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.
General
| Imprint: |
Productivity Press
|
| Country of origin: |
United States |
| Series: |
Chapman & Hall/CRC Computational Biology Series |
| Release date: |
May 2020 |
| First published: |
2020 |
| Authors: |
Hongmei Zhang
|
| Dimensions: |
234 x 156mm (L x W) |
| Format: |
Hardcover
|
| Pages: |
202 |
| ISBN-13: |
978-1-4987-7259-4 |
| Categories: |
Books >
Science & Mathematics >
Biology, life sciences >
General
|
| LSN: |
1-4987-7259-5 |
| Barcode: |
9781498772594 |
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