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Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environme… Read more
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Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environmental, clinical and food analysis cases help readers disseminate these approaches. Chapters of note include an Introduction to Data Analysis Relevance in the Omics Era, Omics Experimental Design and Data Acquisition, Microarrays Data, Analysis of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS Metabolomic, Data Normalization and Scaling, Metabolomics Data Preprocessing, and more.
Academic and non-academic sectors, especially in environmental, biomedical, and food analysis fields. Ph.D. students to senior researchers and scientists who need to update their knowledge regarding data analysis methods
Volume Editor PrefaceRoma Tauler, Carmen Bedia and Joaquim Jaumot1. Introduction to the data analysis relevance in the omics eraRoma Tauler, Carmen Bedia and Joaquim Jaumot2. Omics experimental design and data acquisitionCarmen Bedia 3. Microarrays data analysisAlex Sanchez-Pla 4. Analysis of High-Throughput RNA Sequencing DataAnna Esteve-Codina5. Analysis of High-Throughput DNA Bisulfite Sequencing DataSimon Charles Heath 6. Data quality assessment in untargeted LC-MS metabolomicJulia Kuligowski, Guillermo Quintas, Angel Sanchez-Illana and Jose David Piñeiro-Ramos7. Data normalization and scaling: consequences for the analysis in omics sciencesJan Walach, Peter Filzmoser and Karel Hron, 8. Metabolomics data preprocessing: From raw data to features for statistical analysisIbrahim Karaman and Rui Climaco Pinto, 9. Exploratory data analysis and data decompositionsIvana Stanimirova and Michal Daszykowski, 10. Chemometric methods for classification and feature selectionFederico Marini and Marina Cocchi11. Advanced statistical multivariate data analysisJasper Engel and Jeroen Jansen, 12. Analysis and interpretation of mass spectrometry imaging datasetsBenjamin Bowen 13. Metabolomics tools for data analysisMatej Oresic, Alex Dickens, Tuulia Hyötyläinen, Santosh Lamichhane and Partho Sen14. Metabolite identification and annotationC. Barbas, Joanna Godzien and Alberto Gil de la Fuente, 15. Multi-omic data integration and analysis via model-driven approachesIgor Marín de Mas 16. Integration of metabolomic data from multiple analytical platforms: Toward an extensive coverage of the metabolome.Julien Boccard and Serge Rudaz, 17. Multiomics data integration in time series experimentsAna Conesa and Sonia Tarazona18. Metabolomics applications in environmental researchCarmen Bedia 19. Environmental genomicsCarlos Barata and Benjamín Piña, 20. Transcriptomics and metabolomics systems biology of health and diseaseAntonio Checa, Jose Fernández Navarro and Hector Gallart Ayala,21. Foodomics applicationsAlejandro Cifuentes, Alberto Valdés and Carlos León,
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