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008			080807s2004 enka b 001 0 eng d
010			_a 2004045909
011			_aBIB MATCHES WORLDCAT
020			_a0470849932 _qhbk. (alk. paper)
020			_a9780470849934 _qhbk. (alk. paper)
035			_a(ATU)b11370804
035			_a(OCoLC)54953050
040			_aDLC _beng _erda _cDLC _dNLM _dBAKER _dAGL _dNLGGC _dBTCTA _dUBA _dYDXCP _dSTF _dATU
050	0	0	_aQP624.5.D726 _bW54 2004
082	0	0	_a629.04 _222
100	1		_aWit, Ernst, _eauthor. _91069478
245	1	0	_aStatistics for microarrays : _bdesign, analysis, and inference / _cErnst Wit and John McClure.
264		1	_aChichester, England ; _aHoboken, NJ, USA : _bJohn Wiley & Sons, _c[2004]
264		4	_c©2004
300			_axii, 265 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references (pages 251-258) and index.
505	0	0	_g1. _tPreliminaries -- _g1.1. _tUsing the R Computing Environment -- _g1.1.1. _tInstalling smida -- _g1.1.2. _tLoading smida -- _g1.2. _tData Sets from Biological Experiments -- _g1.2.1. _tArabidopsis experiment: Anna Amtmann -- _g1.2.2. _tSkin cancer experiment: Nighean Barr -- _g1.2.3. _tBreast cancer experiment: John Bartlett -- _g1.2.4. _tMammary gland experiment: Gusterson group -- _g1.2.5. _tTuberculosis experiment: BuG@S group -- _tI Getting Good Data -- _g2. _tSet-up of a Microarray Experiment -- _g2.1. _tNucleic Acids: DNA and RNA -- _g2.2. _tSimple cDNA Spotted Microarray Experiment -- _g2.2.1. _tGrowing experimental material -- _g2.2.2. _tObtaining RNA -- _g2.2.3. _tAdding spiking RNA and poly-T primer -- _g2.2.4. _tPreparing the enzyme environment -- _g2.2.5. _tObtaining labelled cDNA -- _g2.2.6. _tPreparing cDNA mixture for hybridization -- _g2.2.7. _tSlide hybridization -- _g3. _tStatistical Design of Microarrays -- _g3.1. _tSources of Variation -- _g3.2. _tReplication -- _g3.2.1. _tBiological and technical replication -- _g3.2.2. _tHow many replicates? -- _g3.2.3. _tPooling samples -- _g3.3. _tDesign Principles -- _g3.3.1. _tBlocking, crossing and randomization -- _g3.3.2. _tDesign and normalization -- _g3.4. _tSingle-channelMicroarray Design -- _g3.4.1. _tDesign issues -- _g3.4.2. _tDesign layout -- _g3.4.3. _tDealing with technical replicates -- _g3.5. _tTwo-channelMicroarray Designs -- _g3.5.1. _tOptimal design of dual-channel arrays -- _g3.5.2. _tSeveral practical two-channel designs -- _g4. _tNormalization -- _g4.1. _tImage Analysis -- _g4.1.1. _tFiltering -- _g4.1.2. _tGridding -- _g4.1.3. _tSegmentation -- _g4.1.4. _tQuantification -- _g4.2. _tIntroduction to Normalization -- _g4.2.1. _tScale of gene expression data -- _g4.2.2. _tUsing control spots for normalization -- _g4.2.3. _tMissing data -- _g4.3. _tNormalization for Dual-channel Arrays -- _g4.3.1. _tOrder for the normalizations -- _g4.3.2. _tSpatial correction -- _g4.3.3. _tBackground correction -- _g4.3.4. _tDye effect normalization -- _g4.3.5. _tNormalization within and across conditions -- _g4.4. _tNormalization of Single-channel Arrays -- _g4.4.1. _tAffymetrix data structure -- _g4.4.2. _tNormalization of Affymetrix data -- _g5. _tQuality Assessment -- _g5.1. _tUsing MIAME in Quality Assessment -- _g5.1.1. _tComponents of MIAME -- _g5.2. _tComparing Multivariate Data -- _g5.2.1. _tMeasurement scale -- _g5.2.2. _tDissimilarity and distance measures -- _g5.2.3. _tRepresenting multivariate data -- _g5.3. _tDetecting Data Problems -- _g5.3.1. _tClerical errors -- _g5.3.2. _tNormalization problems -- _g5.3.3. _tHybridization problems -- _g5.3.4. _tArray mishandling -- _g5.4. _tConsequences of Quality Assessment Checks -- _g6. _tMicroarray Myths: Data -- _g6.1. _tDesign -- _g6.1.1. _tSingle-versus dual-channel designs? -- _g6.1.2. _tDye-swap experiments -- _g6.2. _tNormalization -- _g6.2.1. _tMyth: 'microarray data is Gaussian' -- _g6.2.2. _tMyth: 'microarray data is not Gaussian' -- _g6.2.3. _tConfounding spatial and dye effect -- _g6.2.4. _tMyth: 'non-negative background subtraction' -- _tII Getting Good Answers -- _g7. _tMicroarray Discoveries -- _g7.1. _tDiscovering Sample Classes -- _g7.1.1. _tWhy cluster samples? -- _g7.1.2. _tSample dissimilarity measures -- _g7.1.3. _tClustering methods for samples -- _g7.2. _tExploratory Supervised Learning -- _g7.2.1. _tLabelled dendrograms -- _g7.2.2. _tLabelled PAM-type clusterings -- _g7.3. _tDiscovering Gene Clusters -- _g7.3.1. _tSimilarity measures for expression profiles -- _g7.3.2. _tGene clustering methods -- _g8. _tDifferential Expression -- _g8.1. _tIntroduction -- _g8.1.1. _tClassical versus Bayesian hypothesis testing -- _g8.1.2. _tMultiple testing 'problem' -- _g8.2. _tClassical Hypothesis Testing -- _g8.2.1. _tWhat is a hypothesis test? -- _g8.2.2. _tHypothesis tests for two conditions -- _g8.2.3. _tDecision rules -- _g8.2.4. _tResults from skin cancer experiment -- _g8.3. _tBayesian Hypothesis Testing -- _g8.3.1. _tA general testing procedure -- _g8.3.2. _tBayesian t-test -- _g9. _tPredicting Outcomes with Gene Expression Profiles -- _g9.1. _tIntroduction -- _g9.1.1. _tProbabilistic classification theory -- _g9.1.2. _tModelling and predicting continuous variables -- _g9.2. _tCurse of Dimensionality: Gene Filtering -- _g9.2.1. _tUse only significantly expressed genes -- _g9.2.2. _tPCA and gene clustering -- _g9.2.3. _tPenalized methods -- _g9.2.4. _tBiological selection -- _g9.3. _tPredicting ClassMemberships -- _g9.3.1. _tVariance-bias trade-off in prediction -- _g9.3.2. _tLinear discriminant analysis -- _g9.3.3. _tk-nearest neighbour classification -- _g9.4. _tPredicting Continuous Responses -- _g9.4.1. _tPenalized regression: LASSO -- _g9.4.2. _tk-nearest neighbour regression -- _g10. _tMicroarray Myths: Inference -- _g10.1. _tDifferential Expression -- _g10.1.1. _tMyth: 'Bonferroni is too conservative' -- _g10.1.2. _tFPR and collective multiple testing -- _g10.1.3. _tMisinterpreting FDR -- _g10.2. _tPrediction and Learning -- _g10.2.1. _tCross-validation.
520			_a"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."--Publisher's website.
588			_aMachine converted from AACR2 source record.
650		0	_aDNA microarrays _xStatistical methods _9656445
650		2	_aOligonucleotide Array Sequence Analysis _xmethods _9357927
650		2	_aStatistics as Topic _xmethods _9359580
700	1		_aMcClure, John D., _eauthor. _91069479
907			_a.b11370804 _b10-05-18 _c27-10-15
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