Data Analysis for Omic Sciences: Methods and Applications

Erscheinungsjahr 2018
ISBN: 978-0-444-64044-4
Verlag: Elsevier Science & Technology

Buch, Englisch, 730 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 1470 g

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.

Autoren/Hrsg.

Weitere Mitwirkende

Jaumot, Joaquim

Bedia, Carmen

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien
• Naturwissenschaften Chemie Analytische Chemie Umweltchemie, Lebensmittelchemie

Inhaltsverzeichnis

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

Über Autor(innen)

Jaumot, Joaquim
Joaquim Jaumot is Research Scientist at the Spanish National Research Council (CSIC) in the Institute of Environmental Assessment and Water Research (IDAEA-CSIC). He graduated in Chemistry in 2001 and received a Ph.D. in Chemistry in 2006 both from the University of Barcelona (Barcelona, Spain). He has published more than 50 scientific papers and participated in several national and international projects. His main research line is focused on the development and application of chemometric data analysis tools to the study of different types of chemical and biological systems. For instance, he has actively participated in the development of the multivariate curve resolution alternating least squares (MCR-ALS) software. In recent years, his research has been focused on the analysis of large -omic data sets. In particular, main efforts have been aimed at the development and application of chemometric tools in the analysis of mass spectrometry metabolomic data evaluating effects of environmental stressors on model organisms.

Bedia, Carmen
Carmen Bedia studied Pharmacy at the University of Barcelona (Spain) and obtained her Ph.D. in 2007 in the field of sphingolipid metabolism, in the Spanish National Research Council (CSIC, Barcelona). She moved to the French Institute of Health and Medical Research (INSERM, Toulouse, France) as a postdoctoral researcher where she focused on cell biology of sphingolipid metabolism in the development of melanoma malignancy. After her return to Barcelona in 2010 to the CSIC, she has been working as postdoctoral researcher on medicinal chemistry of sphingolipids and she expanded her knowledge about lipidomics in biological samples using LC-MS. Since 2013 she works in the Institute of Environmental Assessment and Water Research (IDAEA-CSIC) in the frame of an ERC awarded project in which she uses chemometric techniques to extract meaningful information from LC-MS and Mass Spectrometry imaging datasets, in order to investigate the effects of environmental pollutants on human and plant cells.