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Data analysis for omic sciences : methods and applications /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
[2018]
|
| Colección: | Wilson and Wilson's comprehensive analytical chemistry ;
v. 82. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; Data Analysis for Omic Sciences: Methods and Applications; Copyright; Contents; Contributors to Volume 82; Series Editor�s Preface; Preface; Chapter One: Introduction to the Data Analysis Relevance in the Omic Era; 1. Introduction to Omics; 2. Data Analysis in the Omic Workflow; 2.1. Molecular Hypothesis Formulation; 2.2. Experimental Design; 2.3. Sample Preparation and Instrumental Analysis; 2.4. Preprocessing and Data Analysis; 2.5. Results Evaluation and Biological Interpretation; 3. Data Analysis Aspects Considered in This Volume
- 3.1. Hypothesis Formulation, Experimental Design, Sample Preparation and Analysis3.2. Preprocessing and Data Analysis; 3.3. Results Evaluation and Biological interpretation; 4. Future Trends; Acknowledgements; References; Chapter Two: Experimental Approaches in Omic Sciences; 1. Introduction; 2. The Importance of the Biological Samples; 3. Targeted and Untargeted Analytical Approaches in Omic Studies; 4. Sample Preparation in Omics Studies; 5. Analytical Technologies in Omic Sciences; 5.1. Genomics, Epigenomics, and Transcriptomics; 5.2. Proteomics; 5.3. Metabolomics; 6. Concluding Remarks
- 4.2. Array Normalization4.2.1. Main Steps; 4.2.1.1. Background Correction; 4.2.1.2. Normalization; 4.2.1.3. Summarization; 4.2.2. Methods; 4.2.2.1. Robust Multichip Analysis; 4.2.2.2. Probe Logarithmic Intensity Error; 4.2.2.3. GC-RMA; 4.3. Data Filtering; 4.4. Batch Effect in Microarrays; 5. Experimental Design for Microarray Experiments; 5.1. Replication; 5.1.1. Power and Sample Size; 5.2. Pooling; 5.3. Blocking Microarray Experiments; 6. Statistical Analysis of Microarray Data; 6.1. Class Comparison, Selecting Differentially Expressed Genes; 6.1.1. Statistical Tests for Microarray Data
- 6.1.2. The Multiple Testing Problem and Proposed Solutions6.1.3. Volcano Plots; 6.2. Class Prediction; 6.3. Class Discovery; 6.4. Biological Significance Analysis: Finding Meaning in Data; 6.4.1. Pathway Analysis Methods; 7. Microarray Bioinformatics; 7.1. Software for Microarray Data Analysis; 7.1.1. Open Source Software; 7.1.2. The Bioconductor Project; 7.1.3. Proprietary Software; 7.2. Microarray Databases; 8. Discussion and Conclusions; Supplementary Materials; Acknowledgements; References; Further Reading; Chapter Four: RNA-Seq Data Analysis, Applications and Challenges; 1. Introduction