Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advances in Plant Microbiome and Sustainable Agriculture : Diversity and Biotechnological Applications / / edited by Ajar Nath Yadav, Ali Asghar Rastegari, Neelam Yadav, Divjot Kour
| Advances in Plant Microbiome and Sustainable Agriculture : Diversity and Biotechnological Applications / / edited by Ajar Nath Yadav, Ali Asghar Rastegari, Neelam Yadav, Divjot Kour |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXIII, 296 p. 36 illus., 28 illus. in color.) |
| Disciplina | 579.178 |
| Collana | Microorganisms for Sustainability |
| Soggetto topico |
Agriculture
Plant breeding Microbial ecology Microbial genetics Microbial genomics Genètica vegetal Agricultura sostenible Genètica microbiana Plant Breeding/Biotechnology Microbial Ecology Microbial Genetics and Genomics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-15-3208-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Plant-Microbes Interaction: Current Developments and Future Challenges -- Chapter 2. Rhizospheric Microbiomes: Biodiversity, Current Advancement, and Potential Biotechnological Application -- Chapter 3. Endophytic Microbiomes: Biodiversity, Current Status, and Potential Agricultural Applications -- Chapter 4. Culturable Plant-Associated Endophytic Microbial Communities from Leguminous and Non-Leguminous Crops -- Chapter 5. Arbuscular Mycorrhizal Fungi: Abundance, Interaction with Plants and Potential Biological Application -- Chapter 6. Endophytic Microbiomes and their Plant Growth Promoting Attributes for Plant Health -- Chapter 7. Diversity and Biotechnological Potential of Culturable Rhizospheric Actinomicrobiota -- Chapter 8. Bacillus and Endomicrobiome: Biodiversity and potential Applications in Agriculture -- Chapter 9. Role of Microbes in Improving Plant Growth and Soil Health for Sustainable Agriculture -- Chapter 10. Biofertilizers and Biopesticides: Microbes for Sustainable Agriculture. |
| Record Nr. | UNINA-9910416104403321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Plant Microbiome and Sustainable Agriculture : Functional Annotation and Future Challenges / / edited by Ajar Nath Yadav, Ali Asghar Rastegari, Neelam Yadav, Divjot Kour
| Advances in Plant Microbiome and Sustainable Agriculture : Functional Annotation and Future Challenges / / edited by Ajar Nath Yadav, Ali Asghar Rastegari, Neelam Yadav, Divjot Kour |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXII, 278 p. 31 illus., 29 illus. in color.) |
| Disciplina | 306.4409113 |
| Collana | Microorganisms for Sustainability |
| Soggetto topico |
Agriculture
Microbial ecology Microbial genetics Microbial genomics Plant breeding Genètica vegetal Genètica microbiana Agricultura sostenible Microbial Ecology Microbial Genetics and Genomics Plant Breeding/Biotechnology |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-15-3204-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Phosphorus Solubilization and Mobilization: Mechanisms, Current Developments and Future Challenge -- Chapter 2. Potassium Solubilization and Mobilization: Functional Impact on Plant Growth for Sustainable Agriculture -- Chapter 3. Zinc Solubilization and Mobilization: A Promising Approach for Cereals Biofortification -- Chapter 4. Microbial ACC-deaminase attributes: perspectives and applications in stress agriculture -- Chapter 5. Plant Microbiomes with Phytohormones Attribute for Plant Growth and Adaptation under the Stress Conditions -- Chapter 6. Mechanisms of Plant Growth Promotion and Functional Annotation in Mitigation of Abiotic Stress -- Chapter 7. Microbiomes Associated with Plant Growing Under the Hypersaline Habitats and Mitigation of Salt Stress -- Chapter 8. Alleviation of Cold Stress by Psychrotrophic Microbes -- Chapter 9. Microbes-Mediated Mitigation of Drought Stress in Plants: Recent Trends and Future Challenges -- Chapter 10. Microbial Consortium with Multifunctional Plant Growth Promoting Attributes: Future Perspective in Agriculture -- Chapter 11. Cyanobacteria as Biofertilizers: Current Research, Commercial Aspects, and Future Challenges. |
| Record Nr. | UNINA-9910416102803321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fungal Biotechnology and Bioengineering / / edited by Abd El-Latif Hesham, Ram Sanmukh Upadhyay, Gauri Dutt Sharma, Chakravarthula Manoharachary, Vijai Kumar Gupta
| Fungal Biotechnology and Bioengineering / / edited by Abd El-Latif Hesham, Ram Sanmukh Upadhyay, Gauri Dutt Sharma, Chakravarthula Manoharachary, Vijai Kumar Gupta |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (493 pages) |
| Disciplina | 660.62 |
| Collana | Fungal Biology |
| Soggetto topico |
Mycology
Plant breeding Microbial genetics Microbial genomics Botanical chemistry Plant Breeding/Biotechnology Microbial Genetics and Genomics Plant Biochemistry Biotecnologia agrícola Bioenginyeria Genètica microbiana |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-41870-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword -- Fungal Epigenetic Engineering -- Yeast engineering for new antifungal compounds: a contextualized overview -- G protein-coupled receptors in fungi -- Prompt and convenient preparation of oral vaccines using yeast cell surface display -- Trichoderma a factory of multipurpose enzymes -- Advances genomic approaches for mining fungal genes for biofuels -- Genetically modified fungi for second generation bioethanol production -- Fungal Bioengineering in Biodiesel Production -- Bioengineering fungi and yeast for the production of enzymes, metabolites and value-added compounds -- Fungal production of prebiotics -- Fermentative production of secondary metabolites from Bioengineered Fungi and their applications -- Recent progress on Trichoderma secondary metabolites -- Fungal genes encoding enzymes that are used in cheese production and fermentation industries -- Unravelling the potentials of endophytes and its applications -- Fungal genes and gene products involved in wastewater treatments -- DNA Barcode for Species Identification in Fungi -- Current progress on endophytic microbial dynamics on Dendrobium plants.-Understanding its role bioengineered Trichoderma in managing soil-borne plant diseases and its other benefits -- Beyond classical biocontrol: new perspectives on Trichoderma -- Systemic acquired resistance (SAR) and Induced systemic resistance (ISR) mechanism of Trichoderma against pathogens -- Index. |
| Record Nr. | UNINA-9910409683003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Microbial Genomics in Sustainable Agroecosystems : Volume 1 / / edited by Vijay Tripathi, Pradeep Kumar, Pooja Tripathi, Amit Kishore
| Microbial Genomics in Sustainable Agroecosystems : Volume 1 / / edited by Vijay Tripathi, Pradeep Kumar, Pooja Tripathi, Amit Kishore |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIV, 285 p. 33 illus., 27 illus. in color.) |
| Disciplina | 579.135 |
| Soggetto topico |
Microbial genetics
Microbial genomics Sustainable development Microbial ecology Agriculture Biotic communities Microbial Genetics and Genomics Sustainable Development Microbial Ecology Ecosystems Genètica microbiana |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-13-8739-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Functional genomics and System biology approach in bioremediation of soil and water from organic and inorganic pollutants -- 2. Mycorrhiza and plant disease control -- 3. Mycovirus and the genome of plant fungi pathogen -- 4. Bacterial Pan-genomics -- 5. Phyllosphere and its potential role in sustainable agriculture -- 6. Functional genomics and systems biology approach for understanding Agroecosystems -- 7. Advancements in Microbial Genome Sequencing and Microbial Community Characterization -- 8. Bioinformatics and microarray-based technologies to Viral genome sequence analysis -- 9. Application of whole genome sequencing (WGS) approach against identification of foodborne bacteria -- 10. Functional metagenomics for rhizospheric soil in agricultural systems -- 11. Microbial genomics in carbon management and energy production -- 12. Microbial genome diversity and microbial genome sequencing -- 13. Molecular Techniques for the Identification and Genotyping of Microorganisms -- 14. RNA-guided CRISPR-Cas9 System for Removal of Microbial Pathogens -- 15. Biosorption-cum-bioaccumulation of heavy metals from industrial effluent by brown algae-Deep insight -- 16. Linking microbial genomics to renewable energy production and global carbon management. |
| Record Nr. | UNINA-9910373919603321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors
| Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (348 pages) |
| Disciplina | 579 |
| Collana | Frontiers in probability and the statistical sciences |
| Soggetto topico |
Microbiology - Statistical methods
Microbiologia Estadística matemàtica Microorganismes Genètica microbiana |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-73351-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Part I Preprocessing and Bioinformatics Pipelines -- Denoising Methods for Inferring Microbiome Community Content and Abundance -- 1 Introduction -- 2 Common Algorithmic Denoising Strategies -- 3 Model-Based Denoising -- 3.1 Hierarchical Divisive Clustering -- 3.2 Finite Mixture Model -- 3.3 Denoising Long-Read Technology -- 4 Model Assessment -- 4.1 With Known Truth -- 4.1.1 Accuracy in ASV Identification -- 4.1.2 Accuracy in Read Assignments -- 4.2 With Unknown Truth -- 4.2.1 Assessment with UMIs -- 4.2.2 Clustering Stability -- 5 Conclusions -- References -- Statistical and Computational Methods for Analysis of Shotgun Metagenomics Sequencing Data -- 1 Introduction -- 2 Methods for Species Identification and Quantification of Microorganisms -- 3 Metagenome Assembly and Applications -- 3.1 de Bruijn Assembly of a Single Genome -- 3.2 Modification for Metagenome and Metagenome-Assembled Genomes -- 3.3 Compacted de Bruijn Graph -- 4 Estimation of Growth Rates for Metagenome-Assembled Genomes (MAGs) -- 5 Methods for Identifying Biosynthetic Gene Clusters -- 5.1 A Hidden Markov Model-Based Approach -- 5.2 A Deep Learning Approach -- 5.3 BGC Identification Based on Metagenomic Data -- 6 Future Directions -- References -- Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics -- 1 Introduction -- 2 Bioinformatics Pipeline -- 2.1 Microbiome Data -- 2.2 Quality Control -- 2.3 Taxonomic Profiling -- 2.3.1 MetaPhlAn2 -- 2.3.2 Kraken2 -- 2.3.3 Kaiju -- 2.4 Computing facilities -- 3 Methodology -- 3.1 Pre-Processing and Feature Selection -- 3.2 Exploration of Candidate Classifiers -- 3.3 The Ensemble Classifier -- 3.4 Class Imbalance -- 3.5 Performance Measures -- 3.6 Data Analysis -- 4 Results -- 5 Discussion -- 6 Data Acknowledgement -- 7 Code Availability.
References -- Part II Exploratory Analyses of Microbial Communities -- Statistical Methods for Pairwise Comparison of Metagenomic Samples -- 1 Introduction -- 2 Microbial Community Comparison Methods Based on OTU Abundance Data -- 3 Microbial Community Comparison Measures Based on a Phylogenetic Tree -- 3.1 The Fst Statistic and Phylogenetic Test for Comparing Communities -- 3.2 UniFrac, W-UniFrac, VAW-UniFrac, and Generalized UniFrac for Comparing Microbial Communities -- 3.3 VAW-UniFrac for Comparing Communities -- 4 Alignment-Free Methods for the Comparison of Microbial Communities -- 5 A Tutorial on the Use of UniFrac Type and Alignment-Free Dissimilarity Measures for the Comparison of Metagenomic Samples -- 5.1 Analysis Steps for UniFrac, W-UniFrac, Generalized UniFrac, and VAW-UniFrac -- 5.2 Analysis Steps for the Comparison of Microbial Communities Based on Alignment-Free Methods -- 6 Discussion -- References -- Beta Diversity and Distance-Based Analysis of Microbiome Data -- 1 Introduction -- 2 Quantifying Dissimilarity: Common Beta Diversity Metrics -- 3 Ordination and Dimension Reduction -- 3.1 Principal Coordinates Analysis -- 3.2 Double Principal Coordinate Analysis -- 3.3 Biplots -- 3.4 Accounting for Compositionality -- 3.5 Model-Based Ordination Using Latent Variables -- 4 Distance-Based Hypothesis Testing -- 4.1 Permutation Tests -- 4.2 Kernel Machine Regression Tests -- 4.3 Sum of Powered Score Tests -- 4.4 Adaptive Tests -- 4.5 Comparison of Distance-Based Tests -- 5 Strengths, Weaknesses, and Future Directions -- References -- Part III Statistical Models and Inference -- Joint Models for Repeatedly Measured Compositional and Normally Distributed Outcomes -- 1 Introduction -- 2 Motivating Data -- 3 Statistical Models -- 3.1 The Multinomial Logistic Mixed Model (MLMM) -- 3.2 Dirichlet-Multinomial Mixed Model (DMMM). 3.3 Goodness of Fit -- 4 Simulation Studies -- 4.1 Simulation Setting -- 4.2 Simulation Results -- 5 Data Analysis -- 6 Discussion -- 7 Software -- Appendix -- References -- Statistical Methods for Feature Identification in Microbiome Studies -- 1 Introduction -- 2 Differential Abundance Analysis -- 2.1 Compositional Methods -- 2.2 Count-Based Methods -- 2.3 Additional Notes -- 3 Mediation Analysis -- 4 Feature Identification Adjusting for Confounding -- 4.1 Covariate Adjustment -- 4.2 Model-Based Standardization -- 5 Summary -- References -- Statistical Methods for Analyzing Tree-Structured Microbiome Data -- 1 Introduction -- 2 Modeling Multivariate Count Data -- 2.1 Dirichlet-Multinomial Model -- 2.2 Dirichlet-Tree Multinomial Model -- 2.3 Implementation and Illustration -- 3 Estimating Microbial Compositions -- 3.1 Empirical Bayes Normalization -- 3.2 Phylogeny-Aware Normalization -- 3.3 Statistical Analysis of Compositional Data -- 3.4 Implementation and Illustration -- 4 Regression with Compositional Predictors -- 4.1 Constrained Lasso and Log-Ratio Lasso -- 4.2 Subcomposition Selection -- 4.3 Phylogeny-Aware Subcomposition Selection -- 4.4 Linear Regression and Variable Fusion -- 5 Additional References -- 6 Discussion -- References -- A Log-Linear Model for Inference on Bias in Microbiome Studies -- 1 Introduction -- 2 Methods -- 2.1 The Brooks' Data -- 2.2 Setup and Estimation -- 2.3 Inference -- 2.4 Testability of the Hypothesis -- 2.4.1 Example: Testable Hypotheses for Main Effects -- 2.4.2 Example: Testable Hypotheses for Interaction Effects -- 3 Simulations -- 3.1 Main Effect Simulation -- 3.2 Interaction Effect Simulation Based on the Brooks Data -- 4 Results -- 4.1 Simulation Results -- 4.2 Do Interactions Between Taxa Affect Bias in the Brooks' Data? -- 4.3 Plate and Sample Type Effects in the Brooks' Data -- 5 Discussion -- Appendix. References -- Part IV Bayesian Methods -- Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data -- 1 Introduction -- 2 Methods -- 2.1 Dirichlet-Multinomial Regression Models for Compositional Data -- 2.2 Variable Selection Priors -- 2.3 Network Priors -- 2.3.1 Unknown G -- 2.4 Dirichlet-Tree Multinomial Models -- 2.5 Posterior Inference -- 3 Simulated Data -- 3.1 Simulation Study for DM Regression Models -- 3.2 DM Sensitivity Analysis -- 3.3 Simulation Study for DTM Regression Models -- 3.4 DTM Sensitivity Analysis -- 4 Applications -- 4.1 Multi-omics Microbiome Study-Pregnancy Initiative (MOMS-PI) -- 4.2 Gut Microbiome Study -- 5 Conclusion -- References -- A Bayesian Approach to Restoring the Duality Between Principal Components of a Distance Matrix and Operational Taxonomic Units in Microbiome Analyses -- 1 Introduction -- 1.1 Motivating Datasets -- 1.2 Nonlinear or Stochastic Distances -- 1.3 Limitations of SVD-Based Approaches -- 2 A Bayesian Formulation -- 2.1 Posterior Density -- 3 Model Sum of Squares and Biplots -- 4 Posterior Inference -- 4.1 Gibbs Sampler -- 4.2 Dimension Reduction: Skinny Bayesian Technique -- 4.2.1 Subsetted Data Matrix -- 4.2.2 Lower Dimensional Parameters and Induced Posterior -- 4.2.3 Faster Inference Procedure -- 4.3 Model Parameter Estimates -- 5 Simulation Study -- 5.1 Generation Strategy -- 6 Data Analysis -- 6.1 Tobacco Data -- 6.2 Subway Data -- 7 Data Acknowledgement -- 8 Discussion -- Supplementary Materials -- Appendix -- Proof of Lemma 1 -- References -- Part V Special Topics -- Tree Variable Selection for Paired Case-Control Studies with Application to Microbiome Data -- 1 Introduction -- 2 Gini Index -- 2.1 Simulation Analysis -- 3 Multivariate Gini Index -- 3.1 Conditional Gini Index -- 4 Variable Importance -- 5 Analysis of Obesity Using Microbiome Data. 6 Discussion -- Appendix -- References -- Networks for Compositional Data -- 1 Introduction -- 2 Methods -- 2.1 Learning Networks from Marginal Associations -- 2.1.1 ReBoot -- 2.1.2 SparCC -- 2.1.3 CCLasso -- 2.1.4 COAT -- 2.2 Learning Networks from Conditional Associations -- 2.2.1 SPIEC-EASI -- 2.2.2 gCoda -- 2.2.3 SPRING -- 3 Data-Generating Models -- 3.1 Null Models -- 3.2 Copula Models -- 3.3 Logistic-Normal Model -- 4 Results -- 4.1 Spurious (Partial) Correlations -- 4.2 Performance in Network Discovery -- 4.3 Case Studies in R -- 5 Future Directions -- References -- Index. |
| Record Nr. | UNINA-9910508445803321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors
| Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (348 pages) |
| Disciplina | 579 |
| Collana | Frontiers in probability and the statistical sciences |
| Soggetto topico |
Microbiology - Statistical methods
Microbiologia Estadística matemàtica Microorganismes Genètica microbiana |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-73351-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Part I Preprocessing and Bioinformatics Pipelines -- Denoising Methods for Inferring Microbiome Community Content and Abundance -- 1 Introduction -- 2 Common Algorithmic Denoising Strategies -- 3 Model-Based Denoising -- 3.1 Hierarchical Divisive Clustering -- 3.2 Finite Mixture Model -- 3.3 Denoising Long-Read Technology -- 4 Model Assessment -- 4.1 With Known Truth -- 4.1.1 Accuracy in ASV Identification -- 4.1.2 Accuracy in Read Assignments -- 4.2 With Unknown Truth -- 4.2.1 Assessment with UMIs -- 4.2.2 Clustering Stability -- 5 Conclusions -- References -- Statistical and Computational Methods for Analysis of Shotgun Metagenomics Sequencing Data -- 1 Introduction -- 2 Methods for Species Identification and Quantification of Microorganisms -- 3 Metagenome Assembly and Applications -- 3.1 de Bruijn Assembly of a Single Genome -- 3.2 Modification for Metagenome and Metagenome-Assembled Genomes -- 3.3 Compacted de Bruijn Graph -- 4 Estimation of Growth Rates for Metagenome-Assembled Genomes (MAGs) -- 5 Methods for Identifying Biosynthetic Gene Clusters -- 5.1 A Hidden Markov Model-Based Approach -- 5.2 A Deep Learning Approach -- 5.3 BGC Identification Based on Metagenomic Data -- 6 Future Directions -- References -- Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics -- 1 Introduction -- 2 Bioinformatics Pipeline -- 2.1 Microbiome Data -- 2.2 Quality Control -- 2.3 Taxonomic Profiling -- 2.3.1 MetaPhlAn2 -- 2.3.2 Kraken2 -- 2.3.3 Kaiju -- 2.4 Computing facilities -- 3 Methodology -- 3.1 Pre-Processing and Feature Selection -- 3.2 Exploration of Candidate Classifiers -- 3.3 The Ensemble Classifier -- 3.4 Class Imbalance -- 3.5 Performance Measures -- 3.6 Data Analysis -- 4 Results -- 5 Discussion -- 6 Data Acknowledgement -- 7 Code Availability.
References -- Part II Exploratory Analyses of Microbial Communities -- Statistical Methods for Pairwise Comparison of Metagenomic Samples -- 1 Introduction -- 2 Microbial Community Comparison Methods Based on OTU Abundance Data -- 3 Microbial Community Comparison Measures Based on a Phylogenetic Tree -- 3.1 The Fst Statistic and Phylogenetic Test for Comparing Communities -- 3.2 UniFrac, W-UniFrac, VAW-UniFrac, and Generalized UniFrac for Comparing Microbial Communities -- 3.3 VAW-UniFrac for Comparing Communities -- 4 Alignment-Free Methods for the Comparison of Microbial Communities -- 5 A Tutorial on the Use of UniFrac Type and Alignment-Free Dissimilarity Measures for the Comparison of Metagenomic Samples -- 5.1 Analysis Steps for UniFrac, W-UniFrac, Generalized UniFrac, and VAW-UniFrac -- 5.2 Analysis Steps for the Comparison of Microbial Communities Based on Alignment-Free Methods -- 6 Discussion -- References -- Beta Diversity and Distance-Based Analysis of Microbiome Data -- 1 Introduction -- 2 Quantifying Dissimilarity: Common Beta Diversity Metrics -- 3 Ordination and Dimension Reduction -- 3.1 Principal Coordinates Analysis -- 3.2 Double Principal Coordinate Analysis -- 3.3 Biplots -- 3.4 Accounting for Compositionality -- 3.5 Model-Based Ordination Using Latent Variables -- 4 Distance-Based Hypothesis Testing -- 4.1 Permutation Tests -- 4.2 Kernel Machine Regression Tests -- 4.3 Sum of Powered Score Tests -- 4.4 Adaptive Tests -- 4.5 Comparison of Distance-Based Tests -- 5 Strengths, Weaknesses, and Future Directions -- References -- Part III Statistical Models and Inference -- Joint Models for Repeatedly Measured Compositional and Normally Distributed Outcomes -- 1 Introduction -- 2 Motivating Data -- 3 Statistical Models -- 3.1 The Multinomial Logistic Mixed Model (MLMM) -- 3.2 Dirichlet-Multinomial Mixed Model (DMMM). 3.3 Goodness of Fit -- 4 Simulation Studies -- 4.1 Simulation Setting -- 4.2 Simulation Results -- 5 Data Analysis -- 6 Discussion -- 7 Software -- Appendix -- References -- Statistical Methods for Feature Identification in Microbiome Studies -- 1 Introduction -- 2 Differential Abundance Analysis -- 2.1 Compositional Methods -- 2.2 Count-Based Methods -- 2.3 Additional Notes -- 3 Mediation Analysis -- 4 Feature Identification Adjusting for Confounding -- 4.1 Covariate Adjustment -- 4.2 Model-Based Standardization -- 5 Summary -- References -- Statistical Methods for Analyzing Tree-Structured Microbiome Data -- 1 Introduction -- 2 Modeling Multivariate Count Data -- 2.1 Dirichlet-Multinomial Model -- 2.2 Dirichlet-Tree Multinomial Model -- 2.3 Implementation and Illustration -- 3 Estimating Microbial Compositions -- 3.1 Empirical Bayes Normalization -- 3.2 Phylogeny-Aware Normalization -- 3.3 Statistical Analysis of Compositional Data -- 3.4 Implementation and Illustration -- 4 Regression with Compositional Predictors -- 4.1 Constrained Lasso and Log-Ratio Lasso -- 4.2 Subcomposition Selection -- 4.3 Phylogeny-Aware Subcomposition Selection -- 4.4 Linear Regression and Variable Fusion -- 5 Additional References -- 6 Discussion -- References -- A Log-Linear Model for Inference on Bias in Microbiome Studies -- 1 Introduction -- 2 Methods -- 2.1 The Brooks' Data -- 2.2 Setup and Estimation -- 2.3 Inference -- 2.4 Testability of the Hypothesis -- 2.4.1 Example: Testable Hypotheses for Main Effects -- 2.4.2 Example: Testable Hypotheses for Interaction Effects -- 3 Simulations -- 3.1 Main Effect Simulation -- 3.2 Interaction Effect Simulation Based on the Brooks Data -- 4 Results -- 4.1 Simulation Results -- 4.2 Do Interactions Between Taxa Affect Bias in the Brooks' Data? -- 4.3 Plate and Sample Type Effects in the Brooks' Data -- 5 Discussion -- Appendix. References -- Part IV Bayesian Methods -- Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data -- 1 Introduction -- 2 Methods -- 2.1 Dirichlet-Multinomial Regression Models for Compositional Data -- 2.2 Variable Selection Priors -- 2.3 Network Priors -- 2.3.1 Unknown G -- 2.4 Dirichlet-Tree Multinomial Models -- 2.5 Posterior Inference -- 3 Simulated Data -- 3.1 Simulation Study for DM Regression Models -- 3.2 DM Sensitivity Analysis -- 3.3 Simulation Study for DTM Regression Models -- 3.4 DTM Sensitivity Analysis -- 4 Applications -- 4.1 Multi-omics Microbiome Study-Pregnancy Initiative (MOMS-PI) -- 4.2 Gut Microbiome Study -- 5 Conclusion -- References -- A Bayesian Approach to Restoring the Duality Between Principal Components of a Distance Matrix and Operational Taxonomic Units in Microbiome Analyses -- 1 Introduction -- 1.1 Motivating Datasets -- 1.2 Nonlinear or Stochastic Distances -- 1.3 Limitations of SVD-Based Approaches -- 2 A Bayesian Formulation -- 2.1 Posterior Density -- 3 Model Sum of Squares and Biplots -- 4 Posterior Inference -- 4.1 Gibbs Sampler -- 4.2 Dimension Reduction: Skinny Bayesian Technique -- 4.2.1 Subsetted Data Matrix -- 4.2.2 Lower Dimensional Parameters and Induced Posterior -- 4.2.3 Faster Inference Procedure -- 4.3 Model Parameter Estimates -- 5 Simulation Study -- 5.1 Generation Strategy -- 6 Data Analysis -- 6.1 Tobacco Data -- 6.2 Subway Data -- 7 Data Acknowledgement -- 8 Discussion -- Supplementary Materials -- Appendix -- Proof of Lemma 1 -- References -- Part V Special Topics -- Tree Variable Selection for Paired Case-Control Studies with Application to Microbiome Data -- 1 Introduction -- 2 Gini Index -- 2.1 Simulation Analysis -- 3 Multivariate Gini Index -- 3.1 Conditional Gini Index -- 4 Variable Importance -- 5 Analysis of Obesity Using Microbiome Data. 6 Discussion -- Appendix -- References -- Networks for Compositional Data -- 1 Introduction -- 2 Methods -- 2.1 Learning Networks from Marginal Associations -- 2.1.1 ReBoot -- 2.1.2 SparCC -- 2.1.3 CCLasso -- 2.1.4 COAT -- 2.2 Learning Networks from Conditional Associations -- 2.2.1 SPIEC-EASI -- 2.2.2 gCoda -- 2.2.3 SPRING -- 3 Data-Generating Models -- 3.1 Null Models -- 3.2 Copula Models -- 3.3 Logistic-Normal Model -- 4 Results -- 4.1 Spurious (Partial) Correlations -- 4.2 Performance in Network Discovery -- 4.3 Case Studies in R -- 5 Future Directions -- References -- Index. |
| Record Nr. | UNISA-996466407003316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
