TY - GEN
T1 - Finding common aberrations in array CGH data
AU - Ylipää, Antti
AU - Nykter, Matti
AU - Kivinen, Virpi
AU - Hu, Limei
AU - Cogdell, David
AU - Hunt, Kelly
AU - Zhang, Wei
AU - Yli-Harja, Olli
PY - 2008
Y1 - 2008
N2 - Array comparative genomic hybridization (aCGH) technology provides high-resolution measurements of DNA aberrations at tens of thousands of locations distributed throughout the genome. These genomic aberrations cause alterations in gene expression patterns which, in turn, are a common cause for emergence of cancer. However, like all other microarray technologies, the obtained measurement data is noisy. In addition to the measurement noise, the heterogeneity of biological samples and cancer cells add biological noise and it also needs to be taken into account. To infer reliable results, analysis of aCGH data requires that different sources of uncertainty are carefully considered. We present an analysis framework that can be used to find reliable estimates of genomic aberrations that are frequent throughout a set of aCGH data. These commonly aberrant segments of DNA and the genes that reside in them, are key factors in understanding cancer. We demonstrate our framework by applying it to a set of aCGH data obtained from two different types of cancer. We also investigate what biological processes are affected by the mutations uncovered by our analysis of these cancer types using gene ontology enrichment. Based on this enrichment analysis, our framework reliably finds common aberrations in aCGH data.
AB - Array comparative genomic hybridization (aCGH) technology provides high-resolution measurements of DNA aberrations at tens of thousands of locations distributed throughout the genome. These genomic aberrations cause alterations in gene expression patterns which, in turn, are a common cause for emergence of cancer. However, like all other microarray technologies, the obtained measurement data is noisy. In addition to the measurement noise, the heterogeneity of biological samples and cancer cells add biological noise and it also needs to be taken into account. To infer reliable results, analysis of aCGH data requires that different sources of uncertainty are carefully considered. We present an analysis framework that can be used to find reliable estimates of genomic aberrations that are frequent throughout a set of aCGH data. These commonly aberrant segments of DNA and the genes that reside in them, are key factors in understanding cancer. We demonstrate our framework by applying it to a set of aCGH data obtained from two different types of cancer. We also investigate what biological processes are affected by the mutations uncovered by our analysis of these cancer types using gene ontology enrichment. Based on this enrichment analysis, our framework reliably finds common aberrations in aCGH data.
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U2 - 10.1109/ISCCSP.2008.4537408
DO - 10.1109/ISCCSP.2008.4537408
M3 - Conference contribution
AN - SCOPUS:50649086556
SN - 9781424416882
T3 - 2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP 2008
SP - 1199
EP - 1204
BT - 2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP2008
T2 - 2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP2008
Y2 - 12 March 2008 through 14 March 2008
ER -