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We develop a model-based methodology for integrating gene-set information with an experimentally-derived gene list. The methodology uses a previously reported sampling model, but takes advantage of natural constraints in the high-dimensional discrete parameter space in order to work from a more structured prior distribution than is currently available. We show how the natural constraints are expressed in terms of linear inequality constraints within a set of binary latent variables. Further, the currently available prior gives low probability to these constraints in complex systems, such as Gene Ontology (GO), thus reducing the efficiency of statistical inference. We develop two computational advances to enable posterior inference within the constrained parameter space: one using integer linear programming for optimization and one using a penalized Markov chain sampler. Numerical experiments demonstrate the utility of the new methodology for a multivariate integration of genomic data with GO or related information systems. Compared to available methods, the proposed multi-functional analyzer covers more reported genes without mis-covering nonreported genes, as demonstrated on genome-wide data from association studies of type 2 diabetes and from RNA interference studies of influenza.
Motivated by gene set enrichment analysis, we investigate the problem of combined hypothesis testing on a graph. We introduce a general framework to effectively use the structural information of the underlying graph when testing multivariate means. A
When drawing causal inference from observational data, there is always concern about unmeasured confounding. One way to tackle this is to conduct a sensitivity analysis. One widely-used sensitivity analysis framework hypothesizes the existence of a s
Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation ra
Risk evaluation to identify individuals who are at greater risk of cancer as a result of heritable pathogenic variants is a valuable component of individualized clinical management. Using principles of Mendelian genetics, Bayesian probability theory,
Smart metering infrastructures collect data almost continuously in the form of fine-grained long time series. These massive time series often have common daily patterns that are repeated between similar days or seasons and shared between grouped mete