Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset

Liu, X.; Sivaganesan, Siva; Yeung, Ka Yee; Guo, Jinpeng; Bumgarner, Roger E.; Medvedovic, Mario

doi:10.1093/bioinformatics/btl184

Cited by 28 publications

(42 citation statements)

References 18 publications

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“…However, these approaches do not explicitly model the correlations between subsequent time points which would be expected to occur in time series data, and the use of diagonal covariances may result in more clusters than necessary to model such correlations. Lui et al have recently extended their previous work to use full-covariance models for time series [14]. Since these authors are clustering short time series, inference in the space of the original data is feasible.…”

Section: Discussionmentioning

confidence: 99%

“…We first proposed and implemented the application of DPMs to clustering gene expression profiles in an extended conference abstract in 2002 [11]. Although this work is not widely known and cited, many groups have subsequently independently rediscovered the value of a fully Bayesian analysis based on DPMs to this problem [12], [13], [14], [15], [16]. We have also subsequently applied the approach to the clustering of protein sequences [17].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

Rasmussen

Cruz

Ghahramani

et al. 2009

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

Rasmussen

Cruz

Ghahramani

et al. 2009

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

“…The Multiple-Dataset Integration (Kirk et al, 2012) (MDI) algorithm permits the simultaneous clustering of an arbitrary number of datasets in a context dependent manner. This, and related, methods, show promise in recovering biologically meaningful clusters, as demonstrated in a number of recent studies (Barash and Friedman, 2002;Liu et al, 2006Liu et al, , 2007Savage et al, 2010;Rogers et al, 2009; Yuan et al, 2011). By performing simultaneous clustering over multiple of complementary datasets our method is able to exploit correlations in clustering structure between datasets.…”

Section: Introductionmentioning

confidence: 87%

MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing

Mason

Sayyid

Kirk

et al. 2016

Statistical Applications in Genetics and Molecular Biology

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Abstract:The integration of multi-dimensional datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct -but often complementary -information. However, the large amount of data adds burden to any inference task. Flexible Bayesian methods may reduce the necessity for strong modelling assumptions, but can also increase the computational burden. We present an improved implementation of a Bayesian correlated clustering algorithm, that permits integrated clustering to be routinely performed across multiple datasets, each with tens of thousands of items. By exploiting GPU based computation, we are able to improve runtime performance of the algorithm by almost four orders of magnitude. This permits analysis across genomic-scale data sets, greatly expanding the range of applications over those originally possible. MDI is available here:

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“…In contrast, probabilistic models provide a principled alternative to these heuristic methods. In particular, model-based clustering of gene expression has been intensively investigated as a means to explore gene function in recent years (Yeung et al, 2001, Medvedovic and Sivaganesan, 2002, Medvedovic et al, 2004, Lin et al, 2004, Ernst et al, 2005, Pan, 2006, Liu et al, 2006b, Ng et al, 2006, Joshi et al, 2008. These methods account for the experimental noise explicitly using a probabilistic model, which can be estimated from the data, and therefore show more robustness to noise.…”

Section: Introductionmentioning

confidence: 99%

“…This paper focuses mainly on model-based approaches which are used to deal with inherent variability in gene expression data. Previous work which involves methods specific to time-series gene expression data (Ernst et al, 2005), rely on prior biological knowledge (Pan, 2006), or model the context-specificity of gene expression patterns (Liu et al, 2006b), are beyond the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

Including Probe-Level Measurement Error in Robust Mixture Clustering of Replicated Microarray Gene Expression

Liu¹,

Rattray²

2010

Statistical Applications in Genetics and Molecular Biology

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Probabilistic mixture models provide a popular approach to cluster noisy gene expression data for exploring gene function. Since gene expression data obtained from microarray experiments are often associated with significant sources of technical and biological noise, replicated experiments are typically used to deal with data variability, and internal replication (e.g. from multiple probes per gene in an experiment) provides valuable information about technical sources of noise. However, current implementations of mixture models either do not consider the correlation between the replicated measurements for the same experimental condition, or ignore the probe-level measurement error, and thus overlook the rich information about technical noise. Moreover, most current methods use non-robust Gaussian components to describe the data, and these methods are therefore sensitive to non-Gaussian clusters and outliers. In many cases, this will lead to over-estimation of the number of model components as multiple Gaussian components are used to fit a non-Gaussian cluster. We propose a robust Student's t-mixture model, which explicitly handles replicated gene expression data, includes the consideration of probe-level measurement error when available and automatically selects the appropriate number of model components using a minimum message length criterion. We apply the model to gene expression data using probe-level measurements from an Affymetrix probe-level model, multi-mgMOS, which provides uncertainty estimates. The proposed Student's t-mixture model shows robust performance on synthetic data sets with realistic noise characteristics in comparison to a standard Gaussian mixture model and two other previously published methods. We also compare performance with these methods on two yeast time-course data sets and show that the new method obtains more biologically meaningful clusters in terms of enrichment statistics for GO categories and interactions between transcription factors and genes. Automatically selecting the number of components is more computationally efficient than using a model selection approach and allows the methods to be applied to larger data sets.

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Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset

Abstract: The open-source package gimm is available at http://eh3.uc.edu/gimm.

Cited by 28 publications

References 18 publications

Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing

Including Probe-Level Measurement Error in Robust Mixture Clustering of Replicated Microarray Gene Expression

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