Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R

Regular price €241.80
Quantity:
Ships in 10-20 days
Delivery/Collection within 10-20 working days
Shipping & Delivery
A01=Hongmei Zhang
Adaptive Lasso
advanced epigenetic data analysis pipeline
Age Group_Uncategorized
Age Group_Uncategorized
Author_Hongmei Zhang
automatic-update
Bayesian Networks
bioinformatics
biomarker identification
Candidate Graphs
Category1=Non-Fiction
Category=PS
CEL File
Cell Type Compositions
Cell Type Proportions
Clustering Objects
computational genomics
COP=United States
CpG Site
data mining
Data Set
Delivery_Pre-order
Divisive Clustering
DNA Methylation
DNA Methylation Data
DNA Methylation Measure
DNAm
Elastic Net
Elastic Net Penalty
Epigenetic Data
epigenetics
eq_bestseller
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
gene expression
genome-wide association
high dimensional statistics
high-dimensional gene expression
Language_English
Microarray Gene Expression Data Set
Mutual Clusters
NOVA1
PA=Temporarily unavailable
Price_€100 and above
PS=Active
R programming techniques
R programs
RF
RNA Seq Data
Scad
Scad Penalty
softlaunch
statistical genetics
SVA
unsupervised learning methods

Product details

  • ISBN 9781498772594
  • Weight: 530g
  • Dimensions: 156 x 234mm
  • Publication Date: 28 May 2020
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
Secure checkout Fast Shipping Easy returns

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.

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.

More from this author