Abstract
Genomic instabilities, amplifications, deletions and translocations are often observed in tumor cells. In the process of cancer pathogenesis cells acquire multiple genomic alterations, some of which drive the process by triggering overexpression of oncogenes and by silencing tumor suppressors and DNA repair genes. We present data analysis methods designed to study the overall transcriptional effects of DNA copy number alterations. Alterations can be measured using several techniques including microarray based hybridization assays. The data have unique properties due to the strong dependence between measurement values in close genomic loci. To account for this dependence in studying the correlation of DNA copy number to expression levels we develop versions of standard correlation methods that apply to genomic regions and methods for assessing the statistical significance of the observed results. In joint DNA copy number and expression data we define significantly altered submatrices as submatrices where a statistically significant correlation of DNA copy number to expression is observed. We develop heuristic approaches to identify these structures in data matrices. We apply all methods to several datasets, highlighting results that can not be obtained by direct approaches or without using the regional view.
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© 2004 Springer-Verlag Berlin Heidelberg
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Lipson, D., Ben-Dor, A., Dehan, E., Yakhini, Z. (2004). Joint Analysis of DNA Copy Numbers and Gene Expression Levels. In: Jonassen, I., Kim, J. (eds) Algorithms in Bioinformatics. WABI 2004. Lecture Notes in Computer Science(), vol 3240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30219-3_12
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DOI: https://doi.org/10.1007/978-3-540-30219-3_12
Publisher Name: Springer, Berlin, Heidelberg
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