Handbook of Statistical Bioinformatics

Preface
Contents
Part I Single-Cell Analysis
	Computational and Statistical Methods for Single-Cell RNA Sequencing Data
		1 Introduction
		2 Data Preprocessing
			2.1 Reads Mapping
			2.2 Cell Barcodes Demultiplexing
			2.3 UMI Collapsing
			2.4 Cell Barcodes Selection
			2.5 Summary
		3 Data Normalization and Visualization
			3.1 Background
			3.2 Global Scaling Normalization for UMI Data
			3.3 Probabilistic Model-Based Normalization for UMI Data
			3.4 Dimension Reduction and Cell Clustering
		4 Dropout Imputation
			4.1 Background
			4.2 Cell-Cell Similarity-Based Imputation
			4.3 Gene-Gene Similarity-Based Imputation
			4.4 Gene-Gene and Cell-Cell Similarity-Based Imputation
			4.5 Deep Neural Network-Based Imputation
			4.6 G2S3
			4.7 Methods Evaluation and Comparison
		5 Differential Expression Analysis
			5.1 Background
			5.2 DE Methods Ignoring Subject Effects
			5.3 DE Methods Considering Subject Effects
			5.4 iDESC
			5.5 DE Methods Evaluation and Comparison
				5.5.1 Type I Error Comparison
				5.5.2 Statistical Power Comparison
		6 Concluding Remarks
		References
	Pre-processing, Dimension Reduction, and Clustering for Single-Cell RNA-seq Data
		1 Introduction
		2 Pre-processing of scRNA-seq Data
			2.1 Removal of Batch Effects
			2.2 Quality Control and Feature Selection
		3 Dimension Reduction and Clustering
			3.1 Dimension Reduction
			3.2 Clustering
		4 Conclusion
		References
	Integrative Analyses of Single-Cell Multi-Omics Data: A Review from a Statistical Perspective
		1 Multi-Omics Data Profiled on Different Cells
		2 Multi-Omics Data Profiled on the Same Single Cells
		3 Challenges and Future Perspectives
		References
	Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data
		1 Introduction
		2 Marker Gene Selection Relies on Identifying Differentially Expressed Genes
		3 Methods for Marker Gene Selection
			3.1 Highest Expressed, Highest Variable
		4 Supervised Methods
			4.1 COMET
			4.2 scGeneFit
		5 Unsupervised Methods
			5.1 Seurat
			5.2 SC3
			5.3 SCMarker
			5.4 scTIM
			5.5 RankCorr
		6 Discussion
		References
	Model-Based Clustering of Single-Cell Omics Data
		1 Introduction
		2 Single-Cell Transcriptomic Data Clustering
			2.1 Single-Cell Transcriptomic Data Structure
			2.2 DIMM-SC
			2.3 Real Data Example
		3 Population-Scale Single-Cell Transcriptomic Data Clustering
			3.1 Population-Scale Single-Cell Transcriptomic Data Structure
			3.2 BAMM-SC
			3.3 Real Data Example
		4 Single-Cell Multi-omics Data Clustering
			4.1 CITE-seq Data Structure
			4.2 BREM-SC
			4.3 Real Data Example
		5 Concluding Remarks
		References
	Deep Learning Methods for Single-Cell Omics Data
		1 Introduction
		2 Factor-Model-Based Deep Learning Approaches
			2.1 Regularization and Priors on the Latent Factors
				2.1.1 Gaussian Prior and Variational Inference
				2.1.2 Adjust for Batch Effects and Confounding Covariates: Identifiability
				2.1.3 Adjust for Batch Effects and Confounding Covariates: Implementation
				2.1.4 Model Cell Population Structure in the Latent Space
			2.2 Distributional Assumptions on Observed Data
				2.2.1 Model Observed Data from scRNA-seq
				2.2.2 Model Observed Data from scATAC-seq
				2.2.3 Model Observed Data from Single-Cell Multiomics Technologies
			2.3 Post-training Statistical Analyses
				2.3.1 Denoising
				2.3.2 Visualization, Clustering, and Trajectory Analysis
				2.3.3 Prediction
		3 Deep Learning Methods for Dimension Reduction
			3.1 Construct the Loss Function
			3.2 Extra Penalties and Regularization
		4 Discussion
		References
Part II Network Analysis
	Probabilistic Graphical Models for Gene Regulatory Networks
		1 Introduction
		2 Probabilistic Graphical Models
			2.1 Graphical Model Basics
			2.2 Markov Networks
			2.3 Bayesian Networks
		3 Classic Graphical Models for Reconstructing GRNs
			3.1 Frequentist Approach
			3.2 Bayesian Approach
			3.3 Graphical Models Incorporating Prior Knowledge
		4 Testing in Graphical Models
			4.1 Parametric Test
			4.2 Non-parametric Test for Global Graph Structure
		5 Conclusion
		References
	Additive Conditional Independence for Large and Complex Biological Structures
		1 Additive Conditional Independence (ACI)
			1.1 Additive Reproducing Kernel Hilbert Spaces and Relevant Linear Operators
		2 Variable Selection via ACI
			2.1 Nonparametric Variable Selection
			2.2 Penalized Least-Square Estimation with RKHS Operators
			2.3 Matrix Representation of Operators and Algorithm
			2.4 Data Example
		3 Graphical Modeling Through ACI
			3.1 Nonparametric Graphical Models
			3.2 The Additive Conditional Covariance and Partial Correlation Operators
			3.3 Operator-Level Estimation and the Algorithm
			3.4 Data Examples
		References
	Integration of Boolean and Bayesian Networks
		1 Introduction
		2 Methods
			2.1 s-p-scores Associated with Networks, SPAN
			2.2 Network Learning
		3 Results
			3.1 An Example
			3.2 Real Example
			3.3 Complex Example
		4 Discussion
		References
	Computational Methods for Identifying MicroRNA-Gene Regulatory Modules
		1 Introduction
		2 Identifying MiRNA-Gene Modules by Integrating Heterogeneous Data Sources
			2.1 Bipartite Graph-Based Methods
			2.2 Nonnegative Matrix Factorization Methods
			2.3 Statistical Modeling Approaches
		3 Evaluating the Performance of MiRNA-Gene Module Identification Methods
		4 Discussion
		5 Conclusions
		References
	Causal Inference in Biostatistics
		1 Introduction
			1.1 Causation and Association
			1.2 Two Conceptual Frameworks: Causal Effect and Causal Discovery
		2 Causal Effect
			2.1 Approaches to Causal Inference
			2.2 Randomized Clinical Trials
				2.2.1 Perfect Randomized Trials
				2.2.2 Randomized Trials with Missing Data
				2.2.3 Randomized Trials with Post-treatment Variables
			2.3 Observational Studies
				2.3.1 Unconfounded Treatment Assignment Conditional on Measured Covariates
				2.3.2 Unmeasured Cofounding
		3 Some Current Research Topics
			3.1 Heterogenous Treatment Effect and Precision Medicine
			3.2 Integrating Data from Randomized Controlled Trials and Observational Studies
			3.3 Multiple Treatments
		4 Software Appendix
		References
	Bayesian Balance Mediation Analysis in Microbiome Studies
		1 Introduction
		2 Bayesian Balance Mediation Model
			2.1 Bayesian Balance Mediation Model with a Binary Treatment
			2.2 Direct and Mediation Effect and Estimation Based on Predictive Posterior Distribution
		3 MCMC Sampling
			3.1 MCMC Sampling
			3.2 Conditional Distributions
		4 Applications to Real Data
			4.1 Mediation Analysis at the Phylum Level
			4.2 Analysis at the Order Level
		5 Simulation Studies
			5.1 Data Generation
			5.2 Simulation Result
		6 Discussion
		References
Part III Systems Biology
	Identifying Genetic Loci Associated with Complex Trait Variability
		1 Introduction
		2 The Concept of vQTL
		3 Statistical Methods for vQTL Mapping
			3.1 Classical Nonparametric Tests
			3.2 Regression-Based Methods
			3.3 Two-Stage Methods
			3.4 Quantile Integral Linear Model (QUAIL)
			3.5 Dispersion Effects
		4 Applications of vQTL
			4.1 Examples of vQTL
			4.2 Screening vQTL for Candidate Loci Involved in GxE Interaction
			4.3 Variance Polygenic Score
			4.4 Other Applications and Future Directions
		References
	Cell Type-Specific Analysis for High-throughput Data
		1 Introduction
		2 Cell Type Composition Estimation
		3 Cell Type-Specific Differential Analysis
		4 Step-by-step Tutorial
		References
	Recent Development of Computational Methods in the Field of Epitranscriptomics
		1 Introduction
		2 MeRIP-seq and Other Technologies for RNA Modification Profiling
		3 Methods to Analyze MeRIP-seq Data
			3.1 Count-Based Methods for Simple Study Designs
			3.2 Methods Compatible with Confounding Factors
			3.3 A Guide for RNA Differential Methylation Analysis Using RADAR
		4 Web Resources on m6A Epitranscriptome
			4.1 Web Servers with m6A Site Prediction
			4.2 m6A Epitranscriptome Database
		5 Discussion
		References
	Estimation of Tumor Immune Signatures from Transcriptomics Data
		1 Introduction
		2 Regression-Based Deconvolution Algorithms
			2.1 Linear Least Squares Regression
			2.2 Support Vector Regression
			2.3 Other Deconvolution Methods
		3 Gene Set Enrichment-Based Methods and Other Gene-Based Algorithms
			3.1 Gene Set Enrichment Analysis (GSEA)
			3.2 Single-Sample GSEA (ssGSEA)
		4 Benchmark Studies
		5 Discussions
		References
	Cross-Linking Mass Spectrometry Data Analysis
		1 Introduction
			1.1 Peptide Identification Based on Mass Spectrometry
			1.2 Cross-Linked Peptides Identification
				1.2.1 Cross-Linker Selection
				1.2.2 Chemical Reaction
				1.2.3 Enzyme Digestion
				1.2.4 Enrichment of Cross-Linked Peptides
				1.2.5 LC-MS and MS2 Acquisition
				1.2.6 Data Interpretation
				1.2.7 Quality Control
				1.2.8 Downstream Applications
		2 Non-cleavable Cross-Linking Methods
		3 Cleavable Cross-Linking Methods
		4 Time-Complexity Comparison Between Non-cleavable Methods and Cleavable Methods
		5 False Discovery Rate in CL-MS
			5.1 Target–Decoy Approach in Linear Peptides Identification
			5.2 TDA in Cross-Linked Peptides Identification
		6 Downstream Applications
			6.1 Protein Structure Analysis
			6.2 Protein–Protein Interactions
		7 Conclusion and Perspective
			7.1 Wet Lab Experiments
			7.2 Dry Lab Analysis
		References
	Cis-regulatory Element Frequency Modules and their Phase Transition across Hominidae
		1 Introduction
		2 Dual Eigen-Analysis of CREF Matrices
			2.1 Cis-regulatory Element Frequency (CREF) Matrices
			2.2 Dual Eigen-Analysis
				2.2.1 Low-Rank Matrix Approximation
				2.2.2 Robust SVD
				2.2.3 Dual CREF Eigen-Modules
				2.2.4 Cross-Species Correlations and Conservation of Motif Eigen-Modules
				2.2.5 Enrichment Analysis of Gene Eigen-Modules
			2.3 Stability of CREF Modules
				2.3.1 Sensitivity of Eigenvalues and Eigenvectors
				2.3.2 Degenerate 2-D Eigen-Space and Its Stability
		3 Dual Eigen-Analysis Unravels the Regulatory Evolution from Apes to Human
			3.1 Evolutionary Conservation and Divergence of CREF Eigen-Modules
			3.2 Conservation of Top Three CREF Modules
			3.3 Dual Eigen-Analysis Identifies the Regulators Involved in Major Biological Processes
			3.4 Phase Transition of CREF Eigen-Modules
			3.5 Human-Specific Phenotypes Corresponding to Its Unique Fourth Gene Eigenvector
			3.6 Motifs Present on Alu Elements Underlying the CREF Module Reorganization
			3.7 Cis-trans Regulation of the Human-Specific Gene Eigen-Module
		4 Discussion
			4.1 Definition of cis-regulatory Profiles
			4.2 Dual Eigen-Analysis and the Classical PCA
			4.3 The Phase Transition in cis-regulatory Evolution
			4.4 Conclusion
		References
	Improved Method for Rooting and Tip-Dating a Viral Phylogeny
		1 Introduction
			1.1 The Need for Answering the “When” Question Without a Good Outgroup
			1.2 Conventional TRAD Methods Assume a Constant  and Need to Be Extended to Allow  to Change over Time
		2 TRAD: Tip Rooting and Ancestor Dating
			2.1 Estimating the Root
			2.2 Dating the Common Ancestor (TA) and Estimating the Evolutionary Rate
		3 Increasing Evolutionary Rate During SARS-CoV-2 Evolution over Time
			3.1 Results from the Viral Phylogeny Released by NCBI on April 3, 2021
			3.2 Results from the Viral Phylogeny Released by NCBI on September 4, 2021
		4 The Difficulty with the “Where” Question
		5 Conclusions
		References