Comparison of pattern detection methods in microarray time series of the segmentation clock.
Comparison of pattern detection methods in microarray time series of the segmentation clock.
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DOI：
10.1371/journal.pone.0002856
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发表时间：
2008-08-06
期刊：
影响因子：
3.7
通讯作者：
Pourquié O
中科院分区：
文献类型：
--
作者：
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
虽然全基因组的基因表达数据正在以越来越快的速度产生，但在这些数据中发现模式的方法仍然有限。在与一定水平的噪声相关的大量数据(数以万计的档案)中识别感兴趣的细微模式仍然是一项挑战。最近产生了一个微阵列时间序列来研究小鼠分段时钟的转录程序，这是一种与体轴分段的周期性形成相关的生物振荡器。一种与傅立叶分析相关的方法，Lomb-Scarger周期图，被用来检测数据集中的周期轮廓，导致识别出与分段时钟相关的一组新的周期基因。在这里，我们对同一微阵列时间序列数据集应用了四种不同的数学方法来识别基因表达谱中的显著模式。这些方法被称为：阶段一致性、地址约简、环面体测试和稳定持久性，它们基于不同的概念框架，要么是假设驱动的，要么是数据驱动的。与傅里叶变换不同的是，一些方法不依赖于感兴趣图案的周期性假设。值得注意的是，这些方法盲目地将已知周期基因的表达谱识别为数据集中最重要的模式。通过不止一种方法预测的许多候选基因似乎是真正的正循环基因，将对未来的研究特别感兴趣。此外，这些方法预测了新的候选循环基因，这些基因与先前的生物学知识和小鼠胚胎的实验验证一致。我们的结果证明了这些新的模式检测策略的实用性，特别是对于周期性图谱的检测，并表明结合几种不同的数学方法来分析微阵列数据集是识别显示新的、有趣的转录模式的基因的一种有价值的策略。
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.
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影响因子：
9.8
作者：
Bozdech, Zbynek;Llinas, Manuel;Pulliam, Brian Lee;Wong, Edith D;Zhu, Jingchun;DeRisi, Joseph L
通讯作者：
DeRisi, Joseph L
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通讯作者：
Pourquie, O
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Rossant, J
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作者：
HENRIQUE, D;ADAM, J;ISHHOROWICZ, D
通讯作者：
ISHHOROWICZ, D