On the choice and number of microarrays for transcriptional regulatory network inference.
On the choice and number of microarrays for transcriptional regulatory network inference.
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DOI：
10.1186/1471-2105-11-454
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发表时间：
2010-09-09
影响因子：
3
通讯作者：
Kolaczyk ED
中科院分区：
文献类型：
--
作者：
Cosgrove EJ;Gardner TS;Kolaczyk ED
转录调控网络推理（TRNI）已成为一种基本的方法，发现转录因子（TF）-基因的相互作用在基因组水平上的大型纲要的DNA微阵列。在基于相关性的TRNI中，原则上可以使用标准统计测试来评估网络边缘。然而，虽然这些测试名义上假设独立的微阵列实验，但由于项目特异性因素（例如，微阵列制备、环境影响）和基因-基因相关性诱导的有效依赖性。在此，我们的特点的性质，依赖性大肠杆菌微阵列纲要，并探讨其后果的问题上，确定哪些和有多少阵列使用相关性为基础的TRNI。我们提出了大量有效的证据之间的依赖关系，在这个纲要中的微阵列，并描述了实验条件因素的依赖关系。然后，我们引入了一个衡量纲要中有效实验数量的指标neff，并发现对应于在这个特定纲要中观察到的依赖性，有效样本量大幅减少，即，neff = 14.7 vs. n = 376。此外，我们发现，实验的选择子集的neff实际上超过了完整纲要的neff，这表明谚语“少即是多”在这里适用。与后一结果一致，我们观察到使用数据子集的TRNI性能比使用完整纲要的结果有所改善。我们确定了实验条件因素的趋势与TRNI的性能和neff的变化，包括生长阶段和媒体类型。最后，利用已知的E.大肠杆菌基因调控相互作用的研究中，我们证明了从neff调整的p值导出的错误发现率（FDR）与基于RegulonDB真值集的FDR很好地匹配。这些结果支持利用neff作为一个有力的描述微阵列药典。此外，他们强调了一种简单的基于相关性的TRNI方法，该方法对显著边缘进行了有意义的统计测试，即使在真值集不可用时，也可适用于任何物种的药典。这项工作有利于一个更完善的方法来构建和利用mRNA表达纲要在TRNI。
Transcriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.g., microarray preparation, environmental effects) in the multi-project compendium setting and effective dependency induced by gene-gene correlations. Herein, we characterize the nature of dependency in an Escherichia coli microarray compendium and explore its consequences on the problem of determining which and how many arrays to use in correlation-based TRNI. We present evidence of substantial effective dependency among microarrays in this compendium, and characterize that dependency with respect to experimental condition factors. We then introduce a measure neff of the effective number of experiments in a compendium, and find that corresponding to the dependency observed in this particular compendium there is a huge reduction in effective sample size i.e., neff = 14.7 versus n = 376. Furthermore, we found that the neff of select subsets of experiments actually exceeded neff of the full compendium, suggesting that the adage 'less is more' applies here. Consistent with this latter result, we observed improved performance in TRNI using subsets of the data compared to results using the full compendium. We identified experimental condition factors that trend with changes in TRNI performance and neff , including growth phase and media type. Finally, using the set of known E. coli genetic regulatory interactions from RegulonDB, we demonstrated that false discovery rates (FDR) derived from neff -adjusted p-values were well-matched to FDR based on the RegulonDB truth set. These results support utilization of neff as a potent descriptor of microarray compendia. In addition, they highlight a straightforward correlation-based method for TRNI with demonstrated meaningful statistical testing for significant edges, readily applicable to compendia from any species, even when a truth set is not available. This work facilitates a more refined approach to construction and utilization of mRNA expression compendia in TRNI.
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影响因子：
4.8
作者：
Piper, MDW;Daran-Lapujade, P;Pronk, JT
通讯作者：
Pronk, JT
影响因子：
--
作者：
Zare H;Sangurdekar D;Srivastava P;Kaveh M;Khodursky A
通讯作者：
Khodursky A
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
DOI：
10.1198/016214506000001211
发表时间：
2007-03-01
影响因子：
3.7
作者：
Efron, Bradley
通讯作者：
Efron, Bradley
DOI：
10.1214/09-aoas236
发表时间：
2009-01-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Efron B
通讯作者：
Efron B