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

  • ID: 4519495
  • Book
  • 730 Pages
  • Elsevier Science and Technology
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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.

  • 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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Volume Editor Preface Roma Tauler, Carmen Bedia and Joaquim Jaumot 1. Introduction to the data analysis relevance in the omics era Roma Tauler, Carmen Bedia and Joaquim Jaumot 2. Omics experimental design and data acquisition Carmen Bedia 3. Microarrays data analysis Alex Sanchez-Pla 4. Analysis of High-Throughput RNA Sequencing Data Anna Esteve-Codina 5. Analysis of High-Throughput DNA Bisulfite Sequencing Data Simon Charles Heath 6. Data quality assessment in untargeted LC-MS metabolomic Julia Kuligowski, Guillermo Quintas, Angel Sanchez-Illana and Jose David Piñeiro-Ramos 7. Data normalization and scaling: consequences for the analysis in omics sciences Jan Walach, Peter Filzmoser and Karel Hron  8. Metabolomics data preprocessing: From raw data to features for statistical analysis Ibrahim Karaman and Rui Climaco Pinto 9. Exploratory data analysis and data decompositions Ivana Stanimirova and Michal Daszykowski 10. Chemometric methods for classification and feature selection Federico Marini and Marina Cocchi 11. Advanced statistical multivariate data analysis Jasper Engel and Jeroen Jansen 12. Analysis and interpretation of mass spectrometry imaging datasets Benjamin Bowen 13. Metabolomics tools for data analysis Matej Oresic, Alex Dickens, Tuulia Hyötyläinen, Santosh Lamichhane and Partho Sen 14. Metabolite identification and annotation C. Barbas, Joanna Godzien and Alberto Gil de la Fuente 15. Multi-omic data integration and analysis via model-driven approaches Igor 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 experiments Ana Conesa and Sonia Tarazona 18. Metabolomics applications in environmental research Carmen Bedia 19. Environmental genomics Carlos Barata and Benjamín Piña  20. Transcriptomics and metabolomics systems biology of health and disease Antonio Checa, Jose Fernández Navarro and Hector Gallart Ayala 21. Foodomics applications Alejandro Cifuentes, Alberto Valdés and Carlos León
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