A Practical Approach to Microarray Data Analysis by Daniel P. Berrar, Werner Dubitzky, Martin Granzow PDF

By Daniel P. Berrar, Werner Dubitzky, Martin Granzow

ISBN-10: 1402072600

ISBN-13: 9781402072604

The booklet addresses the requirement of scientists and researchers to achieve a simple realizing of microarray research methodologies and instruments. it's meant for college kids, lecturers, researchers, and study managers who are looking to comprehend the cutting-edge and of the awarded methodologies and the parts during which gaps in our wisdom call for additional examine and improvement. The ebook is designed for use through the practising expert tasked with the layout and research of microarray experiments or as a textual content for a senior undergraduate- or graduate point path in analytical genetics, biology, bioinformatics, computational biology, information and information mining, or utilized machine technology.

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2009 Sep 9;10:86 Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, Wu W, Uitdehaag BMJ, Kappos L, GeneMSA Consortium, Polman CH, Matthews PM, Hauser SL, Gibson RA, Oksenberg JR, Barnes MR (2009) Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 18:2078–2090. 1093/hmg/ddp120 Greene CS, Penrod NM, Kiralis J, Moore JH (2009) Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions.

16. 17. 18. 19. 20. 21. 22. 23. 24. 9 × 1011 pair-wise interaction tests. Hum Hered 69:268–284. 1159/000295896 Evans DM, Marchini J, Morris AP, Cardon LR (2006) Two-stage two-locus models in genome-wide association. PLoS Genet 2:e157. 0020157 Ueki M, Cordell HJ (2012) Improved statistics for genome-wide interaction analysis. PLoS Genet 8:e1002625. 1002625 Herold C, Steffens M, Brockschmidt FF, Baur MP, Becker T (2009) INTERSNP: genomewide interaction analysis guided by a priori information. Bioinform Oxf Engl 25:3275– 3281.

Validate” rather than “replicate” was used here because linked and proximate SNPs were included in the validation in addition to the original SNPs, as follows.  Assume there are n1 and n2 SNPs, 40 Li Ma et al. respectively, surrounding SNPs A and B and the number of ­statistical tests to be conducted is 1, n1 + n2, and n2 in the three validation stages, respectively. To reduce the number of tests and the cost of multiple-testing correction on power, the validation process proceeds sequentially and stops at any stage where significant results were found after multiple-testing correction.

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A Practical Approach to Microarray Data Analysis by Daniel P. Berrar, Werner Dubitzky, Martin Granzow


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