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Statistics for Microarrays : Design, Analysis and Inference.

By: Contributor(s): Publisher: Hoboken : John Wiley & Sons, Incorporated, 2004Copyright date: ©2005Edition: 1st edDescription: 1 online resource (279 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780470011072
Subject(s): Genre/Form: Additional physical formats: Print version:: Statistics for Microarrays : Design, Analysis and InferenceDDC classification:
  • 629.04
LOC classification:
  • QP624.5.D726W54 2004
Online resources:
Contents:
Intro -- Contents -- Preface -- 1 Preliminaries -- 1.1 Using the R Computing Environment -- 1.1.1 Installing smida -- 1.1.2 Loading smida -- 1.2 Data Sets from Biological Experiments -- 1.2.1 Arabidopsis experiment: Anna Amtmann -- 1.2.2 Skin cancer experiment: Nighean Barr -- 1.2.3 Breast cancer experiment: John Bartlett -- 1.2.4 Mammary gland experiment: Gusterson group -- 1.2.5 Tuberculosis experiment: BμG@S group -- 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-channel Microarray Design -- 3.5 Two-channel Microarray 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 Class Memberships -- 9.4 Predicting Continuous Responses -- 10 Microarray Myths: Inference -- 10.1 Differential Expression -- 10.2 Prediction and Learning -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V.
W.
Summary: 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.
Holdings
Item type Current library Call number Status Date due Barcode Item holds
Ebrary Ebrary Afghanistan Available EBKAF0006241
Ebrary Ebrary Algeria Available
Ebrary Ebrary Cyprus Available
Ebrary Ebrary Egypt Available
Ebrary Ebrary Libya Available
Ebrary Ebrary Morocco Available
Ebrary Ebrary Nepal Available EBKNP0006241
Ebrary Ebrary Sudan Available
Ebrary Ebrary Tunisia Available
Total holds: 0

Intro -- Contents -- Preface -- 1 Preliminaries -- 1.1 Using the R Computing Environment -- 1.1.1 Installing smida -- 1.1.2 Loading smida -- 1.2 Data Sets from Biological Experiments -- 1.2.1 Arabidopsis experiment: Anna Amtmann -- 1.2.2 Skin cancer experiment: Nighean Barr -- 1.2.3 Breast cancer experiment: John Bartlett -- 1.2.4 Mammary gland experiment: Gusterson group -- 1.2.5 Tuberculosis experiment: BμG@S group -- 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-channel Microarray Design -- 3.5 Two-channel Microarray 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 Class Memberships -- 9.4 Predicting Continuous Responses -- 10 Microarray Myths: Inference -- 10.1 Differential Expression -- 10.2 Prediction and Learning -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V.

W.

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.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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