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Comparison of Pattern Detection Methods in Microarray Time Series of the Segmentation Clock

Dequéant, Mary-Lee and Ahnert, Sebastian and Edelsbrunner, Herbert and Fink, Thomas M. A. and Glynn, Earl F. and Hattem, Gaye and Kudlicki, Andrzej and Mileyko, Yuriy and Morton, Jason and Mushegian, Arcady R. and Pachter, Lior and Rowicka, Maga and Shiu, Anne and Sturmfels, Bernd and Pourquié, Olivier (2008) Comparison of Pattern Detection Methods in Microarray Time Series of the Segmentation Clock. PLOS ONE, 3 (8). Art. No. e2856. ISSN 1932-6203. PMCID PMC2481401. https://resolver.caltech.edu/CaltechAUTHORS:20170307-104539290

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1371/journal.pone.0002856DOIArticle
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002856PublisherArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2481401/PubMed CentralArticle
Additional Information:© 2008 Dequéant et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: December 19, 2007; Accepted: April 26, 2008; Published: August 6, 2008. This research was partially supported by DARPA grant HR 0011-05-1-0057. HE and YM mathematical work was supported by DARPA grant HR0011-05-1-0007. AS research was supported by a Lucent Technologies Bell Labs Graduate Research. Fellowship; AK and MR research was supported by NIH grant GM U54 GM74942; and SA research was supported by Association pour la Recherche sur le Cancer (ARC), France. OP, AM, MLD, EG and GH research was supported by the Stowers Institute for Medical Research. OP is a Howard Hughes Medical Institute Investigator. The authors thank Z. Otwinowski for helpful discussions, J. Chatfield for editorial assistance and S. Esteban for artwork. Author Contributions: Performed the experiments: MLD. Analyzed the data: MLD. Wrote the paper: MLD HE TMAF GH AK AM MR OP. Prepared the microarray data: MD. Conceived, designed and implemented the algorithm of the Address reduction method: SA TMAF. Conceived and designed the algorithm of the Stable persistence method: HE YM. Implemented the algorithm of the Lomb Scargle method: EFG. Implemented the automated PubMed Search: GH. Conceived, designed and implemented the algorithm of the Phase consistency method: AK MR. Conceived, designed and implemented the algorithm of the Cyclohedron test method: JM AS. Conceived and designed the algorithm of the Cyclohedron test method: LP BS. Competing interests: Thomas M.A. Fink and Sebastian Ahnert have filed U.S. patent 20070086635, Method of identifying pattern in a series of data.
Funders:
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)HR0011-05-1-0057
Defense Advanced Research Projects Agency (DARPA)HR0011-05-1-0007
Lucent Technologies Bell LabsUNSPECIFIED
NIHU54 GM7494
Association pour la Recherche sur le CancerUNSPECIFIED
Stowers Institute for Medical ResearchUNSPECIFIED
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Issue or Number:8
PubMed Central ID:PMC2481401
Record Number:CaltechAUTHORS:20170307-104539290
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170307-104539290
Official Citation:Dequéant M-L, Ahnert S, Edelsbrunner H, Fink TMA, Glynn EF, Hattem G, et al. (2008) Comparison of Pattern Detection Methods in Microarray Time Series of the Segmentation Clock. PLoS ONE 3(8): e2856. doi:10.1371/journal.pone.0002856
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74845
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Mar 2017 19:09
Last Modified:03 Oct 2019 16:43

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