title: Comparison of pattern detection methods in microarray time series of the segmentation clock.
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.
authors.affiliation: Stowers Institute for Medical Research, Kansas City, Missouri, United States of America.
authors.forename: Mary-Lee
authors.initials: ML
authors.lastname: Dequéant
authors.forename: Sebastian
authors.initials: S
authors.lastname: Ahnert
authors.forename: Herbert
authors.initials: H
authors.lastname: Edelsbrunner
authors.forename: Thomas M A
authors.initials: TM
authors.lastname: Fink
authors.forename: Earl F
authors.initials: EF
authors.lastname: Glynn
authors.forename: Gaye
authors.initials: G
authors.lastname: Hattem
authors.forename: Andrzej
authors.initials: A
authors.lastname: Kudlicki
authors.forename: Yuriy
authors.initials: Y
authors.lastname: Mileyko
authors.forename: Jason
authors.initials: J
authors.lastname: Morton
authors.forename: Arcady R
authors.initials: AR
authors.lastname: Mushegian
authors.forename: Lior
authors.initials: L
authors.lastname: Pachter
authors.forename: Maga
authors.initials: M
authors.lastname: Rowicka
authors.forename: Anne
authors.initials: A
authors.lastname: Shiu
authors.forename: Bernd
authors.initials: B
authors.lastname: Sturmfels
authors.forename: Olivier
authors.initials: O
authors.lastname: Pourquié
mesh_terms: D000818:Animals; D002453:Cell Cycle; D055515:Cysteine-Rich Protein 61 / Q000235:genetics; D015342:DNA Probes; D004622:Embryo, Mammalian / Q000502:physiology; D047108:Embryonic Development; D018507:Gene Expression Regulation, Developmental; D016678:Genome; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D046709:Pattern Recognition, Physiological / Q000502:physiology*; D051880:Receptors, Notch / Q000235:genetics; D051153:Wnt Proteins / Q000235:genetics
chemical_list: C491850:CCN1 protein, mouse; D055515:Cysteine-Rich Protein 61; D015342:DNA Probes; D051880:Receptors, Notch; D051153:Wnt Proteins