Oversized hardcover, weight 2kg (please note: extra shipping may be required), xlviii + 1042 pages, NOT ex-library. Unused CD-ROM still sealed in rear pocket. Book is clean and bright throughout with unmarked text, free of inscriptions and stamps, firmly bound. Gentle wear to tips of outer corners of a portion of leaves. Boards show short creases to corners, small indentations. Issued without a dust jacket. -- Contents: 1 Introduction [Bioinformatics: an emerging discipline] 2 Cell and its basic mechanisms [The cell; Building blocks of genomic information; Expression of genetic information; Need for high-throughput methods] 3 Microarrays [Microarrays: tools for gene expression analysis; Fabrication & Applications of microarrays; Challenges in using microarrays in gene expression studies; Sources of variability] 4 Reliability and reproducibility issues in DNA microarray measurements [What is expected from microarrays?; Basic considerations of microarray measurements; Sensitivity; Accuracy; Reproducibility; Cross-platform consistency; Sources of inaccuracy and inconsistencies in microarray measurements; MicroArray Quality Control (MAQC) project] 5 Image processing [Basic elements of digital imaging; Microarray image processing; Image processing of cDNA microarrays & Affymetrix arrays] 6 Introduction to R [Basic concepts; Data structures and functions; Other capabilities; R environment; Installing Bioconductor; Graphics; Control structures in R; Programming in R vs C/C++/Java; Summary; Solved Exercises; Exercises] 7 Bioconductor: principles and illustrations [Overview; Portal; Some explorations and analyses] 8 Elements of statistics [Some basic concepts; Elementary statistics; Degrees of freedom; Probabilities; Bayes' theorem; Testing for (or predicting) a disease; Solved problems; Exercises] 9 Probability distributions 10 Basic statistics in R [Descriptive statistics in R; Probabilities and distributions in R; Central limit theorem; Exercises] 11 Statistical hypothesis testing [Framework; Hypothesis testing and significance; 'I do not believe God does not exist'; An algorithm for hypothesis testing; Errors in hypothesis testing; Solved problems] 12 Classical approaches to data analysis [Tests involving a single sample & two samples; Exercises] 13 Analysis of Variance ? ANOVA [One- & Two-way ANOVA; Quality control; Exercises] 14 Linear models in R 15 Experiment design 16 Multiple comparisons 17 Analysis and visualization tools [Box plots; Gene pies; Scatter plots; Volcano plots; Histograms; Time series; Time series plots in R; Principal component analysis (PCA); Independent component analysis (ICA)] 18 Cluster analysis 19 Quality control 20 Data preprocessing and normalization 21 Methods for selecting differentially expressed genes 22 Gene Ontology (GO) 23 Functional analysis and biological interpretation of microarray data 24 Uses, misuses, and abuses in GO profiling 25 A comparison of several tools for ontological analysis 26 Focused microarrays ? comparison and selection 27 ID Mapping issues 28 Pathway analysis [Introduction; Terms and problem definition; Over-representation and functional class scoring approaches in pathway analysis; An approach for the analysis of metabolic pathways; An impact analysis of signaling pathways; Variations on the impact analysis theme; Pathway Guide; Kinetic models versus impact analysis; Conclusions; Data sets and software availability] 29 Machine learning techniques [Introduction; Main concepts and definitions; Supervised learning; Practicalities using R] 30 The road ahead [What next?]; Bibliography; Index.

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Detalles bibliogr�ficos

T�tulo: Statistics and Data Analysis for Microarrays Using R and Bioconductor (Chapman & Hall/CRC Computational Biology Series): Second Edition
Editor: Chapman and Hall/CRC
Fecha de publicaci�n: 2012
Idioma: Ingl�s
ISBN 10: 1439809755
ISBN 13: 9781439809754
Encuadernaci�n: Hardcover
Edici�n: 2nd Edition
Estado: Very Good

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Sinopsis

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.

New to the Second EditionCompletely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying downloadable resource.

With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

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Sorin Drăghici the Robert J. Sokol MD Endowed Chair in Systems Biology in the Department of Obstetrics and Gynecology, professor in the Department of Clinical and Translational Science and Department of Computer Science, and head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University. He is also the chief of the Bioinformatics and Data Analysis Section in the Perinatology Research Branch of the National Institute for Child Health and Development. A senior member of IEEE, Dr. Drăghici is an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine. He earned a Ph.D. in computer science from the University of St. Andrews.

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