Modeling dependent gene expression

Telesca, Donatello; Müller, Peter; Parmigiani, Giovanni; Freedman, Ralph S.

doi:10.1214/11-aoas525

Cited by 19 publications

(25 citation statements)

References 48 publications

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“…Additionally, Carvalho & Scott [29] show that when the inclusion probability comes from the Beta hyperprior, the model has a strong control over the number of 'false' edges included in the graph. Telesca et al [30] model dependent gene expression through a graph structure similar to the one in our model, and adopt the binomial-beta model for total number of edges; the authors show through simulations that their model returns good control over false discovery rates. One advantage of the Bayesian phylogenetic framework we exploit in formulating our graph hierarchical model is that the framework easily lends itself to combination of different models.…”

Section: Discussionmentioning

confidence: 99%

Graph hierarchies for phylogeography

Cybis

Sinsheimer

Lemey

et al. 2013

Phil. Trans. R. Soc. B

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One contribution of 18 to a Discussion Meeting Issue 'Next-generation molecular and evolutionary epidemiology of infectious disease'. Bayesian phylogeographic methods simultaneously integrate geographical and evolutionary modelling, and have demonstrated value in assessing spatial spread patterns of measurably evolving organisms. We improve on existing phylogeographic methods by combining information from multiple phylogeographic datasets in a hierarchical setting. Consider N exchangeable datasets or strata consisting of viral sequences and locations, each evolving along its own phylogenetic tree and according to a conditionally independent geographical process. At the hierarchical level, a random graph summarizes the overall dispersion process by informing which migration rates between sampling locations are likely to be relevant in the strata. This approach provides an efficient and improved framework for analysing inherently hierarchical datasets. We first examine the evolutionary history of multiple serotypes of dengue virus in the Americas to showcase our method. Additionally, we explore an application to intrahost HIV evolution across multiple patients.

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Section: Discussionmentioning

confidence: 99%

Graph hierarchies for phylogeography

Cybis

Sinsheimer

Lemey

et al. 2013

Phil. Trans. R. Soc. B

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“…Incorporating biological knowledge into our prior models is one of the most innovative features of our work. First attempts at incorporating biological network information into a probabilistic model, such as in Wei and Li (2007), have adopted the strategy of translating the "functional" network of KEGG into an undirected graphical model, see also Telesca et al (2008) for a more sophisticated approach. However, different approaches are possible relatively to the way that a network is translated into a graphical model (directed, undirected, chain graph, etc...) and also to the way the marginalization with respect to the non observed genes included in the network is performed.…”

Section: Reply To the Discussionmentioning

confidence: 99%

“…Other contributions to the use of MRF priors for genomic data include Telesca et al (2008), who have proposed a model for the identification of differentially expressed genes that takes into account the dependence structure among genes from available pathways while allowing for correction in the gene network topology. Also, Li and Zhang (2010) incorporate the dependence structure of transcription factors in a regression model with gene expression outcomes; in their approach a network is defined based on the Hamming distance between candidate motifs and used to specify a Markov random field prior for the motif selection indicator.…”

Section: Priors That Incorporate Biological Informationmentioning

confidence: 99%

Bayesian Models for Variable Selection that Incorporate Biological Information*

Vannucci

Stingo

Berzuini

2011

Bayesian Statistics 9

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SummaryVariable selection has been the focus of much research in recent years. Bayesian methods have found many successful applications, particularly in situations where the amount of measured variables can be much greater than the number of observations. One such example is the analysis of genomics data. In this paper we first review Bayesian variable selection methods for linear settings, including regression and classification models. We focus in particular on recent prior constructions that have been used for the analysis of genomic data and briefly describe two novel applications that integrate different sources of biological information into the analysis of experimental data. Next, we address variable selection for a different modeling context, i.e. mixture models. We address both clustering and discriminant analysis settings and conclude with an application to gene expression data for patients affected by leukemia.

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“…Thus [12], acknowledging the consequent computational and methodological limitations of their techniques, used dynamic BNs to model subsets of series like those we model here. Albeit in a non-exploratory context [19] analysed variations of a baseline model representing known dependence structures. Other graphical studies of regulatory models include [4].…”

Section: Introductionmentioning

confidence: 99%

Bayesian selection of graphical regulatory models

Liverani

Smith

2016

International Journal of Approximate Reasoning

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We define a new class of coloured graphical models, called regulatory graphs. These graphs have their own distinctive formal semantics and can directly represent typical qualitative hypotheses about regulatory processes like those described by various biological mechanisms. They admit an embellishment into classes of probabilistic statistical models and so standard Bayesian methods of model selection can be used to choose promising candidate explanations of regulation. Regulation is modelled by the existence of a deterministic relationship between the longitudinal series of observations labelled by the receiving vertex and the donating one. This class contains longitudinal cluster models as a degenerate graph. Edge colours directly distinguish important features of the mechanism like inhibition and excitation and graphs are often cyclic. With appropriate distributional assumptions, because the regulatory relationships map onto each other through a group structure, it is possible to define a conditional conjugate analysis. This means that even when the model space is huge it is nevertheless feasible, using a Bayesian MAP search, to a discover regulatory network with a high Bayes Factor score. We also show that, like the class of Bayesian Networks, regulatory graphs also admit a formal but distinctive causal algebra. The topology of the graph then represents collections of hypotheses about the predicted effect of controlling the process by tearing out message passers or forcing them to transmit certain signals. We illustrate our methods on a microarray experiment measuring the expression of thousands of genes as a longitudinal series where the scientific interest lies in the circadian regulation of these plants.

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Modeling dependent gene expression

Cited by 19 publications

References 48 publications

Graph hierarchies for phylogeography

Graph hierarchies for phylogeography

Bayesian Models for Variable Selection that Incorporate Biological Information*

Bayesian selection of graphical regulatory models

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