Statistical modeling of large microarray data sets to identify stimulus-response profiles
Statistical modeling of large microarray data sets to identify stimulus-response profiles
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DOI：
10.1073/pnas.101013198
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发表时间：
2001-05-08
影响因子：
11.1
通讯作者：
Breeden, L
中科院分区：
文献类型：
--
作者：
Zhao, LP;Prentice, R;Breeden, L
提出了一种统计建模方法，用于搜索大的微阵列数据集的基因，有一个转录反应的刺激。该方法在反应的时间、幅度或持续时间或转录本的总体丰度方面不受限制。该统计模型适应了表达水平的系统异质性。相应的数据分析提供了基因特异性信息。并且该方法提供了用于评估这种信息的统计显著性的手段。为了说明这一策略，我们已经推导出一个模型来描述预期的周期性转录的基因，并用它来寻找芽殖酵母转录本，坚持这一配置文件。使用客观标准，该方法确定了81%的已知周期性转录本和1，088个基因，这些基因在分析的三个数据集中至少有一个显示出显著的周期性。然而，这些基因中只有四分之一在至少两个数据集中显示出显著的振荡，并且可以被归类为具有高置信度的周期性。该方法提供了平均激活和失活时间、诱导和基础表达水平的估计值，以及每个周期性转录本的这些估计值的精确度的统计测量。
A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information. and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.