Bayesian models for flexible integrative analysis of multi-platform genomics data

Publisher:
Cambridge University Press
Publication Type:
Chapter
Citation:
Integrating Omics Data, 2015, pp. 221 - 241
Issue Date:
2015-01-01
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© Cambridge University Press 2015. We present hierarchical Bayesian models to integrate an arbitrary number of genomic data platforms incorporating known biological relationships between platforms, with the goal of identifying biomarkers significantly related to a clinical phenotype. Our integrative approach offers increased power and lower false discovery rates, and our model structure allows us to not only identify which gene(s) is (are) significantly related to the outcome, but also to understand which upstream platform(s) is (are) modulating the effect(s). We present both a linear and a more flexible non-linear formulation of our model, with the latter allowing for detection of non-linear dependencies between the platform-specific features. We illustrate our method using both formulations on a multi-platform brain tumor dataset. We identify several important genes related to cancer progression, along with the corresponding mechanistic information, and discuss and compare the results obtained from the two formulations.
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