Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline" of analytical methods with concrete examples starting from raw gene expression and DNA methylation data at the genome scale. Methods on quality control, data pre-processing, data mining, and further assessments are presented in the book, and R programs based on simulated data and real data are included. Codes with example data are all reproducible.
Features:
· Provides a sequence of analytical tools for genome-scale gene expression data and DNA methylation data, starting from quality control and pre-processing of raw genome-scale data.
· Organized by a parallel presentation with explanation on statistical methods and corresponding R packages/functions in quality control, pre-processing, and data analyses (e.g., clustering and networks).
· Includes source codes with simulated and real data to reproduce the results. Readers are expected to gain the ability to independently analyze genome-scaled expression and methylation data and detect potential biomarkers.
This book is ideal for students majoring in statistics, biostatistics, and bioinformatics and researchers with an interest in high dimensional genetic and epigenetic studies.
Table of Contents:
Genome-Scale Genetic and Epigenetic Data. Methods for Data Pre-Processing. Data Mining. Genetic and Epigenetic Factor Selections. Network Construction and Analyses.
About the Author :
Hongmei Zhang is a Biostatistician at the University of Memphis. She has been working with gene expression and DNA methylation data and her methodological research interest is to develop corresponding statistical methods. She has been teaching courses in this field for a number of years.
Review :
'A big asset of the book, which makes it remarkable contribution and ideal reference book for students of statistics, biostatistics, bioinformatics as well as applied workers/researchers interested in exploring high-dimensional genetic and epigenetic, is the well-illustrated applications and reproducible R codes for thoroughly analysing gene expression and DNA methylation data sets at the genome scale along with the ‘pipeline’ for analytical methods.'
-Anoop Chaturvedi, University of Allahabad, Prayagraj, India
"I would recommend this brief but consistent practical volume, especially to students with a statistical background, interested in high-dimensional genetic and epigenetic studies."
-Anca Vitcu, International Society for Clinical Biostatistics, 72, 2021
