Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection

Ishwaran, Hemant; Rao, J. Sunil

doi:10.1198/016214503000224

Cited by 142 publications

(130 citation statements)

References 21 publications

(24 reference statements)

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“…According to Ishwaran and Rao (2003), these values do not need to be tuned for each data set and can be fixed. No substantial differences have been observed when several different sets of priors were tried in a simulation study (data not shown).…”

Section: Applicationsmentioning

confidence: 99%

Mapping Quantitative Trait Loci for Expression Abundance

Jia

2007

Genetics

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Mendelian loci that control the expression levels of transcripts are called expression quantitative trait loci (eQTL). When mapping eQTL, we often deal with thousands of expression traits simultaneously, which complicates the statistical model and data analysis. Two simple approaches may be taken in eQTL analysis: (1) individual transcript analysis in which a single expression trait is mapped at a time and the entire eQTL mapping involves separate analysis of thousands of traits and (2) individual marker analysis where differentially expressed transcripts are detected on the basis of their association with the segregation pattern of an individual marker and the entire analysis requires scanning markers of the entire genome. Neither approach is optimal because data are not analyzed jointly. We develop a Bayesian clustering method that analyzes all expressed transcripts and markers jointly in a single model. A transcript may be simultaneously associated with multiple markers. Additionally, a marker may simultaneously alter the expression of multiple transcripts. This is a model-based method that combines a Gaussian mixture of expression data with segregation of multiple linked marker loci. Parameter estimation for each variable is obtained via the posterior mean drawn from a Markov chain Monte Carlo sample. The method allows a regular quantitative trait to be included as an expression trait and subject to the same clustering assignment. If an expression trait links to a locus where a quantitative trait also links, the expressed transcript is considered to be associated with the quantitative trait. The method is applied to a microarray experiment with 60 F 2 mice measured for 25 different obesity-related quantitative traits. In the experiment, $40,000 transcripts and 145 codominant markers are investigated for their associations. A program written in SAS/IML is available from the authors on request.

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Section: Applicationsmentioning

confidence: 99%

Mapping Quantitative Trait Loci for Expression Abundance

Jia

2007

Genetics

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“…For example, Bayesian multivariate sparse latent factor model (West 2003) provides a flexible platform for introducing prior design-dependent covariate structure in feature selection in high-dimensional settings. Our focus is to identify important covariates instead of latent factors in this paper, and thus we adopt the Bayesian "spike and slab" approaches to variable selection (e.g., McCulloch 1993, 1997;Brown, Vannucci, and Fearn 1998;Rao 2003, 2005a;Clyde and George 2004;and reference therein). The basic idea behind this framework is to define latent variables γ = (γ i : 1 ≤ i ≤ p), where γ i is the indicator of whether covariate i is included in the model.…”

Section: Introductionmentioning

confidence: 99%

“…Then, Markov chain Monte Carlo (MCMC) methods are used to stochastically approximate the posterior distribution of γ given the data. For a detailed comparison of Bayesian and frequentist penalized regression approaches, see Ishwaran and Rao (2005a). These MCMC based procedures involve extensive computing and have been traditionally applied to regression problems where p is not too large, although recently they have been applied with some success to high-dimensional problems (Ibrahim, Chen, and Gray 2002;Rao 2003, 2005b;Tadesse, Sha, and Vannucci 2005).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics

Zhang

2010

Journal of the American Statistical Association

169

182

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We consider the problem of variable selection in regression modeling in high-dimensional spaces where there is known structure among the covariates. This is an unconventional variable selection problem for two reasons: (1) The dimension of the covariate space is comparable, and often much larger, than the number of subjects in the study, and (2) the covariate space is highly structured, and in some cases it is desirable to incorporate this structural information in to the model building process. We approach this problem through the Bayesian variable selection framework, where we assume that the covariates lie on an undirected graph and formulate an Ising prior on the model space for incorporating structural information. Certain computational and statistical problems arise that are unique to such high-dimensional, structured settings, the most interesting being the phenomenon of phase transitions. We propose theoretical and computational schemes to mitigate these problems. We illustrate our methods on two different graph structures: the linear chain and the regular graph of degree k. Finally, we use our methods to study a specific application in genomics: the modeling of transcription factor binding sites in DNA sequences.

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“…For example, we have said nothing about gene expression analysis (see e.g. References [228,229]), which can provide phenotypes to be investigated by some of the methods we have described [230], nor anything about new methods for mapping using admixed populations [231], an idea first proposed over 50 years ago [232]. We have given more attention to the methods that we believe will continue to be important in the future, in the areas of linkage analysis and association analysis, for finding genes important to human health.…”

Section: Discussionmentioning

confidence: 99%

Advances in statistical human genetics over the last 25 years

Elston

Spence

2006

Statistics in Medicine

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SUMMARYThe past 25 years has seen an explosion in the number of genetic markers that can be measured on DNA samples at an ever decreasing cost. Although basic statistical methods for analysing such data gathered on samples of either independent individuals or family members, one or two markers at a time, were already well developed before this explosion occurred, there has been a corresponding burst in activity to develop multiple marker models to find disease-causing gene variants, capitalizing on the data that have become available, to increase the power of such methods. This has required the concomitant development of faster algorithms to speed up the computation of various likelihoods. For linkage analysis, to obtain the approximate locations for genes of interest, Mendelian segregation models have been extended to be more realistic and statistical models that do not assume specific modes of inheritance have been extended to allow for the analysis of larger pedigree structures. For association analysis, to obtain more precise locations for genes of interest, the recent completion of the first stage of the HapMap project has spurred the development, still underway, of novel experimental designs and analytical methods to combat the curse of dimensionality and the resulting multiple testing problem. Perhaps the greatest current challenge concerns how best to gather and synthesize the many lines of evidence possible in order to discover the genetic determinants underlying complex diseases.

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Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection

Cited by 142 publications

References 21 publications

Mapping Quantitative Trait Loci for Expression Abundance

Mapping Quantitative Trait Loci for Expression Abundance

Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics

Advances in statistical human genetics over the last 25 years

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