We introduced Pathifier – an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data [1]. This score is determined, in a context-specific manner, for every particular data set and type of cancer that is being investigated. The algorithm transforms gene level information into pathway level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm’s performance on three colorectal cancer datasets, two glioblastoma multiforme datasets, and an very extensive dataset on breast cancer. We show that our multi-pathway-based representation is robust, preserves much of the original information, and allows inference of complex biologically significant information, such as pathways that were significantly associated with survival. We also discover new cancer sub-classes, that were not seen in direct straightforward analysis of the corresponding expression data.
[1] Pathway-based personalized analysis of cancer. Yotam Drier, Michal Sheffer, and Eytan Domany, PNAS 110, 6388 (2013)
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