Gene Expression Data Analysis: A Statistical and Machine Learning Perspective

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Acknowledgements
Authors
Preface
1. Introduction
	1.1. Introduction
	1.2. Central Dogma
	1.3. Measuring Gene Expression
	1.4. Representation of Gene Expression Data
	1.5. Gene Expression Data Analysis: Applications
	1.6. Machine Learning
	1.7. Statistical and Biological Evaluation
	1.8. Gene Expression Analysis Approaches
		1.8.1. Preprocessing in Microarray and RNAseq Data
		1.8.2. Co-Expressed Pattern-Finding Using Machine Learning
		1.8.3. Co-Expressed Pattern-Finding Using Network-Based Approaches
	1.9. Differential Co-Expression Analysis
	1.10. Differential Expression Analysis
	1.11. Tools and Systems for Gene Expression Data Analysis
		1.11.1. (Diff) Co-Expression Analysis Tools and Systems
		1.11.2. Differential Expression Analysis Tools and Systems
	1.12. Contribution of This Book
	1.13. Organization of This Book
2. Information Flow in Biological Systems
	2.1. Concept of Systems Theory
		2.1.1. A Brief History of Systems Thinking
		2.1.2. Areas of Application of Systems Theory in Biology
	2.2. Complexity in Biological Systems
		2.2.1. Hierarchical Organization of Biological Systems from Macroscopic Levels to Microscopic Levels
		2.2.2. Information Flow in Biological Systems
		2.2.3. Top-Down and Bottom-Up Flow
	2.3. Central Dogma of Molecular Biology
		2.3.1. DNA Replication
		2.3.2. Transcription
		2.3.3. Translation
	2.4. Ambiguity in Central Dogma
		2.4.1. Reverse Transcription
		2.4.2. RNA Replication
	2.5. Discussion
		2.5.1. Biological Information Flow from a Computer Science Perspective
		2.5.2. Future Perspective
3. Gene Expression Data Generation
	3.1. History of Gene Expression Data Generation
	3.2. Low-Throughput Methods
		3.2.1. Northern Blotting
		3.2.2. Ribonuclease Protection Assay
		3.2.3. qRT-PCR
		3.2.4. SAGE
	3.3. High-Throughput Methods
		3.3.1. Microarray
		3.3.2. RNA-Seq
		3.3.3. Types of RNA-Seq
		3.3.4. Gene Expression Data Repositories
		3.3.5. Standards in Gene Expression Data
	3.4. Chapter Summary
4. Statistical Foundations and Machine Learning
	4.1. Introduction
	4.2. Statistical Background
		4.2.1. Statistical Modeling
		4.2.2. Probability Distributions
		4.2.3. Hypothesis Testing
		4.2.4. Exact Tests
		4.2.5. Common Data Distributions
		4.2.6. Multiple Testing
		4.2.7. False Discovery Rate
		4.2.8. Maximum Likelihood Estimation
	4.3. Machine Learning Background
		4.3.1. Significance of Machine Learning
		4.3.2. Machine Learning and Its Types
		4.3.3. Supervised Learning Methods
		4.3.4. Unsupervised Learning Methods
		4.3.5. Outlier Mining
		4.3.6. Association Rule Mining
	4.4. Chapter Summary
		4.4.1. Statistical Modeling
		4.4.2. Supervised Learning: Classification and Regression Analysis
		4.4.3. Proximity Measures
		4.4.4. Unsupervised Learning: Clustering
		4.4.5. Unsupervised Learning: Biclustering
		4.4.6. Unsupervised Learning: Triclustering
		4.4.7. Outlier Mining
		4.4.8. Unsupervised Learning: Association Mining
5. Co-Expression Analysis
	5.1. Introduction
	5.2. Gene Co-Expression Analysis
		5.2.1. Types of Gene Co-Expression
		5.2.2. An Example
	5.3. Measures to Identify Co-Expressed Patterns
	5.4. Co-Expression Analysis Using Clustering
		5.4.1. CEA Using Clustering: A Generic Architecture
		5.4.2. Co-Expressed Pattern Finding Using 1-Way Clustering
		5.4.3. Subspace or 2-way Clustering in Co-Expression Mining
		5.4.4. Co-Expressed Pattern-Finding Using 3-Way Clustering
	5.5. Network Analysis for Co-Expressed Pattern-Finding
		5.5.1. Definition of CEN
		5.5.2. Analyzing CENs: A Generic Architecture
	5.6. Chapter Summary and Recommendations
6. Differential Expression Analysis
	6.1. Introduction
		6.1.1. Importance of DE Analysis
	6.2. Differential Expression (DE) of a Gene
		6.2.1. Differential Expression of a Gene: An Example
	6.3. Differential Expression Analysis (DEA)
		6.3.1. A Generic Framework
		6.3.2. Preprocessing
		6.3.3. DE Genes Identification
		6.3.4. DE Gene Analysis
		6.3.5. Statistical Validation
		6.3.6. Discussion
	6.4. Biomarker Identification Using DEA: A Case Study
		6.4.1. Problem Definition
		6.4.2. Dataset Used
		6.4.3. Preprocessing
		6.4.4. Framework of Analysis Used
		6.4.5. Results
		6.4.6. Discussion
	6.5. Summary and Recommendations
7. Tools and Systems
	7.1. Introduction
		7.1.1. Generic Characteristics of a Systems Biology Tool
		7.1.2. Target Systems Biology Activities
	7.2. Systems Biology Tools
		7.2.1. A Taxonomy
		7.2.2. Pre-Processing Tools
	7.3. Gene Expression Data Analysis Tools
		7.3.1. Co-Expression Analysis
		7.3.2. Differential Co-Expression Analysis
		7.3.3. Differential Expression Analysis
	7.4. Visualization
	7.5. Validation
		7.5.1. Statistical Validation
	7.6. Biological Validation
	7.7. Chapter Summary and Concluding Remarks
8. Concluding Remarks and Research Challenges
	8.1. Concluding Remarks
	8.2. Some Issues and Research Challenges
Bibliography
Glossary
Index