Microarrays data analysis

Leung YF, Cavalieri D. 2003. Fundamentals of cDNA microarray data analysis. Trends in Genetics 19 649. 

Smyth GK, Yang YH, Speed T. 2003. Statistical issues in cDNA microarray data analysis. Methods Mol Biol 224 111. 

Valafar R 2002. Pattern recognition techniques in microarray data analysis a survey. Ann NY Acad Sci 980 41. 

Hu, D. (2003) Microarray data analysis in studies of membrane transporters, in Membrane transporters methods and protocols (Q. Yan, ed.). Methods in Molecular Biology, Humana, Totowa, NJ, pp. 71-84. 

Allison DB et al (2006) Microarray data analysis firom disarray to consolidation and consensus. Nat Rev Genet 7 55-65. doi nrgl749 (pii)10.1038/nrgl749... 

Demrrkale CY, Nettleton D, Maiti T (2010) Linear mixed model selection for false discovery rate control in microarray data analysis. Biometrics 66 621-629. doi BIOM1286 (pii)10.1111/j.l541-0420.2009.01286.x... 

Okada, Y., and Fujibuchi, W. (2007) Mining a Large-scale Microarray Database for Similar Gene Expression Modules to Find Distant Relationships between Down Syndrome and Huntington s Disease. Proceedings of Critical Assessment of Microarray Data Analysis 07, Valencia, Spain. 

Scan the array at 550 nm using a DNA microarray scanner and process the data using a DNA microarray data analysis software package (Fig. 5 see Note 12). 

Data analysis Training with the technical experts of PerkinElmer is necessary before performing microarray data analysis using the software package. 

Mateos A, Herrero J, Tamames J, Dopazo J, Supervised neural networks for clustering conditions in DNA array data after reducing noise by clustering gene expression profiles, In Lin SM, Johnson KF, eds., Methods of Microarray Data Analysis II, Boston, Kluwer Academic Publ, pp. 91-103, 2002. 

Sluka JP. Extracting knowledge from genomic experiments by incorporating the biomedical literature. In Lin SM, Johnson KF, editors, Methods of microarray data analysis II Boston Kluwer Academics, 2002. 

Lin SM Johnson, KF, Eds. Methods of microarray data analysis, Boston Kluwer Academic Publishers, 2002. 

Research on microarrays, especially on techniques for microarray data analysis, and with microarrays, that is, applications that use microarrays, has been very active during the first decade of the century (4,5), and thousands of papers on their use, applications, and analysis have been published, as can be seen by searching PubMed for references with the term microarray in their title (see Figure 2) (6). In this chapter the basic... 

A whole spectrum of statistical techniques have been applied to the analysis of DNA microarray data [26-28]. These include clustering analysis (hierarchical, K-means, self-organizing maps), dimension reduction (singular value decomposition, principal component analysis, multidimensional scaling, or correspondence analysis), and supervised classification (support vector machines, artificial neural networks, discriminant methods, or between-group analysis) methods. More recently, a number of Bayesian and other probabilistic approaches have been employed in the analysis of DNA microarray data [11], Generally, the first phase of microarray data analysis is exploratory data analysis. 

Expression Profiler An online-based microarray data analysis tool provided by the European Bioinformatics Institute [35]... 

Wall, M. E., Rechtsteiner, A., and Rocha, L. M. 2003. Singular value decomposition and principal component analysis. In A Practical Approach to Microarray Data Analysis, (eds. D. R Berrar, W. Dubitzky, M. Granzow), pp. 91-109, Norwell, MA Kluwer. 

Dasgupta, L., Lin, S. M., Carin, L. 2002. Modeling Pharmacogenomics of the NCI-60 Anticancer Data Set Utilizing kernel PLS to correlate the Microarray Data to Therapeutic Responses. In Methods of Microarray Data Analysis II (ed. S. Lin and K. M. Johnson). Kluwer Academic Publishers. 

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