{"product_id":"statistics-for-microarrays-design-analysis-and-inference-hardback-9780470849934","title":"Statistics for Microarrays; Design, Analysis and Inference (Hardback) 9780470849934","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eStatistics for Microarrays\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eDesign, Analysis and Inference\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eErnst Wit (Author), John McClure (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470849934, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 22 June 2004\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e282 pages\u003cbr\u003e23.8 x 15.4 x 2 cm, 0.539 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"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…\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, June 2006)  \u003cp\u003e\"...clear...up-to-date...lively advice...an excellent reference text for any researcher interested in the analysis of transcriptomic data.\" (\u003ci\u003eShort Book Reviews\u003c\/i\u003e, Vol.25, No.1, April 2005)\u003c\/p\u003e \u003cp\u003e\"...this is a very good introduction to one of the most widely used methods for assessing differential expression...\" (\u003ci\u003eJournal of the Royal Statistical Society\u003c\/i\u003e, Vol 168 (4) 2005)\u003c\/p\u003e \u003cp\u003e\"...presents a coherent and systematic overview of statistical methods in all stages of the process of analysing microarray data...\" (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, Vol.1049, 2004)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eInterest 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. \u003ci\u003eStatistics for Microarrays: Design, Analysis and Inference\u003c\/i\u003e 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.  \u003cul\u003e \u003cli\u003eProvides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference.\u003c\/li\u003e \u003cli\u003eFeatures many examples throughout using real data from microarray experiments.\u003c\/li\u003e \u003cli\u003eComputational techniques are integrated into the text.\u003c\/li\u003e \u003cli\u003eTakes a very practical approach, suitable for statistically-minded biologists.\u003c\/li\u003e \u003cli\u003eSupported by a Website featuring colour images, software, and data sets.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePrimarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Preliminaries.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Using the R Computing Environment.\u003c\/p\u003e \u003cp\u003e1.1.1 Installing smida.\u003c\/p\u003e \u003cp\u003e1.1.2 Loading smida.\u003c\/p\u003e \u003cp\u003e1.2 Data Sets from Biological Experiments.\u003c\/p\u003e \u003cp\u003e1.2.1 Arabidopsis experiment: Anna Amtmann.\u003c\/p\u003e \u003cp\u003e1.2.2 Skin cancer experiment: Nighean Barr.\u003c\/p\u003e \u003cp\u003e1.2.3 Breast cancer experiment: John Bartlett.\u003c\/p\u003e \u003cp\u003e1.2.4 Mammary gland experiment: Gusterson group.\u003c\/p\u003e \u003cp\u003e1.2.5 Tuberculosis experiment: B\u003ci\u003eµ\u003c\/i\u003eG@S group.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Getting Good Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Set-up of a Microarray Experiment.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Nucleic Acids: DNA and RNA.\u003c\/p\u003e \u003cp\u003e2.2 Simple cDNA Spotted Microarray Experiment.\u003c\/p\u003e \u003cp\u003e2.2.1 Growing experimental material.\u003c\/p\u003e \u003cp\u003e2.2.2 Obtaining RNA.\u003c\/p\u003e \u003cp\u003e2.2.3 Adding spiking RNA and poly-T primer.\u003c\/p\u003e \u003cp\u003e2.2.4 Preparing the enzyme environment.\u003c\/p\u003e \u003cp\u003e2.2.5 Obtaining labelled cDNA.\u003c\/p\u003e \u003cp\u003e2.2.6 Preparing cDNA mixture for hybridization.\u003c\/p\u003e \u003cp\u003e2.2.7 Slide hybridization.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Statistical Design of Microarrays.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Sources of Variation.\u003c\/p\u003e \u003cp\u003e3.2 Replication.\u003c\/p\u003e \u003cp\u003e3.2.1 Biological and technical replication.\u003c\/p\u003e \u003cp\u003e3.2.2 How many replicates?\u003c\/p\u003e \u003cp\u003e3.2.3 Pooling samples.\u003c\/p\u003e \u003cp\u003e3.3 Design Principles.\u003c\/p\u003e \u003cp\u003e3.3.1 Blocking, crossing and randomization.\u003c\/p\u003e \u003cp\u003e3.3.2 Design and normalization.\u003c\/p\u003e \u003cp\u003e3.4 Single-channelMicroarray Design.\u003c\/p\u003e \u003cp\u003e3.4.1 Design issues.\u003c\/p\u003e \u003cp\u003e3.4.2 Design layout.\u003c\/p\u003e \u003cp\u003e3.4.3 Dealing with technical replicates.\u003c\/p\u003e \u003cp\u003e3.5 Two-channelMicroarray Designs.\u003c\/p\u003e \u003cp\u003e3.5.1 Optimal design of dual-channel arrays.\u003c\/p\u003e \u003cp\u003e3.5.2 Several practical two-channel designs.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Normalization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Image Analysis.\u003c\/p\u003e \u003cp\u003e4.1.1 Filtering.\u003c\/p\u003e \u003cp\u003e4.1.2 Gridding.\u003c\/p\u003e \u003cp\u003e4.1.3 Segmentation.\u003c\/p\u003e \u003cp\u003e4.1.4 Quantification.\u003c\/p\u003e \u003cp\u003e4.2 Introduction to Normalization.\u003c\/p\u003e \u003cp\u003e4.2.1 Scale of gene expression data.\u003c\/p\u003e \u003cp\u003e4.2.2 Using control spots for normalization.\u003c\/p\u003e \u003cp\u003e4.2.3 Missing data.\u003c\/p\u003e \u003cp\u003e4.3 Normalization for Dual-channel Arrays.\u003c\/p\u003e \u003cp\u003e4.3.1 Order for the normalizations.\u003c\/p\u003e \u003cp\u003e4.3.2 Spatial correction.\u003c\/p\u003e \u003cp\u003e4.3.3 Background correction.\u003c\/p\u003e \u003cp\u003e4.3.4 Dye effect normalization.\u003c\/p\u003e \u003cp\u003e4.3.5 Normalization within and across conditions.\u003c\/p\u003e \u003cp\u003e4.4 Normalization of Single-channel Arrays.\u003c\/p\u003e \u003cp\u003e4.4.1 Affymetrix data structure.\u003c\/p\u003e \u003cp\u003e4.4.2 Normalization of Affymetrix data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Quality Assessment.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Using MIAME in Quality Assessment.\u003c\/p\u003e \u003cp\u003e5.1.1 Components of MIAME.\u003c\/p\u003e \u003cp\u003e5.2 Comparing Multivariate Data.\u003c\/p\u003e \u003cp\u003e5.2.1 Measurement scale.\u003c\/p\u003e \u003cp\u003e5.2.2 Dissimilarity and distance measures.\u003c\/p\u003e \u003cp\u003e5.2.3 Representing multivariate data.\u003c\/p\u003e \u003cp\u003e5.3 Detecting Data Problems.\u003c\/p\u003e \u003cp\u003e5.3.1 Clerical errors.\u003c\/p\u003e \u003cp\u003e5.3.2 Normalization problems.\u003c\/p\u003e \u003cp\u003e5.3.3 Hybridization problems.\u003c\/p\u003e \u003cp\u003e5.3.4 Array mishandling.\u003c\/p\u003e \u003cp\u003e5.4 Consequences of Quality Assessment Checks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Microarray Myths: Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Design.\u003c\/p\u003e \u003cp\u003e6.1.1 Single-versus dual-channel designs?\u003c\/p\u003e \u003cp\u003e6.1.2 Dye-swap experiments.\u003c\/p\u003e \u003cp\u003e6.2 Normalization.\u003c\/p\u003e \u003cp\u003e6.2.1 Myth: ‘microarray data is Gaussian’.\u003c\/p\u003e \u003cp\u003e6.2.2 Myth: ‘microarray data is not Gaussian’.\u003c\/p\u003e \u003cp\u003e6.2.3 Confounding spatial and dye effect.\u003c\/p\u003e \u003cp\u003e6.2.4 Myth: ‘non-negative background subtraction’.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Getting Good Answers.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Microarray Discoveries.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Discovering Sample Classes.\u003c\/p\u003e \u003cp\u003e7.1.1 Why cluster samples?\u003c\/p\u003e \u003cp\u003e7.1.2 Sample dissimilarity measures.\u003c\/p\u003e \u003cp\u003e7.1.3 Clustering methods for samples.\u003c\/p\u003e \u003cp\u003e7.2 Exploratory Supervised Learning.\u003c\/p\u003e \u003cp\u003e7.2.1 Labelled dendrograms.\u003c\/p\u003e \u003cp\u003e7.2.2 Labelled PAM-type clusterings.\u003c\/p\u003e \u003cp\u003e7.3 Discovering Gene Clusters.\u003c\/p\u003e \u003cp\u003e7.3.1 Similarity measures for expression profiles.\u003c\/p\u003e \u003cp\u003e7.3.2 Gene clustering methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Differential Expression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.1.1 Classical versus Bayesian hypothesis testing.\u003c\/p\u003e \u003cp\u003e8.1.2 Multiple testing ‘problem’.\u003c\/p\u003e \u003cp\u003e8.2 Classical Hypothesis Testing.\u003c\/p\u003e \u003cp\u003e8.2.1 What is a hypothesis test?\u003c\/p\u003e \u003cp\u003e8.2.2 Hypothesis tests for two conditions.\u003c\/p\u003e \u003cp\u003e8.2.3 Decision rules.\u003c\/p\u003e \u003cp\u003e8.2.4 Results from skin cancer experiment.\u003c\/p\u003e \u003cp\u003e8.3 Bayesian Hypothesis Testing.\u003c\/p\u003e \u003cp\u003e8.3.1 A general testing procedure.\u003c\/p\u003e \u003cp\u003e8.3.2 Bayesian \u003ci\u003et\u003c\/i\u003e-test.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Predicting Outcomes with Gene Expression Profiles.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.1.1 Probabilistic classification theory.\u003c\/p\u003e \u003cp\u003e9.1.2 Modelling and predicting continuous variables.\u003c\/p\u003e \u003cp\u003e9.2 Curse of Dimensionality: Gene Filtering.\u003c\/p\u003e \u003cp\u003e9.2.1 Use only significantly expressed genes.\u003c\/p\u003e \u003cp\u003e9.2.2 PCA and gene clustering.\u003c\/p\u003e \u003cp\u003e9.2.3 Penalized methods.\u003c\/p\u003e \u003cp\u003e9.2.4 Biological selection.\u003c\/p\u003e \u003cp\u003e9.3 Predicting ClassMemberships.\u003c\/p\u003e \u003cp\u003e9.3.1 Variance-bias trade-off in prediction.\u003c\/p\u003e \u003cp\u003e9.3.2 Linear discriminant analysis.\u003c\/p\u003e \u003cp\u003e9.3.3 \u003ci\u003ek\u003c\/i\u003e-nearest neighbour classification.\u003c\/p\u003e \u003cp\u003e9.4 Predicting Continuous Responses.\u003c\/p\u003e \u003cp\u003e9.4.1 Penalized regression: LASSO.\u003c\/p\u003e \u003cp\u003e9.4.2 \u003ci\u003ek\u003c\/i\u003e-nearest neighbour regression.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Microarray Myths: Inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Differential Expression.\u003c\/p\u003e \u003cp\u003e10.1.1 Myth: ‘Bonferroni is too conservative’.\u003c\/p\u003e \u003cp\u003e10.1.2 FPR and collective multiple testing.\u003c\/p\u003e \u003cp\u003e10.1.3 Misinterpreting FDR.\u003c\/p\u003e \u003cp\u003e10.2 Prediction and Learning.\u003c\/p\u003e \u003cp\u003e10.2.1 Cross-validation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52278037610776,"sku":"9780470849934","price":81.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470849934.jpg?v=1781457087","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/statistics-for-microarrays-design-analysis-and-inference-hardback-9780470849934","provider":"Freshly Printed Books","version":"1.0","type":"link"}