Sorin Draghici

Statistics and Data Analysis for Microarrays Using R and Bioconductor (eBook, PDF)

• Format: PDF

Produktbeschreibung

Richly illustrated in color, this bestselling text provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that explains the basics of R and micr

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 1036
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781439809761
• Artikelnr.: 57069870

Autorenporträt

Sorin Draghici the Robert J. Sokol MD Endowed Chair in Systems Biology in the Department of Obstetrics and Gynecology, professor in the Department of Clinical and Translational Science and Department of Computer Science, and head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University. He is also the chief of the Bioinformatics and Data Analysis Section in the Perinatology Research Branch of the National Institute for Child Health and Development. A senior member of IEEE, Dr. Draghici is an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine. He earned a Ph.D. in computer science from the University of St. Andrews.

Inhaltsangabe

Introduction. The Cell and Its Basic Mechanisms. Microarrays. Reliability
and Reproducibility Issues in DNA Microarray Measurements. Image
Processing. Introduction to R. Bioconductor: Principles and Illustrations.
Elements of Statistics. Probability Distributions. Basic Statistics in R.
Statistical Hypothesis Testing. Classical Approaches to Data Analysis.
Analysis of Variance (ANOVA). Linear Models in R. Experiment Design.
Multiple Comparisons. Analysis and Visualization Tools. Cluster Analysis.
Quality Control. Data Pre-Processing and Normalization. Methods for
Selecting Differentially Regulated Genes. The Gene Ontology (GO).
Functional Analysis and Biological Interpretation of Microarray Data. Uses,
Misuses, and Abuses in GO Profiling. A Comparison of Several Tools for
Ontological Analysis. Focused Microarrays - Comparison and Selection. ID
Mapping Issues. Pathway Analysis. Machine Learning Techniques. The Road
Ahead. References.