A framework for integrating wavelet based CNV inference and gene network analysis. The samples in a given gene expression study are first partitioned into two groups based on phenotypes such as poor versus good outcome, followed by differential expression analysis (t-test) to yields expression scores (ES t-statistics). Wavelet analysis is then performed on ES' ordered by gene chromosomal locations to detect significant consecutive regions (called inferred CNV regions). Using the same gene expression data, a gene regulatory network (Bayesian network) is constructed. Finally, the inferred CNV regions and the Bayesian network are input to the key driver analysis to identify potential cancer driver genes.
Tran et al. BMC Systems Biology 2011 5:121 doi:10.1186/1752-0509-5-121