A Biologist's Guide to Analysis of DNA Microarray Data
A great introductory book that details reliable approaches to problems met in standard microarray data analyses. It provides examples of established approaches such as cluster analysis, function prediction, and principle component analysis. Discover real examples to illustrate the key concepts of data analysis. Written for those without any advanced background in math, statistics, or computer sciences, this book is essential for anyone interested in harnessing the immense potential of microarrays in biology and medicine.
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2 Overview of Data Analysis
3 Basic Data Analysis
4 Visualization by Reduction of Dimensionality
5 Cluster Analysis
6 Beyond Cluster Analysis
7 Reverse Engineering of Regulatory Networks
8 Molecular Classifiers
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AAAAA Affymetrix Affymetrix chip Affymetrix GeneChip algorithm analysis of gene ANOVA array data Average Difference AvgDiff Bioinformatics Biology bladder cancer Bonferroni correction Brunak calculate cDNA cell classiﬁcation ClustArray data analysis database detection distance matrix DNA array DNA microarrays down-regulated Euclidean distance example expression analysis expression level expression proﬁles false positives Figure ﬁle ﬁnd ﬁrst principal component fold change four genes function gene expression gene expression data GeneChip genetic network Genome Hierarchical clustering hybridization identiﬁed Knudsen labeled line ending Logfold messenger RNA method microarray data mRNA Natl neural network nucleotide number of genes number of replicates oligonucleotide oligos online at http://psb.stanford.edu P-value Paciﬁc Symposium perform prediction primers principal component analysis probe pairs promoter regions regulatory effect regulatory network sample sequence signiﬁcance signiﬁcantly speciﬁc spotted array statistical steady-state subtype Symposium on Biocomputing t-test Thykjaer tumors Unix/Linux vector angle distance visualization Workman