Dataset Information

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects.

ABSTRACT: BACKGROUND: Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. RESULTS: We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that our model outperforms existing models to provide more reliable and robust clustering of time-course data. Our model provides superior results when genetic profiles are correlated. It also gives comparable results when the correlation between the gene profiles is weak. In the applications to real time-course data, relevant clusters of coregulated genes are obtained, which are supported by gene-function annotation databases. CONCLUSIONS: Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clustering time-course data because it adopts a random effects model that allows for the correlation among observations at different time points. It postulates gene-specific random effects with an autocorrelation variance structure that models coregulation within the clusters. The developed R package is flexible in its specification of the random effects through user-input parameters that enables improved modelling and consequent clustering of time-course data.

SUBMITTER: Wang K

PROVIDER: S-EPMC3574839 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Publications

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects.

Wang Kui K Ng Shu Kay SK McLachlan Geoffrey J GJ

BMC bioinformatics 20121114

<h4>Background</h4>Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitation ...[more]

PMID: 23151154

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Project description:BackgroundTight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.ResultsIn this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms.ConclusionFor the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the combination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion.

Project description:BACKGROUND:Longitudinally collected gene expression data provides an opportunity to investigate the dynamic behavior of gene expression and is crucial for establishing causal links between changes on a molecular level and disease development and progression. In terms of the analysis of such data, clustering of subjects based on time-course expression data may improve our understanding of temporal expression patterns that result in disease phenotypes. Although there are numerous existing methods for clustering subjects using gene expression data, most are not suitable when expression measurements are repeatedly collected over a time-course. METHODS:We present a modified version of the recursively partitioned mixture model (RPMM) for clustering subjects based on longitudinally collected gene expression data. In the proposed time-course RPMM (TC-RPMM), subjects are clustered on the basis of their temporal profiles of gene expression using a mixture of mixed effects models framework. This framework captures changes in gene expression over time and models the autocorrelation between repeated gene expression measurements for the same subject. We assessed the performance of TC-RPMM using extensive simulation studies and a dataset from a multi-center research study of inflammation and response to injury (www.gluegrant.org), which consisted of time-course gene expression data for 140 subjects. RESULTS:Our simulation studies encompassed several different scenarios and were aimed at assessing the ability of TC-RPMM to correctly recover true class memberships when the expression trajectories that characterized those classes differed. Overall, our simulation studies revealed favorable performance of TC-RPMM compared to competing approaches, however clustering performance was observed to be highly dependent on the proportion of class discriminating genes used in clustering analysis. When applied to real epidemiologic data with repeated-measures, longitudinal gene expression measurements, TC-RPMM identified clusters that had strong biological and clinical significance. CONCLUSIONS:Methods for clustering subjects based on temporal gene expression profiles is a high priority for molecular biology and bioinformatics research. Along these lines, the proposed TC-RPMM represents a promising new approach for analyzing time-course gene expression data.

Project description:BackgroundCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness.ResultsIn simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three 'omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis.ConclustionsOur approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.

Dataset Information

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects.

Publications

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects.

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