Statistical significance of combinatorial regulations
Statistical significance of combinatorial regulations
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DOI：
10.1073/pnas.1302233110
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发表时间：
2013-08-06
影响因子：
11.1
通讯作者：
Sese, Jun
中科院分区：
文献类型：
--
作者：
Terada, Aika;Okada-Hatakeyama, Mariko;Sese, Jun
三种以上的转录因子通常一起工作，使细胞能够对各种信号做出反应。然而，检测多个转录因子的组合调控不仅在计算上是不平凡的，而且由于多个测试校正而极不可能。测试数量的指数级增长迫使我们对最大arity设置严格的限制。在这里，我们提出了一个有效的分支定界算法称为“无限元多重测试过程”（LAMP）计数的确切数量的可测试的组合和校准的Bonferroni因子的最小可能值。LAMP列出了没有任何限制的重要组合，而整个家族的错误率被严格控制在阈值以下。在人类乳腺癌转录组中，LAMP发现了多达8个结合基序的统计学显著组合。这种方法可能有助于发现以协调方式调节的通路，并在异构数据中找到隐藏的关联。
More than three transcription factors often work together to enable cells to respond to various signals. The detection of combinatorial regulation by multiple transcription factors, however, is not only computationally nontrivial but also extremely unlikely because of multiple testing correction. The exponential growth in the number of tests forces us to set a strict limit on the maximum arity. Here, we propose an efficient branch-and-bound algorithm called the "limitless arity multiple-testing procedure" (LAMP) to count the exact number of testable combinations and calibrate the Bonferroni factor to the smallest possible value. LAMP lists significant combinations without any limit, whereas the family-wise error rate is rigorously controlled under the threshold. In the human breast cancer transcriptome, LAMP discovered statistically significant combinations of as many as eight binding motifs. This method may contribute to uncover pathways regulated in a coordinated fashion and find hidden associations in heterogeneous data.