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Advances in statistical bioinformatics : models and integrative inference for high-throughput data / / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX [[electronic resource]]
| Advances in statistical bioinformatics : models and integrative inference for high-throughput data / / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX [[electronic resource]] |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2013 |
| Descrizione fisica | 1 online resource (xv, 481 pages) : digital, PDF file(s) |
| Disciplina | 572.80285 |
| Soggetto topico |
Bioinformatics - Statistical methods
Biometry Genetics - Technique |
| ISBN |
1-139-89118-9
1-107-24941-4 1-107-24858-2 1-299-70751-3 1-107-25107-9 1-107-25024-2 1-139-22644-4 1-107-24775-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Contents""; ""List of Contributors""; ""Preface""; ""1 An Introduction to Next-Generation Biological Platforms""; ""Virginia Mohlere, Wenting Wang, and Ganiraju Manyam""; ""1.1 Introduction""; ""1.2 The Biology of Gene Silencing""; ""1.2.1 DNA Methylation""; ""1.2.2 RNA Interference""; ""1.3 High-Throughput Profiling""; ""1.3.1 Molecular Inversion Probe Arrays""; ""1.3.2 Array Comparative Genomic Hybridization (aCGH)""; ""1.3.3 Genome-Wide Association Studies""; ""1.3.4 Reverse-Phase Protein Array""; ""1.4 Next-Generation Sequencing""; ""1.4.1 Whole-Genome and Whole-Exome Sequencing""
""1.4.2 ChIP-Seq""""1.4.3 RNA-Seq""; ""1.4.4 BS-seq""; ""1.5 NGS Data Management and Analysis""; ""1.6 Platform Integration""; ""Acknowledgments""; ""References""; ""References""; ""2 An Introduction to The Cancer Genome Atlas""; ""Bradley M. Broom and Rehan Akbani""; ""2.1 Introduction""; ""2.2 History and Goals of the TCGA Project""; ""2.3 Sample Collection and Processing""; ""2.3.1 Step 1: Tissue Collection""; ""2.3.2 Step 2: Quality Control and DNA/RNA Extraction""; ""2.3.3 Step 3: Molecular Profiling and Sequencing""; ""2.3.4 Step 4: Data Collection and Public Distribution"" ""2.3.5 Step 5: Data Analysis""""2.4 Data Processing, Storage, and Access""; ""2.4.1 TCGA Barcodes and UUIDs""; ""2.4.2 The Data Coordinating Center""; ""2.4.3 Data Access Matrix""; ""2.4.4 Bulk Download""; ""2.4.5 HTTP""; ""2.4.6 CGHub""; ""2.4.7 Sample and Data Relationship Format (SDRF) and Investigation Description Format (IDF) Files""; ""2.4.8 File Format""; ""2.4.9 Version""; ""2.5 Tools for Visualizing and Analyzing TCGA Data""; ""2.5.1 cBio Cancer Genomics Portal""; ""2.5.2 MBatch Portal""; ""2.5.3 Next-Generation Clustered Heat Maps""; ""2.5.4 Regulome Explorer"" ""2.5.5 Integrative Genome Viewer""""2.5.6 Cancer Genomics Browser""; ""2.6 Summary""; ""Acknowledgments""; ""References""; ""References""; ""3 DNA Variant Calling in Targeted Sequencing Data""; ""Wenyi Wang, Yu Fan, and Terence P. Speed""; ""3.1 Introduction""; ""3.2 Background""; ""3.2.1 Single-Nucleotide Variation""; ""3.2.2 Long Padlock Probes""; ""3.2.3 Array-Based Resequencing""; ""3.3 Sequence Robust Multiarray Analysis""; ""3.3.1 Quality Control""; ""3.3.2 Variant Calling""; ""3.4 Application of SRMA""; ""3.4.1 Candidate Gene Study for Mitochondrial Diseases"" ""3.4.2 Validation Results""""3.4.3 Biological Findings""; ""3.5 Conclusion""; ""Appendix""; ""References""; ""References""; ""4 Statistical Analysis of Mapped Reads from mRNA-Seq Data""; ""Ernest Turro and Alex Lewin""; ""4.1 Background""; ""4.1.1 RNA Biology""; ""4.1.2 RNA Technology""; ""4.2 Mapping and Assembly Strategies""; ""4.2.1 De Novo Assembly of the Transcriptome""; ""4.2.2 Genome-Guided Assembly of the Transcriptome""; ""4.2.3 Alignment to a Reference Transcriptome""; ""4.3 Modeling Expression Levels""; ""4.3.1 Poisson Model for Expression Quantification""; ""4.4 Normalization"" ""4.4.1 RPKM Normalization"" |
| Record Nr. | UNINA-9910464935503321 |
| Cambridge : , : Cambridge University Press, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in statistical bioinformatics : models and integrative inference for high-throughput data / / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX [[electronic resource]]
| Advances in statistical bioinformatics : models and integrative inference for high-throughput data / / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX [[electronic resource]] |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2013 |
| Descrizione fisica | 1 online resource (xv, 481 pages) : digital, PDF file(s) |
| Disciplina | 572.80285 |
| Soggetto topico |
Bioinformatics - Statistical methods
Biometry Genetics - Technique |
| ISBN |
1-139-89118-9
1-107-24941-4 1-107-24858-2 1-299-70751-3 1-107-25107-9 1-107-25024-2 1-139-22644-4 1-107-24775-6 |
| Classificazione | MED090000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Contents""; ""List of Contributors""; ""Preface""; ""1 An Introduction to Next-Generation Biological Platforms""; ""Virginia Mohlere, Wenting Wang, and Ganiraju Manyam""; ""1.1 Introduction""; ""1.2 The Biology of Gene Silencing""; ""1.2.1 DNA Methylation""; ""1.2.2 RNA Interference""; ""1.3 High-Throughput Profiling""; ""1.3.1 Molecular Inversion Probe Arrays""; ""1.3.2 Array Comparative Genomic Hybridization (aCGH)""; ""1.3.3 Genome-Wide Association Studies""; ""1.3.4 Reverse-Phase Protein Array""; ""1.4 Next-Generation Sequencing""; ""1.4.1 Whole-Genome and Whole-Exome Sequencing""
""1.4.2 ChIP-Seq""""1.4.3 RNA-Seq""; ""1.4.4 BS-seq""; ""1.5 NGS Data Management and Analysis""; ""1.6 Platform Integration""; ""Acknowledgments""; ""References""; ""References""; ""2 An Introduction to The Cancer Genome Atlas""; ""Bradley M. Broom and Rehan Akbani""; ""2.1 Introduction""; ""2.2 History and Goals of the TCGA Project""; ""2.3 Sample Collection and Processing""; ""2.3.1 Step 1: Tissue Collection""; ""2.3.2 Step 2: Quality Control and DNA/RNA Extraction""; ""2.3.3 Step 3: Molecular Profiling and Sequencing""; ""2.3.4 Step 4: Data Collection and Public Distribution"" ""2.3.5 Step 5: Data Analysis""""2.4 Data Processing, Storage, and Access""; ""2.4.1 TCGA Barcodes and UUIDs""; ""2.4.2 The Data Coordinating Center""; ""2.4.3 Data Access Matrix""; ""2.4.4 Bulk Download""; ""2.4.5 HTTP""; ""2.4.6 CGHub""; ""2.4.7 Sample and Data Relationship Format (SDRF) and Investigation Description Format (IDF) Files""; ""2.4.8 File Format""; ""2.4.9 Version""; ""2.5 Tools for Visualizing and Analyzing TCGA Data""; ""2.5.1 cBio Cancer Genomics Portal""; ""2.5.2 MBatch Portal""; ""2.5.3 Next-Generation Clustered Heat Maps""; ""2.5.4 Regulome Explorer"" ""2.5.5 Integrative Genome Viewer""""2.5.6 Cancer Genomics Browser""; ""2.6 Summary""; ""Acknowledgments""; ""References""; ""References""; ""3 DNA Variant Calling in Targeted Sequencing Data""; ""Wenyi Wang, Yu Fan, and Terence P. Speed""; ""3.1 Introduction""; ""3.2 Background""; ""3.2.1 Single-Nucleotide Variation""; ""3.2.2 Long Padlock Probes""; ""3.2.3 Array-Based Resequencing""; ""3.3 Sequence Robust Multiarray Analysis""; ""3.3.1 Quality Control""; ""3.3.2 Variant Calling""; ""3.4 Application of SRMA""; ""3.4.1 Candidate Gene Study for Mitochondrial Diseases"" ""3.4.2 Validation Results""""3.4.3 Biological Findings""; ""3.5 Conclusion""; ""Appendix""; ""References""; ""References""; ""4 Statistical Analysis of Mapped Reads from mRNA-Seq Data""; ""Ernest Turro and Alex Lewin""; ""4.1 Background""; ""4.1.1 RNA Biology""; ""4.1.2 RNA Technology""; ""4.2 Mapping and Assembly Strategies""; ""4.2.1 De Novo Assembly of the Transcriptome""; ""4.2.2 Genome-Guided Assembly of the Transcriptome""; ""4.2.3 Alignment to a Reference Transcriptome""; ""4.3 Modeling Expression Levels""; ""4.3.1 Poisson Model for Expression Quantification""; ""4.4 Normalization"" ""4.4.1 RPKM Normalization"" |
| Record Nr. | UNINA-9910789313903321 |
| Cambridge : , : Cambridge University Press, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in statistical bioinformatics : models and integrative inference for high-throughput data / / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX [[electronic resource]]
| Advances in statistical bioinformatics : models and integrative inference for high-throughput data / / edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX [[electronic resource]] |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2013 |
| Descrizione fisica | 1 online resource (xv, 481 pages) : digital, PDF file(s) |
| Disciplina | 572.80285 |
| Soggetto topico |
Bioinformatics - Statistical methods
Biometry Genetics - Technique |
| ISBN |
1-139-89118-9
1-107-24941-4 1-107-24858-2 1-299-70751-3 1-107-25107-9 1-107-25024-2 1-139-22644-4 1-107-24775-6 |
| Classificazione | MED090000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Contents""; ""List of Contributors""; ""Preface""; ""1 An Introduction to Next-Generation Biological Platforms""; ""Virginia Mohlere, Wenting Wang, and Ganiraju Manyam""; ""1.1 Introduction""; ""1.2 The Biology of Gene Silencing""; ""1.2.1 DNA Methylation""; ""1.2.2 RNA Interference""; ""1.3 High-Throughput Profiling""; ""1.3.1 Molecular Inversion Probe Arrays""; ""1.3.2 Array Comparative Genomic Hybridization (aCGH)""; ""1.3.3 Genome-Wide Association Studies""; ""1.3.4 Reverse-Phase Protein Array""; ""1.4 Next-Generation Sequencing""; ""1.4.1 Whole-Genome and Whole-Exome Sequencing""
""1.4.2 ChIP-Seq""""1.4.3 RNA-Seq""; ""1.4.4 BS-seq""; ""1.5 NGS Data Management and Analysis""; ""1.6 Platform Integration""; ""Acknowledgments""; ""References""; ""References""; ""2 An Introduction to The Cancer Genome Atlas""; ""Bradley M. Broom and Rehan Akbani""; ""2.1 Introduction""; ""2.2 History and Goals of the TCGA Project""; ""2.3 Sample Collection and Processing""; ""2.3.1 Step 1: Tissue Collection""; ""2.3.2 Step 2: Quality Control and DNA/RNA Extraction""; ""2.3.3 Step 3: Molecular Profiling and Sequencing""; ""2.3.4 Step 4: Data Collection and Public Distribution"" ""2.3.5 Step 5: Data Analysis""""2.4 Data Processing, Storage, and Access""; ""2.4.1 TCGA Barcodes and UUIDs""; ""2.4.2 The Data Coordinating Center""; ""2.4.3 Data Access Matrix""; ""2.4.4 Bulk Download""; ""2.4.5 HTTP""; ""2.4.6 CGHub""; ""2.4.7 Sample and Data Relationship Format (SDRF) and Investigation Description Format (IDF) Files""; ""2.4.8 File Format""; ""2.4.9 Version""; ""2.5 Tools for Visualizing and Analyzing TCGA Data""; ""2.5.1 cBio Cancer Genomics Portal""; ""2.5.2 MBatch Portal""; ""2.5.3 Next-Generation Clustered Heat Maps""; ""2.5.4 Regulome Explorer"" ""2.5.5 Integrative Genome Viewer""""2.5.6 Cancer Genomics Browser""; ""2.6 Summary""; ""Acknowledgments""; ""References""; ""References""; ""3 DNA Variant Calling in Targeted Sequencing Data""; ""Wenyi Wang, Yu Fan, and Terence P. Speed""; ""3.1 Introduction""; ""3.2 Background""; ""3.2.1 Single-Nucleotide Variation""; ""3.2.2 Long Padlock Probes""; ""3.2.3 Array-Based Resequencing""; ""3.3 Sequence Robust Multiarray Analysis""; ""3.3.1 Quality Control""; ""3.3.2 Variant Calling""; ""3.4 Application of SRMA""; ""3.4.1 Candidate Gene Study for Mitochondrial Diseases"" ""3.4.2 Validation Results""""3.4.3 Biological Findings""; ""3.5 Conclusion""; ""Appendix""; ""References""; ""References""; ""4 Statistical Analysis of Mapped Reads from mRNA-Seq Data""; ""Ernest Turro and Alex Lewin""; ""4.1 Background""; ""4.1.1 RNA Biology""; ""4.1.2 RNA Technology""; ""4.2 Mapping and Assembly Strategies""; ""4.2.1 De Novo Assembly of the Transcriptome""; ""4.2.2 Genome-Guided Assembly of the Transcriptome""; ""4.2.3 Alignment to a Reference Transcriptome""; ""4.3 Modeling Expression Levels""; ""4.3.1 Poisson Model for Expression Quantification""; ""4.4 Normalization"" ""4.4.1 RPKM Normalization"" |
| Record Nr. | UNINA-9910810575303321 |
| Cambridge : , : Cambridge University Press, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
| Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Berlin : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (406 pages) |
| Disciplina | 570.285 |
| Collana | Springer Handbooks of Computational Statistics |
| Soggetto topico |
Bioinformatics - Statistical methods
Bioinformatics Bioinformàtica Biologia computacional Informàtica mèdica Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-65902-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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. |
| Record Nr. | UNINA-9910634054103321 |
| Berlin : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
| Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Berlin : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (406 pages) |
| Disciplina | 570.285 |
| Collana | Springer Handbooks of Computational Statistics |
| Soggetto topico |
Bioinformatics - Statistical methods
Bioinformatics Bioinformàtica Biologia computacional Informàtica mèdica Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-65902-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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. |
| Record Nr. | UNISA-996503551803316 |
| Berlin : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Health informatics . Part 20601 Application profile, optimized exchange protocol : corrigendum 1 : personal health device communication / / Institute of Electrical and Electronics Engineers
| Health informatics . Part 20601 Application profile, optimized exchange protocol : corrigendum 1 : personal health device communication / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, NJ : , : IEEE, , 2015 |
| Descrizione fisica | 1 online resource |
| Disciplina | 570.285 |
| Soggetto topico | Bioinformatics - Statistical methods |
| ISBN | 0-7381-9995-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
11073-20601-2014/Cor 1-2015 - IEEE Health informatics--Personal health device communication Part 20601
IEEE Std 11073-20601-2014/Cor 1-2015 (Corrigendum to IEEE Std 11073-20601-2014): IEEE Health informatics--Personal health device communication Part 20601: Application profile--Optimized Exchange Protocol - Corrigendum 1 IEEE Std 11073-20601-2014/Cor 1-2015 IEEE Health informatics--Personal health device communication Part 20601 |
| Record Nr. | UNISA-996280507503316 |
| Piscataway, NJ : , : IEEE, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Health informatics . Part 20601 Application profile, optimized exchange protocol : corrigendum 1 : personal health device communication / / Institute of Electrical and Electronics Engineers
| Health informatics . Part 20601 Application profile, optimized exchange protocol : corrigendum 1 : personal health device communication / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, NJ : , : IEEE, , 2015 |
| Descrizione fisica | 1 online resource |
| Disciplina | 570.285 |
| Soggetto topico | Bioinformatics - Statistical methods |
| ISBN | 0-7381-9995-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
11073-20601-2014/Cor 1-2015 - IEEE Health informatics--Personal health device communication Part 20601
IEEE Std 11073-20601-2014/Cor 1-2015 (Corrigendum to IEEE Std 11073-20601-2014): IEEE Health informatics--Personal health device communication Part 20601: Application profile--Optimized Exchange Protocol - Corrigendum 1 IEEE Std 11073-20601-2014/Cor 1-2015 IEEE Health informatics--Personal health device communication Part 20601 |
| Record Nr. | UNINA-9910135417303321 |
| Piscataway, NJ : , : IEEE, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical bioinformatics [[electronic resource] ] : a guide for life and biomedical science researchers / / edited by Jae K. Lee
| Statistical bioinformatics [[electronic resource] ] : a guide for life and biomedical science researchers / / edited by Jae K. Lee |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2010 |
| Descrizione fisica | 1 online resource (386 p.) |
| Disciplina | 570.285 |
| Altri autori (Persone) | LeeJae K |
| Soggetto topico |
Bioinformatics - Statistical methods
Biology - Data processing |
| ISBN |
1-118-21152-9
1-282-69036-1 9786612690365 0-470-56764-3 0-470-56763-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
STATISTICAL BIOINFORMATICS; CONTENTS; PREFACE; CONTRIBUTORS; 1 ROAD TO STATISTICAL BIOINFORMATICS; 2 PROBABILITY CONCEPTS AND DISTRIBUTIONS FOR ANALYZING LARGE BIOLOGICAL DATA; 3 QUALITY CONTROL OF HIGH-THROUGHPUT BIOLOGICAL DATA; 4 STATISTICAL TESTING AND SIGNIFICANCE FOR LARGE BIOLOGICAL DATA ANALYSIS; 5 CLUSTERING: UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA; 6 CLASSIFICATION: SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA; 7 MULTIDIMENSIONAL ANALYSIS AND VISUALIZATION ON LARGE BIOMEDICAL DATA; 8 STATISTICAL MODELS, INFERENCE, AND ALGORITHMS FOR LARGE BIOLOGICAL DATA ANALYSIS
9 EXPERIMENTAL DESIGNS ON HIGH-THROUGHPUT BIOLOGICAL EXPERIMENTS10 STATISTICAL RESAMPLING TECHNIQUES FOR LARGE BIOLOGICAL DATA ANALYSIS; 11 STATISTICAL NETWORK ANALYSIS FOR BIOLOGICAL SYSTEMS AND PATHWAYS; 12 TRENDS AND STATISTICAL CHALLENGES IN GENOMEWIDE ASSOCIATION STUDIES; 13 R AND BIOCONDUCTOR PACKAGES IN BIOINFORMATICS: TOWARDS SYSTEMS BIOLOGY; INDEX |
| Record Nr. | UNINA-9910140591203321 |
| Hoboken, NJ, : Wiley, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical bioinformatics : a guide for life and biomedical science researchers / / edited by Jae K. Lee
| Statistical bioinformatics : a guide for life and biomedical science researchers / / edited by Jae K. Lee |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2010 |
| Descrizione fisica | 1 online resource (386 p.) |
| Disciplina | 570.285 |
| Altri autori (Persone) | LeeJae K |
| Soggetto topico |
Bioinformatics - Statistical methods
Biology - Data processing |
| ISBN |
1-118-21152-9
1-282-69036-1 9786612690365 0-470-56764-3 0-470-56763-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
STATISTICAL BIOINFORMATICS; CONTENTS; PREFACE; CONTRIBUTORS; 1 ROAD TO STATISTICAL BIOINFORMATICS; 2 PROBABILITY CONCEPTS AND DISTRIBUTIONS FOR ANALYZING LARGE BIOLOGICAL DATA; 3 QUALITY CONTROL OF HIGH-THROUGHPUT BIOLOGICAL DATA; 4 STATISTICAL TESTING AND SIGNIFICANCE FOR LARGE BIOLOGICAL DATA ANALYSIS; 5 CLUSTERING: UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA; 6 CLASSIFICATION: SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA; 7 MULTIDIMENSIONAL ANALYSIS AND VISUALIZATION ON LARGE BIOMEDICAL DATA; 8 STATISTICAL MODELS, INFERENCE, AND ALGORITHMS FOR LARGE BIOLOGICAL DATA ANALYSIS
9 EXPERIMENTAL DESIGNS ON HIGH-THROUGHPUT BIOLOGICAL EXPERIMENTS10 STATISTICAL RESAMPLING TECHNIQUES FOR LARGE BIOLOGICAL DATA ANALYSIS; 11 STATISTICAL NETWORK ANALYSIS FOR BIOLOGICAL SYSTEMS AND PATHWAYS; 12 TRENDS AND STATISTICAL CHALLENGES IN GENOMEWIDE ASSOCIATION STUDIES; 13 R AND BIOCONDUCTOR PACKAGES IN BIOINFORMATICS: TOWARDS SYSTEMS BIOLOGY; INDEX |
| Record Nr. | UNINA-9910814421103321 |
| Hoboken, NJ, : Wiley, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics for bioinformatics : methods for multiple sequence alignment / / Julie Dawn Thompson
| Statistics for bioinformatics : methods for multiple sequence alignment / / Julie Dawn Thompson |
| Autore | Thompson Julie Dawn |
| Pubbl/distr/stampa | London, England ; ; Oxford, England : , : iSTE Press : , : Elsevier, , 2016 |
| Descrizione fisica | 1 online resource (148 pages) : illustrations |
| Disciplina | 570.285 |
| Soggetto topico | Bioinformatics - Statistical methods |
| ISBN | 0-08-101961-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910583056703321 |
Thompson Julie Dawn
|
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| London, England ; ; Oxford, England : , : iSTE Press : , : Elsevier, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
