Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach
Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach
复制标题
DOI：
10.1186/1742-4682-2-23
复制
发表时间：
2005-01-01
影响因子：
--
通讯作者：
Strimmer, Korbinian
中科院分区：
文献类型：
--
作者：
Boulesteix, Anne-Laure;Strimmer, Korbinian
背景：转录因子及其靶标之间网络的研究对于理解细胞中复杂的调控机制非常重要。不幸的是，使用标准微阵列实验不可能直接测量转录因子活性 (TFA)，因为它们自身的转录水平会受到翻译后修饰的影响。结果：在这里，我们提出了一种基于偏最小二乘 (PLS) 回归的统计方法，通过结合 mRNA 表达和 DNA-蛋白质结合测量来推断真实的 TFA。该方法对于小样本来说在统计上也是合理的，并且允许通过“元”转录因子的概念检测转录因子之间的功能相互作用。此外，它还能够识别 ChIP 数据中的假阳性，并区分激活和抑制活动。结论：所提出的方法对于模拟数据以及来自酵母和大肠杆菌实验的真实表达和 ChIP 数据都表现良好。它克服了以前使用的 TFA 估算方法的局限性。估计的概况也可以作为进一步研究的输入，例如周期性或差异调节的测试。实现所提出方法的 R 包“plsgenomics”可从 CRAN 档案下载。
Background: The study of the network between transcription factors and their targets is important for understanding the complex regulatory mechanisms in a cell. Unfortunately, with standard microarray experiments it is not possible to measure the transcription factor activities (TFAs) directly, as their own transcription levels are subject to post-translational modifications.Results: Here we propose a statistical approach based on partial least squares (PLS) regression to infer the true TFAs from a combination of mRNA expression and DNA-protein binding measurements. This method is also statistically sound for small samples and allows the detection of functional interactions among the transcription factors via the notion of "meta"-transcription factors. In addition, it enables false positives to be identified in ChIP data and activation and suppression activities to be distinguished.Conclusion: The proposed method performs very well both for simulated data and for real expression and ChIP data from yeast and E. Coli experiments. It overcomes the limitations of previously used approaches to estimating TFAs. The estimated profiles may also serve as input for further studies, such as tests of periodicity or differential regulation. An R package "plsgenomics" implementing the proposed methods is available for download from the CRAN archive.