---
_id: '3970'
abstract:
- lang: eng
text: '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.'
acknowledgement: 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.
author:
- first_name: Mary
full_name: Dequéant, Mary-Lee
last_name: Dequéant
- first_name: Sebastian
full_name: Ahnert, Sebastian
last_name: Ahnert
- first_name: Herbert
full_name: Herbert Edelsbrunner
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Thomas
full_name: Fink, Thomas M
last_name: Fink
- first_name: Earl
full_name: Glynn, Earl F
last_name: Glynn
- first_name: Gaye
full_name: Hattem, Gaye
last_name: Hattem
- first_name: Andrzej
full_name: Kudlicki, Andrzej
last_name: Kudlicki
- first_name: Yuriy
full_name: Mileyko, Yuriy
last_name: Mileyko
- first_name: Jason
full_name: Morton, Jason
last_name: Morton
- first_name: Arcady
full_name: Mushegian, Arcady R
last_name: Mushegian
- first_name: Lior
full_name: Pachter, Lior
last_name: Pachter
- first_name: Maga
full_name: Rowicka, Maga
last_name: Rowicka
- first_name: Anne
full_name: Shiu, Anne
last_name: Shiu
- first_name: Bernd
full_name: Sturmfels, Bernd
last_name: Sturmfels
- first_name: Olivier
full_name: Pourquie, Olivier
last_name: Pourquie
citation:
ama: Dequéant M, Ahnert S, Edelsbrunner H, et al. Comparison of pattern detection
methods in microarray time series of the segmentation clock. PLoS One.
2008;3(8). doi:10.1371/journal.pone.0002856
apa: Dequéant, M., Ahnert, S., Edelsbrunner, H., Fink, T., Glynn, E., Hattem, G.,
… Pourquie, O. (2008). Comparison of pattern detection methods in microarray time
series of the segmentation clock. PLoS One. Public Library of Science.
https://doi.org/10.1371/journal.pone.0002856
chicago: Dequéant, Mary, Sebastian Ahnert, Herbert Edelsbrunner, Thomas Fink, Earl
Glynn, Gaye Hattem, Andrzej Kudlicki, et al. “Comparison of Pattern Detection
Methods in Microarray Time Series of the Segmentation Clock.” PLoS One.
Public Library of Science, 2008. https://doi.org/10.1371/journal.pone.0002856.
ieee: M. Dequéant et al., “Comparison of pattern detection methods in microarray
time series of the segmentation clock,” PLoS One, vol. 3, no. 8. Public
Library of Science, 2008.
ista: Dequéant M, Ahnert S, Edelsbrunner H, Fink T, Glynn E, Hattem G, Kudlicki
A, Mileyko Y, Morton J, Mushegian A, Pachter L, Rowicka M, Shiu A, Sturmfels B,
Pourquie O. 2008. Comparison of pattern detection methods in microarray time series
of the segmentation clock. PLoS One. 3(8).
mla: Dequéant, Mary, et al. “Comparison of Pattern Detection Methods in Microarray
Time Series of the Segmentation Clock.” PLoS One, vol. 3, no. 8, Public
Library of Science, 2008, doi:10.1371/journal.pone.0002856.
short: M. Dequéant, S. Ahnert, H. Edelsbrunner, T. Fink, E. Glynn, G. Hattem, A.
Kudlicki, Y. Mileyko, J. Morton, A. Mushegian, L. Pachter, M. Rowicka, A. Shiu,
B. Sturmfels, O. Pourquie, PLoS One 3 (2008).
date_created: 2018-12-11T12:06:11Z
date_published: 2008-08-06T00:00:00Z
date_updated: 2021-01-12T07:53:33Z
day: '06'
doi: 10.1371/journal.pone.0002856
extern: 1
intvolume: ' 3'
issue: '8'
license: https://creativecommons.org/licenses/by/4.0/
month: '08'
publication: PLoS One
publication_status: published
publisher: Public Library of Science
publist_id: '2157'
quality_controlled: 0
status: public
title: Comparison of pattern detection methods in microarray time series of the segmentation
clock
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
volume: 3
year: '2008'
...