Collaborative Research: Statistical Methodology for Network based Integrative Analysis of Omics Data

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The overarching goal of this project is to delineate pathways-based on coordinated activity of genes, transcripts, proteins and metabolites, that could potentially serve as therapeutic targets, as well as create Omics based biomarker panels for early detection and prognosis of disease. Specifically, we address the problem of integration and analysis of multiple sources of high dimensional biological data with network structure. It is a well documented fact that correlation between different molecular compartments is relatively low, while the information derived from a single compartment is often highly noisy or even incomplete. Hence, there is a need to develop advanced models and techniques for integrating multiple data sets from diverse Omics platforms, while taking explicitly into consideration the available information of interactions within and between compartments. Particular emphasis is placed on pathway analysis and enrichment due to their role in complex diseases onset and progression. The following directions will be pursued: (1) Development of network based methods that integrate data from multiple Omics platforms for pathway analysis and enrichment. (2) Development of fast computational algorithms for estimating large scale network based integrative models. Investigation of associated inference problems and study of properties of proposed estimators together with their robustness to the noise levels of the network information employed. (3) Introduction of novel hypergraph models for assessing differential activity of pathways that utilize different degrees of information about the structure and accuracy of the underlying network. (4) Development of a novel scheme based on perturbed P-values for detecting active members of pathways that would aid in biomarker discovery. (5) Implementation of the propose methodology into an easy to use software tool.The proposed research program will have a three-pronged impact: methodological, scientific and educational. On the methodology front, the research based on this project will lead (a) to a developing a comprehensive framework for assessing differential activity of pathways based on different models that integrate data from multiple Omics platforms and utilize different degrees of information about the structure and accuracy of the underlying network, (b) a systematicunderstanding of the computational issues involved in large scale (generalized) mixed linear models;and (c) a novel scheme based on perturbed P-values for identifying active members of pathways that become potential targets for therapeutic drugs. The enhanced scientific understanding will provide tangible impact at the level of applications. A number of the proposed methods have already been used in the analysis of high dimensional genomic, proteomic and metabolomic data with emphasis on identifying active pathways (subnetworks) in different disease (primarily cancer) states. Further, a number of new experiments are in the design stage that would utilize some of the advanced models and techniques proposed in this project. Another key aspect of this proposal is the development of an easy to use by practitioners open source software, built within a domain independent workflow management system. This allows users to enhance the software by adding their own functionality and computational tools in an easy and transparent manner. The novel methodological procedures ensuing from this research agenda will be disseminated to the relevant scientific communities, both via inter-disciplinary interaction and collaboration and through presentations at conferences and specialized workshops. Finally, on the educational front, the material from the project will provide research topics for doctoral students working under the supervision of the PIs; it will therefore play an important role in the training of future quantitative scientists.