Abstract
BackgroundDetection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time.ResultsA learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented.ConclusionIn comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.
Highlights
Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms
In this work we demonstrate how Bayesian inference [13,14] can use diffuse prior knowledge about the cellcycle period time to achieve improved detection of periodically expressed genes with a period close to that of the cell-cycle, without help from any labelled training examples provided by a supervisor
Our simulations indicate that systematic incorporation of a priori knowledge about the cell-cycle period time using Bayesian inference may improve detection performance in comparison with two other recently proposed learningfree detectors
Summary
Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. One group of algorithms, including those in [1,3,5,6], use supervised learning methods [8] that exploit labelled expression profiles of genes known to be periodically expressed in the experiment to find other genes that are periodic This supervised learning approach precludes many potential applications where labelled training examples are not available e.g. for poorly characterised organisms. Since the period time usually is not exactly known, a novel method was proposed in [4] to resolve this problem by employing Fisher's g-test [8] for detection of periodic temporal profiles This approach seems attractive but is designed to detect any periodic temporal profile, even if its period differs significantly from the period of the cell-cycle. It does not use any prior knowledge about the period time
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