Abstract
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.
Highlights
Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies
We propose within-cluster compactness (WCC) to measure the functional closeness for genes within one cluster based on the corresponding GO relationship graph
Experiments on Yeast Galactose dataset Experiments are conducted on the Yeast Galactose dataset [42], which consists of gene expression measurements in galactose utilization in Saccharomyces cerevisiae
Summary
Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Based on the assumption that co-expression indicates coregulation, gene expression data clustering aims to reveal gene groups of similar functions in the biological pathways. This biological rationale is readily supported by both empirical observations and systematic analysis [1]. Various model-based methods have been proposed to accommodate the needs for data mining in such massive datasets. The basic approach of these model-based methods is to fit a finite mixture model to the observed data, assuming that there is an underlying true model/density, and systemically find the optimal parameters so that the fitted model/density is as close to the true model/density as possible. Current methods can be problematic, as they often fail to show how clustering can assist in mining gene expression data
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