Statistics and data analysis for microarrays using R and Bioconductor
著者
書誌事項
Statistics and data analysis for microarrays using R and Bioconductor
（Chapman and Hall/CRC mathematical & computational biology series / series editors Alison M. Etheridge ... [et al.]）
CRC Press, c2012
2nd ed
- : hbk
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注記
Includes bibliographical references (p. 981-1025) and index
"... and the R code for all routines is available on an accompanying CD-ROM"--Back cover
内容説明・目次
内容説明
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.
目次
Introduction. The Cell and Its Basic Mechanisms. Microarrays. Reliability and Reproducibility Issues in DNA Microarray Measurements. Image Processing. Introduction to R. Bioconductor: Principles and Illustrations. Elements of Statistics. Probability Distributions. Basic Statistics in R. Statistical Hypothesis Testing. Classical Approaches to Data Analysis. Analysis of Variance (ANOVA). Linear Models in R. Experiment Design. Multiple Comparisons. Analysis and Visualization Tools. Cluster Analysis. Quality Control. Data Pre-Processing and Normalization. Methods for Selecting Differentially Regulated Genes. The Gene Ontology (GO). Functional Analysis and Biological Interpretation of Microarray Data. Uses, Misuses, and Abuses in GO Profiling. A Comparison of Several Tools for Ontological Analysis. Focused Microarrays - Comparison and Selection. ID Mapping Issues. Pathway Analysis. Machine Learning Techniques. The Road Ahead. References.
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