Show simple item record Dequéant, ML Ahnert, S Edelsbrunner, H Fink, TM Glynn, EF Hattem, G Kudlicki, A Mileyko, Y Morton, J Mushegian, AR Pachter, L Rowicka, M Shiu, A Sturmfels, B Pourquié, O
dc.coverage.spatial United States 2011-06-21T17:31:24Z 2008
dc.identifier.citation PLoS One, 2008, 3 (8), pp. e2856 - ?
dc.description.abstract While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.
dc.format.extent e2856 - ?
dc.language eng
dc.language.iso en_US en_US
dc.relation.ispartof PLoS One
dc.relation.isversionof 10.1371/journal.pone.0002856
dc.subject Animals
dc.subject Cell Cycle
dc.subject Cysteine-Rich Protein 61
dc.subject DNA Probes
dc.subject Embryo, Mammalian
dc.subject Embryonic Development
dc.subject Gene Expression Regulation, Developmental
dc.subject Genome
dc.subject Mice
dc.subject Oligonucleotide Array Sequence Analysis
dc.subject Pattern Recognition, Physiological
dc.subject Receptors, Notch
dc.subject Wnt Proteins
dc.title Comparison of pattern detection methods in microarray time series of the segmentation clock.
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US 2008-8-6 en_US
duke.description.endpage e2856 en_US
duke.description.issue 8 en_US
duke.description.startpage e2856 en_US
duke.description.volume 3 en_US
dc.relation.journal Plos One en_US
pubs.issue 8
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Faculty
pubs.publication-status Published online
pubs.volume 3
dc.identifier.eissn 1932-6203

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