Figure 1

Workflow for high-throughput data integration to help understand the molecular basis of cancer. An integrative -omics signaling network identification process workflow that begins with processing tissue specific data (instrument outputs). Microarray data is normalized to make comparisons of expression levels and transformed to select genes for further analysis. LOH/AI signals are analyzed to identify regions (and hence regional genes) for both tumor and normal tissue (or noncancerous cells). Next, genes observed within proximity of these markers are merged with their corresponding microarray probes to create expression profiles. In this analysis step, expression profiles are used to calculate Pearson's coexpression correlations among gene pairs. These results are fed into the Pathway Analysis Framework. Integrating gene-gene coexpression values, annotations from Gene Ontology, known signaling pathwas, protein sequence information, protein-protein interaction networks, and protein subcellular colocalization data, pathways are predicted and filtered. Significant pathway subnetworks are merged to form signaling networks connecting genes of interest. The networks and structural variations identified are put together to create a descriptive functional network, creating a molecular basis for the cancer studied. This type of workflow, which we utilized, can be applied to using integrative systems biology approaches to study cancer and other pathologies.