LEADER 04043nam 22005894a 450 001 9910145753603321 005 20230518151739.0 010 $a1-280-27447-6 010 $a9786610274475 010 $a0-470-01107-6 010 $a0-470-01108-4 035 $a(CKB)1000000000018874 035 $a(EBL)210563 035 $a(OCoLC)209570457 035 $a(SSID)ssj0000251182 035 $a(PQKBManifestationID)11204085 035 $a(PQKBTitleCode)TC0000251182 035 $a(PQKBWorkID)10245582 035 $a(PQKB)10725341 035 $a(MiAaPQ)EBC210563 035 $a(Au-PeEL)EBL210563 035 $a(CaPaEBR)ebr10113961 035 $a(CaONFJC)MIL27447 035 $a(EXLCZ)991000000000018874 100 $a20040405d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistics for microarrays$b[electronic resource] $edesign, analysis, and inference /$fErnst Wit and John McClure 210 $aChichester, England ;$aHoboken, NJ, USA $cJohn Wiley & Sons$dc2004 215 $a1 online resource (279 p.) 300 $aDescription based upon print version of record. 311 $a0-470-84993-2 320 $aIncludes bibliographical references (p. 251-258) and index. 327 $aContents; 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: B?G@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 327 $a3.2 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 Classes 327 $a7.2 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; W 330 $aInterest 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 meaning 606 $aDNA microarrays$xStatistical methods 615 0$aDNA microarrays$xStatistical methods. 676 $a629.04 700 $aWit$b Ernst$0614547 701 $aMcClure$b John D$0989215 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145753603321 996 $aStatistics for microarrays$92262274 997 $aUNINA