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. If pathway information is organized into a network topology, its use in interpreting omics data can become very 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. We hope that the availability of our model encourages wet lab scientists to generate extra data sets to reap the benefits of using multiple data types in pathway analysis. The majority of pathways distinguished can be confirmed by biological literature. Moreover, the proportion of correctly indentified pathways is 10 percent higher than previous work where only mRNA-seq and mutation data is incorporated for breast cancer patients. Consequently, such an in-depth pathway analysis incorporating more diverse data can give rise to the accuracy of perturbed pathway detection.