Preface xi Christine FROIDEVAUX, Marie-Laure MARTIN-MAGNIETTE and Guillem RIGAILL -- Part 1 Knowledge Integration -- Chapter 1 Clinical Data Warehouses 3 Maxime WACK and Bastien RANCE -- 1.1 Introduction to clinical information systems and biomedical warehousing: data warehouses for what purposes? -- 1.1.1 Warehouse history -- 1.1.2 Using data warehouses today -- 1.2 Challenge: widely scattered data -- 1.3 Data warehouses and clinical data -- 1.3.1 Warehouse structures -- 1.3.2 Warehouse construction and supply -- 1.3.3 Uses -- 1.4 Warehouses and omics data: challenges -- 1.4.1 Challenges of data volumetry and structuring omic data -- 1.4.2 Attempted solutions -- 1.5 Challenges and prospects -- 1.5.1 Toward general-purpose warehouses -- 1.5.2 Ethical dimension of the implementation and the use of warehouses -- 1.5.3 Origin and reproducibility -- 1.5.4 Data quality -- 1.5.5 Data warehousing federation and data sharing -- 1.6 References -- Chapter 2 Semantic Web Methods for Data Integration in Life Sciences 25 Olivier DAMERON -- 2.1 Data-related requirements in life sciences -- 2.1.1 Databases for the life sciences -- 2.1.2 Requirements -- 2.1.3 Common approaches: InterMine and BioMart -- 2.2 Semantic Web -- 2.2.1 Techniques -- 2.2.2 Implementation -- 2.3 Perspectives -- 2.3.1 Facilitating appropriation to users -- 2.3.2 Facilitating the appropriation by software programs: FAIR data -- 2.3.3 Federated queries -- 2.4 Conclusion -- 2.5 References -- Chapter 3 Workflows for Bioinformatics Data Integration 53 Sarah COHEN-BOULAKIA and Frédéric LEMOINE -- 3.1 Introduction -- 3.2 Bioinformatics data processing chains: difficulties -- 3.2.1 Designing a data processing chain -- 3.2.2 Analysis execution and reproducibility -- 3.2.3 Maintenance, sharing and reuse -- 3.3 Solutions provided by scientific workflow systems -- 3.3.1 Fundamentals of workflow systems -- 3.3.2 Workflow systems -- 3.4 Use case: RNA-seq data analysis -- 3.4.1 Study description -- 3.4.2 From data processing chain to workflows -- 3.4.3 Data processing chains implemented as workflows: conclusion -- 3.5 Challenges, open problems and research opportunities -- 3.5.1 Formalizing workflow development -- 3.5.2 Workflow testing -- 3.5.3 Discovering and sharing workflows -- 3.6 Conclusion -- 3.7 References -- Part 2 Integration and Statistics -- Chapter 4 Variable Selection in the General Linear Model: Application to Multiomic Approaches for the Study of Seed Quality 89 Céline LÉVY-LEDUC, Marie PERROT-DOCKÈS, Gwendal CUEFF and Loïc RAJJOU -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Estimation of the covariance matrix Eq -- 4.2.2 Estimation of B -- 4.3 Numerical experiments -- 4.3.1 Statistical performance -- 4.3.2 Numerical performance -- 4.4 Application to the study of seed quality -- 4.4.1 Metabolomics data -- 4.4.2 Proteomics data -- 4.5 Conclusion -- 4.6 Appendices -- 4.6.1 Example of using the package MultiVarSel for metabolomic data analysis -- 4.6.2 Example of using the package MultiVarSel for proteomic data analysis -- 4.7 Acknowledgments -- 4.8 References -- Chapter 5 Structured Compression of Genetic Information and Genome-Wide Association Study by Additive Models 117 Florent GUINOT, Marie SZAFRANSKI and Christophe AMBROISE -- 5.1 Genome-wide association studies -- 5.1.1 Introduction to genetic mapping and linkage analysis -- 5.1.2 Principles of genome-wide association studies -- 5.1.3 Single nucleotide polymorphism -- 5.1.4 Disease penetrance and odds ratio -- 5.1.5 Single marker analysis -- 5.1.6 Multi-marker analysis -- 5.2 Structured compression and association study -- 5.2.1 Context -- 5.2.2 New structured compression approach -- 5.3 Application to ankylosing spondylitis (AS) -- 5.3.1 Data -- 5.3.2 Predictive power evaluation -- 5.3.3 Manhattan diagram -- 5.3.4 Estimation for the most significant SNP aggregates -- 5.4 Conclusion -- 5.5 References -- Chapter 6 Kernels for Omics 151 Jérôme MARIETTE and Nathalie VIALANEIX -- 6.1 Introduction -- 6.2 Relational data -- 6.2.1 Data described by the kernel -- 6.2.2 Data described by a general (dis)similarity measure -- 6.3 Exploratory analysis for relational data -- 6.3.1 Kernel clustering -- 6.3.2 Kernel principal component analysis -- 6.3.3 Kernel self-organizing maps -- 6.3.4 Limitations of relational methods -- 6.4 Combining relational data -- 6.4.1 Data integration in systems biology -- 6.4.2 Kernel approaches in data integration -- 6.4.3 A consensual kernel -- 6.4.4 A parsimonious kernel that preserves the topology of the initial data -- 6.4.5 A complete kernel preserving the topology of the initial data -- 6.5 Application -- 6.5.1 Loading Tara Ocean data -- 6.5.2 Data integration by kernel approaches -- 6.5.3 Exploratory analysis: kernel PCA -- 6.6 Session information for the results of the example -- 6.7 References -- Chapter 7 Multivariate Models for Data Integration and Biomarker Selection in 'Omics Data 195 Sébastien DÉJEAN and Kim-Anh LÊ CAO -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Mathematical notations -- 7.2.2 Terminology -- 7.2.3 Multivariate projection-based approaches -- 7.2.4 A criterion to maximize specific to each methodology -- 7.2.5 A linear combination of variables to reduce the dimension of the data -- 7.2.6 Identifying a subset of relevant molecular features -- 7.2.7 Summary -- 7.3 From the biological question to the statistical analysis -- 7.3.1 Exploration of one dataset: PCA -- 7.3.2 Classify samples: projection to latent structure discriminant analysis -- 7.3.3 Integration of two datasets: projection to latent structure and related methods -- 7.3.4 Integration of several datasets: multi-block approaches -- 7.4 Graphical outputs -- 7.4.1 Individual plots -- 7.4.2 Variable plots -- 7.5 Overall summary -- 7.6 Liver toxicity study -- 7.6.1 The datasets -- 7.6.2 Biological questions and statistical methods -- 7.6.3 Single dataset analysis -- 7.6.4 Integrative analysis -- 7.7 Conclusion -- 7.8 Acknowledgments -- 7.9 Appendix: reproducible R code -- 7.9.1 Toy examples -- 7.9.2 Liver toxicity -- 7.10 References -- List of Authors -- Index.