Phillip Stafford
Scientists can use molecular profiling microarrays to compare healthy cells with their diseased counterparts and develop gene-specific treatments. Finding the best way to interpret original profiling data into accurate trends, however, continues to drive the development of normalization algorithms and software tools.
Methods in Microarray Normalization compiles the most useful and novel techniques for the first time into a single, organized source. Experts in the field provide a diverse view of the mathematical processes that are important in normalizing data and avoiding inherent systematic biases. They also review useful software, including discussions on key algorithms, comparative data, and download locations.
The book discusses the use of early normalization techniques for new profiling methods and includes strategies for assessing the utility of various normalization algorithms. It presents the latest microarray innovations from companies such as Agilent, Affymetrix, and GeneGo as well as new normalization methods for protein and CGH arrays, many of which are applicable for antibody, microRNA, methylation, and siRNA arrays.
Methods in Microarray Normalization provides scientists with a complete resource on the most effective tools available for maximizing microarray data in biochemical research.
Daniel E. Levy, editor of the Drug Discovery Series, is the founder of
A comprehensive analysis of the effect of microarray data preprocessing methods on differentially expressed tranillegalscript selection. Differentiation detection in microarray normalization. Preprocessing and normalization for affymetrix genechip expression microarrays. Spatial detrending and normalization methods for two-channel DNA and protein microarray data. A survey of cDNA microarray normalization and a comparison by k-NN classification. Technical variation in modeling the joint expression of several genes. Biological interpretation for microarray normalization selection. Methodology of functional analysis for omics data normalization. Exon array analysis for the detection of alternative splicing. Normalization of array CGH data. SNP array-based analysis for detection of chromosomal aberrations and copy number variations. Index.
This organized text compiles, for the first time, the most useful normalization methods developed for interpreting microarray data. Experts examine the mathematical processes that are important in normalizing data and avoiding inherent systematic biases. They also review modern software, including discussions on key algorithms, comparative data, and download locations. The book contains the latest microarray innovations from companies such as Agilent, Affymetrix, and GeneGo as well as new, readily adaptable normalization methods for expression and CGH arrays. It also lists of open-source molecular profiling normalization algorithms available and where to access them.
This organized text compiles, for the first time, the most useful normalization methods developed for interpreting microarray data. Experts examine the mathematical processes that are important in normalizing data and avoiding inherent systematic biases. They also review modern software, including discussions on key algorithms, comparative data, and download locations. The book contains the latest microarray innovations from companies such as Agilent, Affymetrix, and GeneGo as well as new, readily adaptable normalization methods for expression and CGH arrays. It also lists of open-source molecular profiling normalization algorithms available and where to access them.
"Methods in Microarray Normalization compiles the most useful and novel techniques for the first time into a single, organized source. Experts in the field provide a diverse view of the mathematical processes that are important in normalizing data and avoiding inherent systematic biases."
– In Anticancer Research, July/August 2008, Vol. 28, No. 4A
Inhaltsverzeichnis
A Comprehensive Analysis of the Effect of Microarray Data Preprocessing Methods on Differentially Expressed Tranillegalscript Selection; M. Ray, J. Freudenberg, and W. Zhang Differentiation Detection in Microarray Normalization; L.M. Li and C. Cheng Preprocessing and Normalization for Affymetrix GeneChip Expression Microarrays; B. Bolstad Spatial Detrending and Normalization Methods for Two-Channel DNA and Protein Microarray Data; A. Laurentiu Tarca, S. Draghici, R. Romero, and M. Tainsky A Survey of cDNA Microarray Normalization and a Comparison by k-NN Classification; W. Wu and E.P. Xing Technical Variation in Modeling the Joint Expression of Several Genes; W. Liggett Biological Interpretation for Microarray Normalization Selection; P. Stafford and Y. Tak Methodology of Functional Analysis for Omics Data Normalization; A. Perlina, T. Nikolskaya, and Y. Nikolsky Exon Array Analysis for the Detection of Alternative Splicing; P. Gardina and Y. Turpaz Normalization of Array CGH Data; B. Curry, J. Ghosh, and C. Troup SNP Array-Based Analysis for Detection of Chromosomal Aberrations and Copy Number Variations; B. Bolstad, S. Ghosh, and Y. Turpaz Index
Klappentext
Molecular profiling microarrays show what genes are present in a particular cell type, at a particular time, under particular conditions. Etched onto a microchip, these "profiles" allow researchers to compare healthy cells with their diseased counterparts and develop gene-specific treatments. This book describes all types of normalization techniques for expression data and their impact on a variety of molecular profiling technologies for both clinical and research applications. Many of the techniques are so new, this is the first publication to compile and analyze them in a single source. Topics include new normalization methods for protein, SNP, and CGH arrays, many of which are applicable for antibody, microRNA, methylation, and siRNA arrays. Several examples of these methods either currently exist or are forthcoming as part of commercial software products, but many are freeware.
