Microarray Data Analysis

Preface
Contents
Contributors
Chapter 1: Tools in Pharmacogenomics Biomarker Identification for Cancer Patients
1 Introduction
2 TaqMan OpenArrayPGx Express Panel
3 The DMET Plus Array
4 PharmacoScan Solution
5 iPLEX ADME PGxPro Panel
6 Ion AmpliSeqPGx and the PRNG-seq Panels
7 Comparative Analysis of PGx Tools
7.1 Other Considerations
8 Conclusion
References
Chapter 2: High-Performance Framework to Analyze Microarray Data
1 Introduction
2 Related Work
3 Cloud4SNP
3.1 Loading of the Input Dataset and Sample Class Assignment
3.2 Execution of Statistical Tests and Correction of p-Values
3.2.1 An Example of Fisher Test Applied to SNPs
3.3 Data Mining Cloud Framework
3.4 Workflow Implementation
3.5 Using Apache Spark for Faster In-Memory Processing
4 Performance Evaluation
5 Conclusion
References
Chapter 3: Web and Cloud Computing to Analyze Microarray Data
1 Introduction
2 Microarray Data Analysis
3 Cloud Computing Background
4 Web and Cloud Computing to Analyze Microarray Data
4.1 Databases for Microarray Data Storage and Retrieval
4.2 Web Applications for Microarray Data Analysis
4.3 IaaS for Microarray Data Storage and Analysis
4.4 PaaS for Microarray Data Analysis
4.5 SaaS for Microarray Data Analysis
5 Conclusions
References
Chapter 4: A Microarray Analysis Technique Using a Self-Organizing Multiagent Approach
1 Introduction
2 Multiagent Algorithm for Virtual Structure Construction
3 Related Work
4 Performance Evaluation
4.1 Clustering Evaluation
5 Conclusion
References
Chapter 5: Improving Analysis and Annotation of Microarray Data with Protein Interactions
1 Introduction
2 Advanced Network Analysis
2.1 Network Structures
2.2 Network Properties
2.2.1 Global Network Properties
2.2.2 Local Network Properties
2.3 Using Stage-Specific Data to Model Tumor Progression
3 Materials
3.1 Computing Requirements
4 Methods
4.1 Finding Publicly Available Microarray Datasets and Running Differential Expression Analysis
4.2 Obtaining PPIs and Using IID to Analyze Network Properties of Differentially Expressed Genes
4.3 Using R to Analyze the Network Topology of Differentially Expressed Genes
5 Note
References
Chapter 6: Algorithms to Preprocess Microarray Image Data
1 Introduction
2 Microarray Structure
2.1 Possible Sources of Errors
3 Gridding
4 Segmentation
5 Intensity Quantification
6 Software Platforms
7 Conclusions
References
Chapter 7: Microarray Data Preprocessing: From Experimental Design to Differential Analysis
1 Introduction
2 Methods
2.1 Experimental Design
2.2 Quality Check
2.2.1 DNA/RNA Quality Check
2.2.2 Data Quality Check
Chip Image Analysis
Data Quality Check
Expression-Specific Data Quality Check
Methylation-Specific Data Quality Check
Platform-Independent Data Quality Check
2.3 Filtering
2.3.1 Filtering
2.3.2 Expression-Specific Probe Filtering
2.3.3 Methylation-Specific Probe Filtering
2.3.4 Platform-Independent Filtering
2.4 Imputation
2.5 Normalization
2.5.1 Expression-Specific Data Normalization
2.5.2 Methylation-Specific Data Normalization
2.5.3 Platform-Independent Data Normalization
2.6 Batch Effect Estimation and Correction
2.7 Probe Annotation
2.8 Data Representation for Expression and Methylation Microarrays
3 Differential Testing
4 Conclusions
References
Chapter 8: Supervised Methods for Biomarker Detection from Microarray Experiments
1 Introduction
2 Feature Selection-Based Approaches for Biomarker Discovery
3 Predictive Modeling
4 Classification-Based Predictive Modeling
5 Regression-Based Predictive Modeling
6 Validation Metrics
7 Accuracy Measures in Classification
8 Data Unbalancing
9 Goodness of Fit Measures in Regression
10 Model Selection and Hyperparameter Optimization
11 External Validation of Biomarkers
12 Biological Validation
13 Multiomics Strategies
14 Conclusions
References
Chapter 9: Unsupervised Algorithms for Microarray Sample Stratification
1 Introduction
2 Methods
2.1 Metrics for Unsupervised Learning
2.2 Dimensionality Reduction
2.2.1 Principal Components Analysis (PCA)
2.2.2 Non-negative Matrix Factorization (NMF)
2.2.3 Isometric Mapping
2.3 Clustering
2.3.1 Consensus Clustering
2.3.2 Subspace Clustering
2.3.3 Evaluation Metrics
2.4 Biclustering
2.5 Multiomics Clustering
3 Conclusions
References
Chapter 10: Pathway Enrichment Analysis of Microarray Data
1 Introduction
2 Computing Requirements
3 Methods
3.1 Differential Genes Obtained from Microarray Data
3.2 Pathway Enrichment Analysis
3.3 GSOAP Plot
4 Notes
References
Chapter 11: Network Analysis of Microarray Data
1 Introduction
2 What Is a Graph
3 Algorithms for Gene Coexpression Networks
4 Local and Global Connectivity Measures
5 Community Detection Algorithms
6 Pathway Enrichment Analysis
7 Differential Coexpression Analysis
8 Integration Strategies for Graphs
9 Graphical Models
10 Conclusions/Summary
References
Chapter 12: geneExpressionFromGEO: An R Package to Facilitate Data Reading from Gene Expression Omnibus (GEO)
1 Introduction
2 The geneExpressionFromGEO Package
3 Installation
4 Example of Usage
5 Conclusions
References
Chapter 13: Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies
1 Introduction
2 Microarray and Next-Generation Sequencing Technologies for Human Host Expression Profiling
3 COVID-19 and Its Responsible Virus
3.1 Knowledge Transfer from Other Viruses´ Infections to SARS-CoV-2 Ones
3.2 Knowledge Transfer from Related Diseases to COVID-19
4 Data Acquisition
5 Integration Levels
6 Integrative Studies: Possible Scenarios
7 Conclusions
References
Chapter 14: Alignment of Microarray Data
1 Introduction
2 Microarray Data Analysis
2.1 General Approach
2.1.1 Clustering
2.1.2 Distance Between Data Points
L2 and L1 Norms
Cosine Distance
Hamming Distance
3 Overview of Innovative Methods
4 Beyond the Classical Approach
4.1 Edit Distance
4.2 Distance in Heterogeneous Contexts
4.3 An Extension of the Edit Distance for Heterogeneous Contexts
4.4 Applications
4.4.1 Wireless Sensor Area Networks
4.4.2 Biomedical Data
5 Conclusion
References
Chapter 15: Integration of DNA Microarray with Clinical and Genomic Data
1 Introduction
2 DNA Microarrays
3 Gene Expression Profiling
4 Epigenomic Profiling
5 Copy Number Variation Analysis
6 Pharmacogenomic Genotyping
7 Standardization of Microarray Data
8 Integration of Microarray with Genomic and Clinical Data
9 Conclusion
References
Chapter 16: Clustering Methods for Microarray Data Sets
1 Introduction
2 Cluster Analysis
2.1 Hierarchical Clustering
2.2 The Single Linkage Method
2.3 The Centroid Linkage Method
3 Conclusion
References
Chapter 17: Microarray Data Analysis Protocol
1 Introduction
2 Software Tools to Analyze SNP Microarrays
3 Microarray Data Analysis Protocol
4 Conclusion
References
Chapter 18: Using Gene Ontology to Annotate and Prioritize Microarray Data
1 Introduction
2 Related Work
2.1 Ontologies
2.2 Semantic Similarities
2.3 Gene Prioritization Approaches
3 GOD Tool
3.1 Application of GoD on Case Study
4 Results and Discussion
5 Conclusion
References
Chapter 19: Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma
1 Introduction
2 Background
2.1 Genomic Data Sources
2.1.1 The Cancer Genome Atlas (TCGA)
2.1.2 NCI GDC Genomic Data Commons (GDC) Data Portal
2.1.3 Multiple Myeloma Research Foundation (MMRF) CoMMpass
2.1.4 Gene Expression Omnibus
2.2 Methods for Integration and Analysis of Genomic Data
2.2.1 Differential Gene Expression Analysis
2.2.2 Kaplan-Meier Survival Analysis
2.2.3 Enrichment Analysis
3 MMRFBiolinks Package
4 Workflow for Downloading and Analyzing MMRF-CoMMpass Data
4.1 Searching
4.2 Downloading and Preparing
4.3 Analyzing
4.4 Searching, Downloading and Preparing
4.5 Analyzing
5 Results
5.1 Data
5.2 Case Study 1: RNA-Seq Analysis for Bone Marrow Sample Types
5.2.1 Array-Array Intensity Correlation
5.2.2 Differential Gene Expression Analysis
5.2.3 Kaplan-Meier Survival Analysis
5.2.4 Enrichment Analysis
5.3 Case Study 2: Correlation Between Annotated Variants, Best Overall Response and Treatment Class
6 Discussion
References
Index