Statistics and Data Analysis for Microarrays using MATLAB , 2nd edition

✍ Scribed by Draghici, Sorin

• Publisher: CRC Press
• Year: 2011
• Tongue: English
• Leaves: 1076
• Series: Chapman & Hall/CRC Mathematical and Computational Biology
• Edition: 2nd ed
• Category: Library

✦ Synopsis

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 predec. Read more...

Abstract:

Replaces the commercial software with the open source R computing environment. This title contains chapters on cutting-edge microarray topics and provides the R code on an accompanying CD-ROM. It bridges the gap between an introduction to data analysis and advanced material for performing data analysis. Read more...

✦ Table of Contents

Content: Front Cover
Dedication
Contents
List of Figures
List of Tables
Preface
1. Introduction
2. The cell and its basic mechanisms
3. Microarrays
4. Reliability and reproducibility issues in DNA microarray measurements
5. Image processing
6.Introduction to R
7. Bioconductor: principles and illustrations
8. Elements of statistics
9. Probability distributions
10. Basic statistics in R
11. Statistical hypothesis testing
12. Classical approaches to data analysis
13. Analysis of Variance --
ANOVA
14. Linear models in R
15. Experiment design
16. Multiple comparisons 17. Analysis and visualization tools18. Cluster analysis
19. Quality control
20. Data preprocessing and normalization
21. Methods for selecting differentially expressed genes
22. The Gene Ontology (GO)
23. Functional analysis and biological interpretation of microarray data
24. Uses, misuses, and abuses in GO profiling
25. A comparison of several tools for ontological analysis
26. Focused microarrays --
comparison and selection
27. ID Mapping issues
28. Pathway analysis
29. Machine learning techniques
30. The road ahead
Bibliography
Back Cover

✦ Subjects

Библиотека;Компьютерная литература;R;