Shotgun Stochastic Search for “Large<i>p</i>” Regression

Hans, Chris; Dobra, Adrian; West, Mike

doi:10.1198/016214507000000121

Cited by 203 publications

(192 citation statements)

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“…Here, we extend iBMA to linear regression by rank ordering putative regulators using the coefficient of determination (R 2 ) from single-variable models and then iteratively applying BMA to the top ranked genes, removing variables with low posterior probabilities (see Materials and Methods). Other methods for implementing BMA for linear regression with highdimensional data have also been proposed more recently (25)(26)(27)(28)(29).…”

Section: Resultsmentioning

confidence: 99%

Construction of regulatory networks using expression time-series data of a genotyped population

Yeung

Dombek²,

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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The inference of regulatory and biochemical networks from largescale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale geneexpression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.L arge-scale sequencing has provided a wealth of data on the presence, absence, and variation of genes within and between species. However, functional annotation is unavailable for many genes and the majority of genes within most species are not placed within regulatory or biochemical pathways. Classic biochemical methods for placing genes in pathways cannot keep pace with the rapidly increasing amount of genomic information. To address this problem, we and others have been developing methods to infer networks from large-scale functional genomics data (1-5). The overall goals of such methods are to generate predictions of systems behavior and testable hypotheses of gene-to-gene influences. Predictions of systems behavior can be useful even in the absence of detailed mechanistic understanding. For example, the predicted response to the inhibition of a given gene can guide the selection of drug targets (6). The generation of testable hypotheses provides a path to more rapidly gain mechanistic understanding as it focuses bench experiments on subsets of potential gene-to-gene influences. Moreover, network construction and experimental work can be used in an iterative process to converge on underlying mechanisms (7,8).At present, the data most used in network construction methods are from large-scale gene-expression studies. Coexpression of genes across a wide variety of experimental conditions implies coregulation (9, ...

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Section: Resultsmentioning

confidence: 99%

Construction of regulatory networks using expression time-series data of a genotyped population

Yeung

Dombek²,

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…Thus, they are more flexible in avoiding 'sticky patches' in model space; see the discussion in, for example, Dellaportas and Roberts (2003). Indeed, Hans et al (2007) point out that the chain may fail to move if it starts at a region of zero posterior probability mass. In more simulation studies this has been also noted by Petralias (2010).…”

Section: ′ ′mentioning

confidence: 99%

“…Algorithms 1 and 2 have a similar philosophy with the MCMC version of the Shotgun stochastic search Algorithm of Hans et al (2007) but instead of attempting to move directly to all models in , they propose more local moves. Thus, they are more flexible in avoiding 'sticky patches' in model space; see the discussion in, for example, Dellaportas and Roberts (2003).…”

Section: ′ ′mentioning

confidence: 99%

“…Our suggested algorithm can be viewed as a combination of the Carlin and Chib algorithm (Carlin and Chib, 1995), the 'Metropolised' Carlin and Chib algorithm (Godsill, 2001;Dellaportas et al, 2002) and the Shotgun stochastic search (Hans et al, 2007) algorithm. We call it a 'Subspace Carlin and Chib' (SCC) algorithm and its main idea is based on the following arguments.…”

Section: Introductionmentioning

confidence: 99%

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An MCMC model search algorithm for regression problems

Petralias

Δελλαπόρτας

2013

Journal of Statistical Computation and Simulation

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“…Jones et al (2005) and Hans et al (2007) highlight this question in the context of Gaussian graphical models and regression variable selection. They introduce the shotgun stochastic search (SSS) method that is similar to MCMC but it focuses on aggresively moving towards regions of high posterior probability in the models space instead of attempting to sample from the posterior distribution over the models space.…”

Section: Introductionmentioning

confidence: 99%

The mode oriented stochastic search (MOSS) algorithm for log-linear models with conjugate priors

Dobra

Massam

2010

Statistical Methodology

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Summary. We describe a novel stochastic search algorithm for rapidly identifying regions of high posterior probability in the space of decomposable, graphical and hierarchical log-linear models. Our approach is based on the conjugate priors for log-linear parameters introduced in Massam et al. (2008). We discuss the computation of Bayes factors through Laplace approximations and the Bayesian iterate proportional fitting algorithm for sampling model parameters. We also present a clustering algorithm for discrete data and develop regressions derived from log-linear models. We compare our model determination approach with similar results based on multivariate normal priors for log-linear parameters. The examples concern six-way, eightway and sixteen-way contingency tables.

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Shotgun Stochastic Search for “Largep” Regression

Cited by 203 publications

References 1 publication

Construction of regulatory networks using expression time-series data of a genotyped population

Construction of regulatory networks using expression time-series data of a genotyped population

An MCMC model search algorithm for regression problems

The mode oriented stochastic search (MOSS) algorithm for log-linear models with conjugate priors

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