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Biostatistics with R: An Introduction to Statistics Through Biological Data by Babak Shahbaba

Biostatistics with R: An Introduction to Statistics Through Biological Data Babak Shahbaba ebook
Format: pdf
Publisher: Springer
Page: 369
ISBN: 146141301X, 9781461413028

"If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics. Our tools use gene sets to Author Affiliations. Data mining is the art and science of intelligent data analysis. Probability with R: An Introduction with Computer Science Applications book download Biostatistics with R - An Introduction to Statistics Through Biological . Topics include microarray analysis, RNA-seq analysis, genome rearrangements, copy number alteration,network/pathway analysis, genome visualization, gene function prediction, functional annotation, data analysis using R, statistics for metabolomics, and much more. "It is a total delight reading this book." —Pharmaceutical Research. The more recent workshops focus on training researchers using advanced high-throughput technologies on the latest approaches being used in computational biology to deal with the new data. 1 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02115, USA Many of these approaches are implemented in the extensively used statistical computing environment R/Bioconductor [5]. It's great for troubleshooting data that break the assumptions of the common tests, i.e., just about all biological data. We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. I am chairing a committee to completely revamp my department's introductory biostatistics course. Its major weakness is that it does not have canned problem sets included for using R. But I'd still use this textbook to teach an introduction to stats course and then create the R exercises myself. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers.