04040nam 22006014a 450 991081337900332120200520144314.01-280-27447-697866102744750-470-01107-60-470-01108-4(CKB)1000000000018874(EBL)210563(OCoLC)209570457(SSID)ssj0000251182(PQKBManifestationID)11204085(PQKBTitleCode)TC0000251182(PQKBWorkID)10245582(PQKB)10725341(MiAaPQ)EBC210563(Au-PeEL)EBL210563(CaPaEBR)ebr10113961(CaONFJC)MIL27447(EXLCZ)99100000000001887420040405d2004 uy 0engur|n|---|||||txtccrStatistics for microarrays design, analysis, and inference /Ernst Wit and John McClure1st ed.Chichester, England ;Hoboken, NJ, USA John Wiley & Sonsc20041 online resource (279 p.)Description based upon print version of record.0-470-84993-2 Includes bibliographical references (p. 251-258) and index.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 Variation3.2 Replication3.3 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 Classes7.2 Exploratory Supervised Learning7.3 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; WInterest 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 meaningDNA microarraysStatistical methodsDNA microarraysStatistical methods.629.04Wit Ernst614547McClure John D989215MiAaPQMiAaPQMiAaPQBOOK9910813379003321Statistics for microarrays2262274UNINA