profiles in Different. Tissues
1.2.2 Developmental Genetics
1.2.3 Gene Expression Patlers in Model Systems
1.2.4 Differential Gene Expression Patterns in Diseases
1.2.5 Cene Expression Patterns in Pathogens
1.2.6 Genc Expression in Response to Drug Treatments.
1.2.7 Genotypic Analysis
1.2.8 Mutation Screening of Disease Genes
1.3 Framework of Microarray Data Analysis
1.4 Summary
2. Basic Concepts of Molecular Biology
2.1 Introduction.
2.2 Cells
2.3 Proteins
2.4 Nucieic Acids
2.4.1 DNA
2.4.2 RNA
2.5 Central Dogma of Molecular Biology
2.5.1 Genes and the Genetic Code
2.5.2 Transcription and Gene Expression
2.5.3 Translation and Protein Synthesis
2.6 Genotype and Phenotype
2.7 Summary
3. Overview of Microarray Experiments
3.1 Introduction.
3.2 Manufactured Microarray Chip
3.2.1 Deposition-Based Manufacture
3.2.2 In Situ Manufacture
3.2.2.1 The Affymetrix GeneChip
3.3 Steps of Microarray Experiments
3.3.1 Sample Preparation and Labeling
3.3.2 Hybridization
3.3.3 Image Scanning
3.4 Image Processing
3.5 Microarray Data Cleaning and Preprocessing
3.5.1 Data Transformation
3.5.2 Missing Value Estimation
3.6 Data Normalization
3.6.1 Global Normalization Approaches
3.6.1.1 Standardization
3.6.1.2 Iterative linear regression.
3.6.2 Intensity-Dependent Normalization
3.6.2.1 LOWESS: Locally weighted linear regression
3.6.2.2 Distribution normalization.
3.7 Summary
4. Analysis of Differentially-Expressed Genes
4.1 Introduction.
4.2 Basic Concepts in Statistics
4.2.1 Statistical Inference
4.2.2 Hypothesis Test
4.3 Fold Change Methods
4.3.1 k-fold Change
4.3.2 Umasual Ratios
4.3.3 Model-Based Methods
4.4 Parametric Tests
4.4.1 Paired t-Test
4.4.2 Unpaired t-Test
4.4.3 Variants of t-Test
4.5 Non-Parametric Tests
4.5.1 Classical Non-Parametric Statistics
4.5.2 Other Non-Parametric Statistics
4.5.3 Bootstrap Analysis
4.6 Multiple Testing
4.6.1 Family-Wise Error Rate
4.6.1.1 Šidák correction and Bonferroni correction
4.6.1.2 Holm’s step wise correction
4.6.2 False Discovery Rate
4.6.3 Permutation Correction
4.6.4 SAM: Significance Analysis of Microarrays
4.7 ANOVA: Analysis of Variance
4.7.1 One-Way ANOVA
4.7.2 Two-Way ANOVA
4.8 Summary
5. Gene Based Analysis
5.1 Introduction.
5.2 Proximity Measurement for Gene Expression Data
5.2.1 Euclidean Distance
5.2.2 Correlation Coefficient
5.2.2.1 Pearson’s correlation coefficient
5.2.2.2 Jackknife correlation
5.2.2.3 Spearman’s rank-order correlation
5.2.3 Kullback-Leibler Divergence
5.3 Partition-Based Approaches
5.3.1 K-means and its Variations
5.3.2 SOM and its Extensions
5.3.3 Graph-Theoretical Approaches
5.3.3.1 HCS and CLICK
5.3.3.2 CAST: Cluster affinity search technique
5.3.4 Model-Based Clustering
5.4 Hierarchical Approaches
5.4.1 Agglomerative Algorithms
5.4.2 Divisive Algorithms..
5.4.2.1 DAA: Deterministic annealing algorithm
5.4.2.2 SPC: Super-paramagnetic clustering
5.5 Density-Based Approaches
5.5.1 DBSCAN
5.5.2 OPTICS
5.5.3 DENCLUE
5.6 GPX: Gene Pattern eXplorer
5.6.1 The Attraction Tree
5.6.1.1 The distance measure
5.6.1.2 The density definition.
5.6.1.3 The attraction tree
5.6.1.4 An example of attraction tree
5.6.2 Interactive Exploration of Coherent Patterns
5.6.2.1 Generating the index list.
5.6.2.2 The coherent pattern index and its graph
5.6.2.3 Drilling down to subgroups
5.6.3 Experimental Results.
5.6.3.1 Interactive exploration of Iyer’s data and Spellman’s data
5.6.3.2 Comparison with other algorithms
5.6.4 Efficiency and Scalability.
5.7 Cluster Validation
5.7.1 Homogeneity and Separation
5.7.2 Agreement with Reference Partition
5.7.3 Reliability of Clusters
5.7.3.1 P-value of a cluster
5.7.3.2 Prediction strength
5.8 Summary
6. Sample-Based Analysis
6.1 Introduction
6.2 Selection of Informative Genes
6.2.1 Supervised Approaches
6.2.1.1 Differentially expressed genes
6.2.1.2 Gene pairs
6.2.1.3 Virtual genes
6.2.1.4 Genetic algorithms
6.2.2 Unsupervised Approaches
6.2.2.1 PCA: Principal component analysis
6.2.2.2 Gene shaving
6.3 Class Prediction
Contents
6.3.1 Linear Discriminant Analysis
6.3.2 Instance-Based Classification
6.3.2.1 KNN: -Nearest Neighbor
6.3.2.2 Weighted voting
6.3.3 Decision Trees.
6.3.4 Support Vector Machines
6.4 Class Discovery
6.4.1 Problem statement
6.4.2 CLIFF: CLustering via Iterative Feature Filtering
6.4.2.1 The sample-partition process.
6.4.2.2 The gene-filtering process
6.4.3 ESPD: Empirical Sample Pattern Detection
6.4.3.1 Measurements for phenotype structure detection
6.4.3.2 Algorithms.
6.4.3.3 Experimental results
6.5 Classification Validation
6.5.1 Prediction Accuracy
6.5.2 Prediction Reliability
6.6 Summary
7. Pattern-Based Analysis
7.1 Introduction.
7.2 Mining Association Rules
7.2.1 Concepts of Association-Rule Mining
7.2.2 The Apriori Algorithm
7.2.3 The FP-Growth Algorithm.
7.2.4 The CARPENTER Algorithm
7.2.5 Generating Association Rules in Microarray Data
7.2.5.1 Rule filtering
7.2.5.2 Rule grouping
7.3 Mining Pattern-Based Clusters in Microarray Data
7.3.1 Heuristic Approaches
7.3.1.1 Coupled two-way clustering (CTWC)
7.3.1.2 Plaid model
7.3.1.3 Biclustering and 6-Clusters.
7.3.2 Deterministic Approaches
7.3.2.1 5-pCluster
7.3.2.2 OP Cluster
7.4 Mining Gene-Sample-Time Microarray Data.
7.4.1 Three-dimensional Microarray Data.
7.4.2 Coherent Gene Clusters
7.4.2.1 Problem description..
7.4.2.2 Maximal coherent sample sets
7.4.2.3 The mining algorithms
7.4.2.4 Experimental results
7.4.3 Tri-Clusters
7.4.3.1 The tri-cluster model
7.4.3.2 Properties of tri-clusters
7.4.3.3 Mining tri-clusters
7.5 Summary
8. Visualization of Microarray Data
8.1 Introduction
8.2 Single-Array Visualization
8.2.1 Box Plug
8.2.2 Ilistogram
8.2.3 Scatter Plot
8.2.1 Gene Pies
8.3 Multi-Array Visualization
8.3.1 Global VisualizaticniS
8.3.2 Optimal Visualizations
8.3.3 Projection Visualization
8.4 VizStruct
8.4.1 Fourier Harmonic Projections
8.4.1.1 Discrete-time signal paradigm
8.4.1.2 The Fourier harmonic projection algorithm.
8.4.2 Properties of FHPs
8.4.2.1 Basic properties
8.4.2.2 Advanced properties
8.4.2.3 Harmonic equivalency
8.4.2.4 Effects of harmonic twiddle power index
8.4.3 Enhancements of Fourier Harmonic Projections
8.4.4 Exploratory Visualization of Gene Profiling
8.4.4.1 Microarray data sets for visualization
8.4.4.2 Identification of informative genes
8.4.4.3 Classifier construction and evaluation.
8.4.4.4 Dimension arrangement
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Tags: Aidong Zhang, Advanced analysis, gene expression, microarray data