Statistics for Microarrays
Design, Analysis and Inference
Published on 22. June 2004
Book
Hardback
278 pages
978-0-470-84993-4 (ISBN)
Description
Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data - from getting good data to obtaining meaningful results.
* Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference.
* Features many examples throughout using real data from microarray experiments.
* Computational techniques are integrated into the text.
* Takes a very practical approach, suitable for statistically-minded biologists.
* Supported by a Website featuring colour images, software, and data sets.
Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.
Reviews / Votes
"I liked this book and would recommend it to any statistician new to microarray data analysis...a unique combination of features that make it a contender among the standard textbooks..." (Journal of the American Statistical Association, June 2006) "...clear...up-to-date...lively advice...an excellent reference text for any researcher interested in the analysis of transcriptomic data." (Short Book Reviews, Vol.25, No.1, April 2005)"...this is a very good introduction to one of the most widely used methods for assessing differential expression..." (Journal of the Royal Statistical Society, Vol 168 (4) 2005)
"...presents a coherent and systematic overview of statistical methods in all stages of the process of analysing microarray data..." (Zentralblatt Math, Vol.1049, 2004)
More details
Product info
gebunden
Edition
1. Auflage
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
College/higher education
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 20 mm
Weight
559 gr
ISBN-13
978-0-470-84993-4 (9780470849934)
Schweitzer Classification
Other editions
Additional editions
E-Book
10/2004
Wiley
€94.99
Available for download
Persons
Ernst Wit is the author of Statistics for Microarrays: Design, Analysis and Inference, published by Wiley.
John McClure is the author of Statistics for Microarrays: Design, Analysis and Inference, published by Wiley.
John McClure is the author of Statistics for Microarrays: Design, Analysis and Inference, published by Wiley.
Content
Preface.
1 Preliminaries.
1.1 Using the R Computing Environment.
1.2 Data Sets from Biological Experiments.
I Getting Good Data.
2 Set-up of a Microarray Experiment.
2.1 Nucleic Acids: DNA and RNA.
2.2 Simple cDNA Spotted Microarray Experiment.
3 Statistical Design of Microarrays.
3.1 Sources of Variation.
3.2 Replication.
3.3 Design Principles.
3.4 Single-channelMicroarray Design.
3.5 Two-channelMicroarray Designs.
4 Normalization.
4.1 Image Analysis.
4.2 Introduction to Normalization.
4.3 Normalization for Dual-channel Arrays.
4.4 Normalization of Single-channel Arrays.
5 Quality Assessment.
5.1 Using MIAME in Quality Assessment.
5.2 Comparing Multivariate Data.
5.3 Detecting Data Problems.
5.4 Consequences of Quality Assessment Checks.
6 Microarray Myths: Data.
6.1 Design.
6.2 Normalization.
II Getting Good Answers.
7 Microarray Discoveries.
7.1 Discovering Sample Classes.
7.2 Exploratory Supervised Learning.
7.3 Discovering Gene Clusters.
8 Differential Expression.
8.1 Introduction.
8.2 Classical Hypothesis Testing.
8.3 Bayesian Hypothesis Testing.
9 Predicting Outcomes with Gene Expression Profiles.
9.1 Introduction.
9.2 Curse of Dimensionality: Gene Filtering.
9.3 Predicting ClassMemberships.
9.4 Predicting Continuous Responses.
10 Microarray Myths: Inference.
10.1 Differential Expression.
10.2 Prediction and Learning.
Bibliography.
Index.