An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation).
An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation).
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DOI：
10.1186/1471-2105-10-409
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发表时间：
2009-12-10
影响因子：
3
通讯作者：
Quackenbush J
中科院分区：
文献类型：
--
作者：
Aryee MJ;Gutiérrez-Pabello JA;Kramnik I;Maiti T;Quackenbush J
微阵列基因表达时间过程实验提供了观察细胞用于响应内部和外部刺激的转录程序进化的机会。大多数常用的鉴定差异表达基因的方法将每个时间点视为独立的，忽略了重要的相关性，包括样本内和采样时间之间的相关性。因此，它们没有充分利用数据所固有的信息，从而导致功率的损失。我们提出了一个灵活的随机效应模型，将这种相关性考虑在内，提高了我们检测在多个时间点上持续差异表达的基因的能力。通过对所有时间点的样本的联合分布进行建模，与孤立地检查每个时间点的边际分析相比，我们获得了灵敏度。我们使用经验贝叶斯方法为每个基因分配一个差异表达的概率，该方法减少了待估计参数的有效数量。基于理论、模拟数据和对基因组数据的应用结果，我们发现BETR在时间序列数据中检测细微差异表达的能力有所提高。通过Bioconductor可以更好地获得开源R包。BETR也被纳入了免费提供的开源MeV软件工具，可从http://www.tm4.org/mev.html获得。
Microarray gene expression time-course experiments provide the opportunity to observe the evolution of transcriptional programs that cells use to respond to internal and external stimuli. Most commonly used methods for identifying differentially expressed genes treat each time point as independent and ignore important correlations, including those within samples and between sampling times. Therefore they do not make full use of the information intrinsic to the data, leading to a loss of power. We present a flexible random-effects model that takes such correlations into account, improving our ability to detect genes that have sustained differential expression over more than one time point. By modeling the joint distribution of the samples that have been profiled across all time points, we gain sensitivity compared to a marginal analysis that examines each time point in isolation. We assign each gene a probability of differential expression using an empirical Bayes approach that reduces the effective number of parameters to be estimated. Based on results from theory, simulated data, and application to the genomic data presented here, we show that BETR has increased power to detect subtle differential expression in time-series data. The open-source R package betr is available through Bioconductor. BETR has also been incorporated in the freely-available, open-source MeV software tool available from http://www.tm4.org/mev.html.
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DOI：
10.1073/pnas.011404098
发表时间：
2001-01-02
影响因子：
11.1
作者：
Li, C;Wong, WH
通讯作者：
Wong, WH
影响因子：
4.4
作者：
Feng, CG;Britton, WJ;Bean, AGD
通讯作者：
Bean, AGD
DOI：
10.1111/1467-9868.00346
发表时间：
2002-01-01
影响因子：
5.8
作者：
Storey, JD
通讯作者：
Storey, JD
影响因子：
1.1
作者：
Eckel, J E;Gennings, C;Zacharewski, T R
通讯作者：
Zacharewski, T R
影响因子：
4.5
作者：
Tai, Yu Chuan;Speed, Terence P.
通讯作者：
Speed, Terence P.