Methods of Microarray Data Analysis III: Papers from CAMDA '02 / Edition 1by Kimberly F. Johnson
Pub. Date: 09/28/2003
Publisher: Springer US
As microarray technology has matured, data analysis methods have advanced as well. Methods Of Microarray Data Analysis III is the third book in this pioneering series dedicated to the existing new field of microarrays. While initial techniques focused on classification exercises (volume I of this series), and later on pattern extraction (volume II of this series),… See more details below
As microarray technology has matured, data analysis methods have advanced as well. Methods Of Microarray Data Analysis III is the third book in this pioneering series dedicated to the existing new field of microarrays. While initial techniques focused on classification exercises (volume I of this series), and later on pattern extraction (volume II of this series), this volume focuses on data quality issues. Problems such as background noise determination, analysis of variance, and errors in data handling are highlighted. Three tutorial papers are presented to assist with a basic understanding of underlying principles in microarray data analysis, and twelve new papers are highlighted analyzing the same CAMDA'02 datasets: the Project Normal data set or the Affymetrix Latin Square data set. A comparative study of these analytical methodologies brings to light problems, solutions and new ideas. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of art of microarray data analysis.
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Table of ContentsContributing Authors. Preface. Introduction. Section I: Tutorials. The Biology Behind Gene Expression: A Basic Tutorial; M.F. Ochs, E.A. Golemis. Monitoring the Quality of Microarray Experiments; K.R. Coombes, Jing Wang, L.V. Abruzzo. Outliers in Microarray Data Analysis; R.K. Pearson, G.E. Gonye, J.S. Schwaber. Section II: Best Presentation Award. Organ-Specific Differences in Gene Expression and Unigene Annotations Describing Source Material; D.N. Stivers, Jing Wang, G.L. Rosner, K.R. Coombes. Section III: Analyzing Images. Characterization, Modeling, and Simulation of Mouse Microarray Data; D.S. Lalush. Topological Adjustments to Genechip Expression Values; A. Ptitsyn. Section IV: Normalizing Raw Data. Comparison of Normalization Methods for CDNA Microarrays; L. Warren, B. Liu. Section V: Characterizing Technical and Biological Variance. Simultaneous Assessment of Transcriptomic Variability and Tissue Effects in the Normal Mouse; Shibing Deng, Tzu-Ming Chu, R. Wolfinger. How Many Mice and How Many Arrays? Replication in Mouse CDNA Microarray Experiments; Xiangqin Cui, G.A. Churchill. Bayesian Characterization of Natural Variation in Gene Expression; M. Bhattacharjee, C. Pritchard, M.J. Sillanpää, E. Arjas. Section VI: Investigating Cross Hybridization On Oligonucleotide Microarrays. Quantification of Cross Hybridization on Oligonucleotide Microarrays; Li Zhang, K.R. Coombes, Lianchun Xiao. Assessing The Potential Effect Of Cross-Hybridization On Oligonucleotide Microarrays; S. Kachalo, Z. Arbieva, Jie Liang. Who Are Those Strangers in the Latin Square? Wen-Ping Hsieh, Tzu-Ming Chu, R. Wolfinger. Section VII: Finding Patterns and Seeking Biological Explanations. Bayesian Decomposition Classification of the Project Normal Data Set; T.D. Moloshok, D. Datta, A.V. Kossenkov, M.F. Ochs. The Use of Go Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data; R. Díaz-Uriarte, F. Al-Shahrour, J. Dopazo.Acknowledgments. Index.
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