Search of reproducible independent components in transcriptomes of patients with breast cancer
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Keywords:
breast cancer, independent component analysis, correlation graphs of gene expression, gene interactions, signaling pathways,Abstract
The technique with using of Independent component analysis for searching of reproducible independent components from Affymetrix HG-U133A array transcriptomes of patients with breast cancer has been developed. This technique can be used to construct correlation graphs of gene expression, to reveal gene interactions and new signaling pathways specifi c to cancer.References
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2 DeRisi J., Penland L., Brown P.O. et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer // Nature Genetics. – 1996. – V.14. – P.457-460.
3 Rolph M.S., Sisavanh M., Liu S.M., Mackay C.R. Clues to asthma pathogenesis from microarray expression studies // Pharmacol.Ther. – 2006. - V.109. - P.284-294.
4 Evans S.J., Choudary P.V., Vawter M.P., et al. DNA microarray analysis of functionally discrete human brain regions reveals divergent transcriptional profiles // Neurobiol. Dis. – 2003. – V14. – P. 240-250.
5 Golub T.R., Slonim D.K., Tamayo P., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring // Science.– 1999. – V.286. – P. 531-537.
6 Van Veer L.J., Dai H., Van de Vijver M.J., et al. Gene expression profiling predicts clinical outcome of breast cancer // Nature. – 2002. – V. 415. – P. 530-536.
7 Sorlie T., Tibshirani R., Parker J., et al. Repeated observation of breast tumor subtypes in independent gene expression data sets // Proc. Natl. Acad. Sci. USA.– 2003.-V.100 (14). – P. 8418-8423.
8 http://www.affymetrix.com
9 Каиров У.Е., Зиновьев А.Ю., Карпенюк Т.А., Раманкулов Е.М. ДНК-микрочипы: от основ технологии к анализу данных // Вестник КазНУ.
Серия биологическая. -2012. - №4 (56). – С. 270-274.
10 Comon P., Independent Component Analysis: a new concept // Signal Processing. – 1994. – 36 (3). – P.287–314.
11 Liebermeister W. Linear modes of gene expression determined by independent component analysis //Bioinformatics. – 2002. – V. 18 (1). – P. 51-60.
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13 Hori G., Inoue M., Nishimura S., and Nahakara H. Blind gene classifi cation an application of a signal separation method // Genome Informatics. – 2001. – V.12. – P. 255-256.
14 http://www.ncbi.nlm.nih.gov/geo/
15 http://www.bioconductor.org/
16 Wu Z., Irizarry R.A., Gentleman R. et al. A model based background adjustment for oligonucleotide expression arrays // Journal of the American Statistical Association. - 2004. – V. 99. – P. 909-917.
17 http://www.mathworks.com/
18 Himberg J., Hyvarinen A. and Esposito F.. Validating the independent components of neuroimaging time series via clustering and visualization // Neuroimage. – 2004. – V.22 (3). – P. 1214-1222.
19 Jackson D. Stopping rules in principal component analysis: a comparison of heuristical and statistical approaches // Ecology. – 1993. – V.74 (8). – P. 2204-2214.
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Kairov, U. Y., Zinovyev, A. Y., Karpenyuk, T. A., & Ramanculov, Y. M. (2015). Search of reproducible independent components in transcriptomes of patients with breast cancer. Experimental Biology, 54(2), 29–32. Retrieved from https://bb.kaznu.kz/index.php/biology/article/view/256
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BIOTECHNOLOGY, BIOCHEMISTRY AND PLANTS PHYSIOLOGY
