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Deep Pathway Analysis V2.0: A Pathway Analysis Framework Incorporating Multi-dimensional Omics Data.

08:00 EDT 7th October 2019 | BioPortfolio

Summary of "Deep Pathway Analysis V2.0: A Pathway Analysis Framework Incorporating Multi-dimensional Omics Data."

Pathway analysis is essential in cancer research particularly when scientists attempt to derive interpretation from genome-wide high-throughput experimental data. When pathway information is organized into a network topology, its use in interpreting omics data becomes even more powerful. In this paper we propose a topology-based pathway analysis method, called DPA V2.0, which can combine multiple heterogeneous omics data types in its analysis. In this method, each pathway route is encoded as a Bayesian network which is initialized with a sequence of conditional probabilities specifically designed to encode directionality of regulatory relationships defined in the pathway. Unlike other topology-based pathway tools, DPA is capable of identifying pathway routes as representatives of perturbed regulatory signals. We demonstrate the effectiveness of our model by applying it to two well-established TCGA data sets, namely, breast cancer study (BRCA) and ovarian cancer study (OV). The analysis combines mRNA-seq, mutation, copy number variation and phosphorylation data publicly available for both TCGA data sets. We performed survival analysis and patient subtype analysis and the analysis outcomes revealed the anticipated strengths of our model.

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This article was published in the following journal.

Name: IEEE/ACM transactions on computational biology and bioinformatics
ISSN: 1557-9964
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