Statistics and Data Analysis for Microarrays Using R and Bioconductor

Statistics and Data Analysis for Microarrays Using R and Bioconductor
Author :
Publisher : CRC Press
Total Pages : 1076
Release :
ISBN-10 : 9781439809761
ISBN-13 : 1439809763
Rating : 4/5 (763 Downloads)

Book Synopsis Statistics and Data Analysis for Microarrays Using R and Bioconductor by : Sorin Draghici

Download or read book Statistics and Data Analysis for Microarrays Using R and Bioconductor written by Sorin Draghici and published by CRC Press. This book was released on 2016-04-19 with total page 1076 pages. Available in PDF, EPUB and Kindle. Book excerpt: Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition 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 teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. New to the Second EditionCompletely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying downloadable resource. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.


Statistics and Data Analysis for Microarrays Using R and Bioconductor Related Books

Statistics and Data Analysis for Microarrays Using R and Bioconductor
Language: en
Pages: 1076
Authors: Sorin Draghici
Categories: Computers
Type: BOOK - Published: 2016-04-19 - Publisher: CRC Press

DOWNLOAD EBOOK

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Language: en
Pages: 478
Authors: Robert Gentleman
Categories: Computers
Type: BOOK - Published: 2005-12-29 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated
The Analysis of Gene Expression Data
Language: en
Pages: 511
Authors: Giovanni Parmigiani
Categories: Medical
Type: BOOK - Published: 2006-04-11 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

This book presents practical approaches for the analysis of data from gene expression micro-arrays. It describes the conceptual and methodological underpinning
Data Analysis for the Life Sciences with R
Language: en
Pages: 537
Authors: Rafael A. Irizarry
Categories: Mathematics
Type: BOOK - Published: 2016-10-04 - Publisher: CRC Press

DOWNLOAD EBOOK

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from r
Molecular Data Analysis Using R
Language: en
Pages: 354
Authors: Csaba Ortutay
Categories: Medical
Type: BOOK - Published: 2017-02-06 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how