Microarray Data Analysis

Description

This meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data. The book includes examples and code in R, requiring only an introductory computer science understanding, and the structure and the presentation of the chapters make it suitable for use in bioinformatics courses. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of key detail and expert implementation advice that ensures successful results and reproducibility.  Authoritative and practical, Microarray Data Analysis is an ideal guide for students or researchers who need to learn the main research topics and practitioners who continue to work with microarray datasets. 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