Abstract
BackgroundClustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the exploration of unknown gene functions. Microarray experiments are associated with many sources of experimental and biological variation and the resulting gene expression data are therefore very noisy. Many heuristic and model-based clustering approaches have been developed to cluster this noisy data. However, few of them include consideration of probe-level measurement error which provides rich information about technical variability.ResultsWe augment a standard model-based clustering method to incorporate probe-level measurement error. Using probe-level measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we include the probe-level measurement error directly into the standard Gaussian mixture model. Our augmented model is shown to provide improved clustering performance on simulated datasets and a real mouse time-course dataset.ConclusionThe performance of model-based clustering of gene expression data is improved by including probe-level measurement error and more biologically meaningful clustering results are obtained.
Highlights
Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the exploration of unknown gene functions
We examine the performance of the extended Gaussian mixture model on two simulated datasets and a realworld mouse time-course dataset [12]
The simulated datasets are generated to reflect the noise commonly seen in real microarray experiments
Summary
Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the exploration of unknown gene functions. Few of them include consideration of probe-level measurement error which provides rich information about technical variability. Microarray experiments are complicated multiple step procedures and variability can be introduced in every step, so that the resulting data are often very noisy, especially for weakly expressed genes. Appropriate statistical analysis of this noisy data is very important in order to obtain meaningful biological information [3,4]. The analysis of microarray data is usually performed in multiple stages, including probe-level analysis, normalisation and higher level analyses. The aim of the probe-level analysis is to obtain reliable gene expression measurements from the image data. Various higher level analyses, such as detecting differential gene expression or clustering, can be carried out depending on the biological aims of the experiment
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