Abstract
BackgroundMicroarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However, experimental data usually contain potential sources of uncertainty and thus many experiments are now designed with repeated measurements to better assess such inherent variability. Many computational methods have been proposed to account for the variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information so that a variety of computational models that take the expression profiles as the input data can explore this information without any modification.ResultsWe propose a methodology which integrates replicate variability into expression profiles, to generate so-called 'true' expression profiles. The study addresses two issues: (i) develop a statistical model that can estimate 'true' expression profiles which are more robust than the average profile, and (ii) extend our previous micro-clustering which was designed specifically for clustering time-series expression data. The model utilizes a previously proposed error model and the concept of 'relative difference'. The clustering effectiveness is demonstrated through synthetic data where several methods are compared. We subsequently analyze in vivo rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.ConclusionsWe have proposed a model which integrates the error information from repeated measurements into the expression profiles. Through numerous synthetic and real time-series data, we demonstrated the ability of the approach to improve the clustering performance and assist in the identification and selection of informative expression motifs.
Highlights
Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses
In addition to comparing the clustering performance using the ‘true’ profiles with the average profile, we compare with several other methods that take into account error information from replicated data
Since our model generates expression profiles which are applicable to any clustering method, we tested an alternative method which uses cubic splines to infer expression profiles which account for repeated measurements, so-called ‘smoothing’ profiles
Summary
Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. Experimental data usually contain potential sources of uncertainty and many experiments are designed with repeated measurements to better assess such inherent variability. Many computational methods have been proposed to account for the variability in replicates. Global gene expression analysis using microarrays has become an essential tool to study genome-wide transcriptional responses. This high-throughput technology produces a huge volume of useful data, enabling researchers to study the response of thousands of genes simultaneously, it faces many potential sources of uncertainties (e.g. technical noise, experimental treatments, biological sampling) [1,2].
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