Statistics and Data Analysis for Microarrays Using R and Bioconductor (Chapman & Hall/CRC Computational Biology Series): Second Edition

Oversized hardcover, weight 2kg, 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.…