Methods in Microarray Normalization

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 DEL BioPharma, a consulting service for drug discovery programs. He also maintains a blog that explores organic chemistry.

A Comprehensive Analysis of the Effect of Microarray Data Preprocessing Methods on Differentially Expressed Transcript 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