Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer

Background: Discovering patient subtypes and molecular drivers of a subtype are difficult and driving problems underlying most modern disease expression studies collected across patient populations. Expression patterns conserved across multiple expression datasets from independent disease studies are likely to represent important molecular events underlying the disease. Methods: We present the INSPIRE (INferring Shared modules from multiPle gene expREssion datasets) method to infer highly coherent and robust modules of co-expressed genes and the dependencies among the modules from multiple expression datasets. Focusing on inferring modules and their dependencies conserved across multiple expression datasets is important for several reasons. First, using multiple datasets will increase the power to detect robust and relevant patterns (modules and dependencies among modules). Second, INSPIRE enables the use of multiple datasets that contain different sets of genes due to, e.g., the difference in microarray platforms. Many methods designed for expression data analysis cannot integrate multiple datasets with variable discrepancy to infer a single combined model, whereas INSPIRE can naturally model the dependencies among the modules...Continue Reading