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Data Analysis for Omic Sciences: Methods and Applications de Joaquim Jaumot

Data Analysis for Omic Sciences: Methods and Applications: Comprehensive Analytical Chemistry, cartea 82

Joaquim Jaumot, Carmen Bedia, Romà Tauler
en Limba Engleză Hardback – 28 sep 2018
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.


  • Presents the best reference book for omics data analysis
  • Provides a review of the latest trends in transcriptomics and metabolomics data analysis tools
  • Includes examples of applications in research fields, such as environmental, biomedical and food analysis
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Specificații

ISBN-13: 9780444640444
ISBN-10: 0444640444
Pagini: 730
Dimensiuni: 152 x 229 mm
Greutate: 1.14 kg
Editura: ELSEVIER SCIENCE
Seria Comprehensive Analytical Chemistry

Cuprins

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 metabolomeJulien 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 Ayala21. Foodomics applicationsAlejandro Cifuentes, Alberto Valdés and Carlos León