Comparison of pattern detection methods in microarray time series of the segmentation clock.

Dequéant, Mary-Lee

Ahnert, Sebastian

Edelsbrunner, Herbert

Fink, Thomas MA

Glynn, Earl F

Hattem, Gaye

Kudlicki, Andrzej

Mileyko, Yuriy

Morton, Jason

Mushegian, Arcady R

Pachter, Lior

Rowicka, Maga

Shiu, Anne

Sturmfels, Bernd

Pourquié, Olivier

United States

2011-06-21T17:31:24Z

2008-08-06

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.

Version of Record

http://www.ncbi.nlm.nih.gov/pubmed/18682743

1932-6203

https://hdl.handle.net/10161/4498

eng

en_US

Public Library of Science (PLoS)

PLoS One

10.1371/journal.pone.0002856

Plos One

Animals

Cell Cycle

Cysteine-Rich Protein 61

DNA Probes

Embryo, Mammalian

Embryonic Development

Gene Expression Regulation, Developmental

Genome

Mice

Oligonucleotide Array Sequence Analysis

Pattern Recognition, Physiological

Receptors, Notch

Wnt Proteins

Comparison of pattern detection methods in microarray time series of the segmentation clock.

Journal article

2008-8-6

8

3

http://www.ncbi.nlm.nih.gov/pubmed/18682743

e2856

8

Duke

Faculty

Published online

3