Translational Bioinformatics for Therapeutic Development

Preface
Contents
About the Editor
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Contributors
Chapter 1: Development and Optimization of Clinical Informatics Infrastructure to Support Bioinformatics at an Oncology Center
1 Introduction
2 Organization-Specific Issues
2.1 Staffing and Workflow Issues
3 Initial Assessment of Workflows for Translational Informatics
3.1 Governance
3.2 Workflows and Staffing
4 Examples of Translational Informatics Projects
5 Future Directions
References
Chapter 2: Leveraging Pathology Informatics Concepts to Achieve Discrete Lab Data for Clinical Use and Translational Research
1 Introduction
2 Suggested Practices to Achieve Discrete Data from the Laboratory
2.1 Team Engagement
2.2 Defining Foundational Lab Concepts
2.3 Requesting System Changes
2.4 Understanding Current Processes
2.5 Defining the Scope and Designing a Solution
2.6 Implementation and Validation
2.7 System Maintenance
3 Discussion of an Example: ``A Day in the Life´´ Utilizing These Suggested Practices
References
Chapter 3: Cohort Identification for Translational Bioinformatics Studies
1 Introduction
2 Software Dependencies
3 Method
References
Chapter 4: Transitioning Clinical Practice Guidelines into the Electronic Health Record through Clinical Pathways
1 Introduction
2 Foundational Requirements for Integrated Clinical Pathways
3 Clinical Pathway Visual Representation
3.1 Initial Pathway Diagram
3.2 Clinical Trials Inclusion
3.3 Usability Refinement
4 Pathway Approval Process
4.1 Consensus
4.2 Pathway Approval Committee
4.3 Chair Approval
5 EHR Clinical Pathway Integration
5.1 Hand-Off and EHR Design
5.2 EHR Build and Testing
5.3 EHR Go-Live and Training
5.4 Version Control
6 Future Directions
References
Chapter 5: Variable Selection for Time-to-Event Data
1 Introduction
2 Materials
3 Methods
3.1 Filter-Test Based Method
3.2 Penalized Regression Method
3.3 Machine Learning Method
3.3.1 Survival Tree and Random Survival Forest
3.3.2 Supervise Principal Component
References
Chapter 6: Binary Classification for Failure Risk Assessment
1 Introduction
2 The Binary Classification Approach
2.1 Problem Setup
2.2 Classification Algorithms
2.2.1 GLM with Logit Link
2.2.2 GLM with Probit Link
2.2.3 GLMs with Quadratic Kernels
2.2.4 Linear Discriminant Analysis
2.2.5 Quadratic Discriminant Analysis
2.2.6 k Nearest Neighbors (kNN)
2.2.7 Random Forests
3 Variable Selection
3.1 Feature Selection Algorithms
3.1.1 t-test
3.1.2 Bhattacharyya Distance
3.1.3 Mutual Information
3.1.4 Wilcoxon Rank Sum Test
3.1.5 Kolmogorov-Smirnov Test
3.1.6 Optimal Bayesian Filtering
3.1.7 Posterior Factor-Constrained
3.2 Variable Selection, Biological Relevance, Prediction Accuracy, and False Discovery Rates
4 How to Handle Random Censoring?
5 Multiple Time Intervals
6 Synthetic Simulations
6.1 Data Generation
6.2 Prediction Evaluation
6.3 Variable Selection
6.4 Prediction in Presence of Selection
6.5 The Effect of Random Censorship
7 Experimental Data Analysis
7.1 Breast Cancer
7.2 Lung Cancer
8 Conclusion
References
Chapter 7: Challenges and Opportunities of Genomic Approaches in Therapeutics Development
1 A Brief History of Genomic Approaches in Biomedical Research
2 Genomic Arenas for Study in Human Health and Disease
2.1 Genomic Structure and Variation to Predict Therapeutic Responses
2.2 Transcriptomic Dynamics, Diversity, and Interactions in Therapeutic Responses
2.3 Genomic and Transcriptomic Modifications
3 Statistical and Computational Challenges in the Analyses of Genomic Approaches
4 Summary
References
Chapter 8: Accessible Pipeline for Translational Research Using TCGA: Examples of Relating Gene Mechanism to Disease-Specific ...
1 Introduction
2 Materials
2.1 Sample Annotation
2.2 RNAseq Gene Expression
2.3 GISTIC Copy Number Alterations Data
2.4 Survival Data
2.5 Methylation Illumina 450K Data
3 Methods
3.1 Tumor Versus Normal Box Plot
3.2 GISTIC Plot
3.3 Gene Expression vs. GISITC
3.4 Correlation Analysis
3.5 Epigenetic Regulation by Methylation
3.6 Survival Analysis
4 Concluding Remarks
References
Chapter 9: Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments
1 Introduction
2 Datasets Used to Illustrate the Methods
2.1 Bulk RNA-Sequencing Study: TCGA Skin Cancer Study
2.2 Single Cell Sequencing Study: Tirosh et al. Study
3 Statistical and Bioinformatics Methods for Analysis of Bulk RNA-Seq Data
3.1 Quality Control of RNA-Seq Data
3.2 Methods for Read Alignment
3.3 Methods for Transcript Reconstruction
3.4 Methods for Gene Summarization or Abundance Estimation
3.5 Normalization
3.6 Methods for Differential Gene Expression Analysis
3.7 Methods for Correcting for Multiple Testing
3.8 Studying TME Using RNA-Seq in Bulk Samples
4 Statistical and Bioinformatics Methods for Analysis of Single-Cell RNA-Seq Data
4.1 Quality Control
4.2 Drop-Outs, Normalization, and Spike-Ins
4.2.1 Normalization Methods
4.2.2 Drop-Out Imputation
4.2.3 Spike-Ins
4.3 Data Integration and Batch Correction
4.4 Dimension Reduction, Clustering, and Cell Type Identification
4.4.1 Dimension Reduction and Feature Selection
4.4.2 Unsupervised Clustering
4.4.3 Supervised Classifier and Cell Type Identification
4.5 Studying Heterogeneity Using scRNA-Seq
5 Conclusions
References
Chapter 10: Investigating Inter- and Intrasample Diversity of Single-Cell RNA Sequencing Datasets
1 Introduction
1.1 Chapter Outline
2 Materials
2.1 Count Matrix Generation
2.1.1 Downloading and Installing Tools
2.2 Preparing Data for Diversity Analysis
3 Methods
3.1 Quality Control and Normalization of Count Matrix
3.2 Cluster Detection
3.2.1 Dimension Reduction
3.2.2 Community-Based Detection Methods
3.3 Diversity Score Calculation
3.3.1 Generalized Diversity Index
3.3.2 Kolmogorov Smirnov Distance
4 Example
4.1 Background
4.2 Method
5 Notes
6 Summary
References
Chapter 11: Managing a Large-Scale Multiomics Project: A Team Science Case Study in Proteogenomics
1 Introduction
2 Materials
2.1 Team
2.2 Large Biobank
2.3 Data Resources for Querying Clinical Information and Biobanked Samples
2.4 Data Concierge with Honest Broker Capability for Managing Data Queries
2.5 Experimental Resources
3 Methods
3.1 Experimental Design
3.2 Cohort Design
3.3 Operational Design
3.4 Clinical Data Collection
3.5 Tissue Processing
3.6 Proteomics
3.7 Analysis of Expression Proteomics Data
3.8 Nucleic Acid Processing
3.9 Molecular Genomics
3.10 DNA Alignment
3.11 RNA Alignment
3.12 Genomic Variation Detection
3.13 RNA Gene Expression
3.14 Individual Omics Analysis
3.15 RNA, DNA, and Protein Integration
3.16 Performing an Integrated Proteogenomics Study in Lung Squamous Cell Carcinoma
3.17 Conclusions
4 Notes
References
Chapter 12: Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models
1 Introduction
2 Materials
2.1 Drug Combination Screening Dataset
2.2 Omics Data of Cancer Cell Lines and TCGA Samples
2.3 Physicochemical Features of Drugs
3 Methods
3.1 Data Preprocessing
3.2 Model Architecture of AuDNNsynergy
3.3 Model Evaluation and Comparison of Drug Combination Prediction
3.3.1 Model Computational Environment
3.3.2 Performance Evaluation of AuDNNsynergy
3.3.3 Synergistic Drug Combination Prediction for TCGA Samples
3.3.4 Model Comparison
4 Summary of the AuDNNsynergy Model
References
Chapter 13: Introduction to Multiparametric Flow Cytometry and Analysis of High-Dimensional Data
1 Introduction
2 Materials
2.1 Flow Cytometry Analysis
2.2 Controls in Flow Cytometry
2.2.1 Compensation in Flow Cytometry
2.2.2 Live-Dead Controls
2.2.3 Doublet Discrimination
2.2.4 Fluorescence-Minus-One Control
2.2.5 Isotype Controls
2.2.6 Nonspecific Binding
2.3 Running the Experiment
2.4 Principles of Multidimensional Data Analysis
2.4.1 The ``Curse of Dimensionality´´
2.4.2 Manual Data Display Utilizing Available Software Packages
2.5 Informatics Analysis of High-Dimensional Flow Cytometry Data
References
Chapter 14: High-Dimensional Flow Cytometry Analysis of Regulatory Receptors on Human T Cells, NK Cells, and NKT Cells
1 Introduction
2 Materials
3 Methods
3.1 Staining Protocol
3.2 Development Strategy
3.3 Gate-Based Analysis
4 Notes
References
Chapter 15: Quantitative Analysis of Bile Acid with UHPLC-MS/MS
1 Introduction
2 Materials
2.1 Standards
2.2 Reagents
2.3 UHPLC-MS/MS
3 Methods
3.1 Sample Collection (See Note 4)
3.2 Sample Preparation (See Note 5)
3.3 UHPLC-MS/MS (See Notes 9-11)
3.4 Data Analysis
4 Notes
References
Chapter 16: Sample Preparation and Data Analysis for NMR-Based Metabolomics
1 Introduction
2 Materials
2.1 NMR Stock Buffers
2.1.1 Cell Extract (100 ml, 0.1 M)
2.1.2 Cell Media (25 ml, 0.4 M)
2.1.3 Serum/Plasma (100 ml, 0.045 M)
2.1.4 Urine (100 ml, 1.5 M)
KF Stock Solution (25 ml, 5 M)
EDTA-d12 Stock Solution (25 ml, 120 mM)
2.1.5 Tissue Extract, Cecum Content, and Feces (100 ml, 0.1 M)
2.1.6 Saliva (100 ml, 0.1 M)
2.2 NMR Sample Preparation
2.2.1 Cell Extraction
2.2.2 Cell Media
2.2.3 Serum/Plasma
When the Volume of Serum/Plasma Enough
When the Volume Serum/Plasma, Not Enough (50 μl)
2.2.4 Urine
2.2.5 Tissue Extract
2.2.6 Cecum Content and Feces Extract
2.2.7 Saliva
2.3 NMR Data Acquisition and Processing
2.4 Data Analysis
3 Metabolomics Profiling and Multivariate Analysis
References
Index