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We present the extention and application of a new unsupervised statisticallearning technique--the Partition Decoupling Method--to gene expression data.Because it has the ability to reveal non-linear and non-convex geometriespresent in the data, the PDM is an improvement over typical gene expressionanalysis algorithms, permitting a multi-gene analysis that can revealphenotypic differences even when the individual genes do not exhibitdifferential expression. Here, we apply the PDM to publicly-available geneexpression data sets, and demonstrate that we are able to identify cell typesand treatments with higher accuracy than is obtained through other approaches.By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM maybe used to find sets of mechanistically-related genes that discriminatephenotypes.

Partition Decoupling for Multi-gene Analysis of Gene Expression Profiling Data