CDCOCA: a statistical method to define complexity dependence of co-occuring XXXX .

@entity5 @entity2008
BACKGROUND: Copy number alterations (CNA) play a key role in @entity5 development and progression. Since more than one CNA can be detected in most @entity5 , frequently co-occurring genetic CNA may point to cooperating @entity5 related genes. Existing methods for co-occurrence evaluation so far have not considered the overall heterogeneity of CNA per @entity5 , resulting in a preferential detection of frequent changes with limited specificity for each association due to the high genetic instability of many samples. METHOD: We hypothesize that in @entity5 some linkage-independent CNA may display a non-random co-occurrence, and that these CNA could be of pathogenetic relevance for the respective @entity5 . We also hypothesize that the statistical relevance of co-occurring CNA may depend on the sample specific CNA complexity. We verify our hypotheses with a simulation based algorithm CDCOCA (complexity dependence of co-occurring @entity2008 ). RESULTS: Application of CDCOCA to example data sets identified co-occurring CNA from low complex background which otherwise went unnoticed. Identification of @entity5 associated genes in these co-occurring changes can provide insights of cooperative genes involved in oncogenesis. CONCLUSIONS: We have developed a method to detect associations of regional copy number abnormalities in @entity5 data. Along with finding statistically relevant CNA co-occurrences, our algorithm points towards a generally low specificity for co-occurrence of regional imbalances in CNA rich samples, which may have negative impact on pathway modeling approaches relying on frequent CNA events.
