This book takes the reader through the basics of using the R statistical programming environment for data analysis using a series of case studies based around tasks that answer real research questions using publicly available molecular biology datasets.
This book takes the reader through the basics of using the R statistical programming environment for data analysis using a series of case studies based around tasks that answer real research questions using publicly available molecular biology datasets.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Ed Curry initially studied computer science (Cambridge) and AI with a systems biology specialism (Edinburgh) before embarking on a PhD in computer-based molecular biology, studying stem cell differentiation at the Centre for Regenerative Medicine in Edinburgh. He spent 10 years in the Faculty of Medicine at Imperial College London, during which time he established a research group focusing on interactions between the genetic, epigenetic and transcriptional state of cancer cells during carcinogenesis and the acquisition of drug resistance. He has extensive teaching experience as a lecturer, examiner and course director, including co-founding Imperial College's Cancer Informatics MRes program and the Genetics & Genomics module for the BSc in Medical Biosciences. He joined GSK R&D in October 2019, remaining an honorary lecturer at Imperial College.
Inhaltsangabe
1, Introduction 2. Introduction to R 3. An Introduction to LINUX for Biological Research 4. Statistical Methods for Data Analysis 5. Analyzing Generic Tabular Numeric Datasets in R 6. Functional Enrichment Analysis 7. Integrating Multiple Datasets in R 8. Analyzing Microarray Data in R 9. Analyzing DNA Methylation Microarray Data in R 10. DNA Analysis With Microarrays 11. Working with Sequencing Data 12. Genomic Sequence Profiling 13. ChIP-seq 14. RNA-seq 15. Bisulphite Sequencing 16. Final Notes
1, Introduction 2. Introduction to R 3. An Introduction to LINUX for Biological Research 4. Statistical Methods for Data Analysis 5. Analyzing Generic Tabular Numeric Datasets in R 6. Functional Enrichment Analysis 7. Integrating Multiple Datasets in R 8. Analyzing Microarray Data in R 9. Analyzing DNA Methylation Microarray Data in R 10. DNA Analysis With Microarrays 11. Working with Sequencing Data 12. Genomic Sequence Profiling 13. ChIP-seq 14. RNA-seq 15. Bisulphite Sequencing 16. Final Notes
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