Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation.
Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation.
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从转录组数据中基于网络识别差异调节因子的算法：系统评估。
DOI：
10.1007/s11427-014-4762-7
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Zhao Z
中科院分区：
文献类型：
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作者：
Yu H;Mitra R;Yang J;Li Y;Zhao Z
识别差异调控因子对于理解细胞系统的动力学和疾病的分子机制至关重要。最近，通过使用转录组和网络数据，已经为此目的开发了几种计算算法。然而，在很大程度上仍然不清楚哪种算法在特定条件下表现得更好。这些知识对于这些算法的适当应用和未来的增强都是重要的。在这里，我们使用模拟数据集和真实数据集系统地评估了七种主要算法(TED、TDD、TFactS、Rif1、Rif2、dCSA_T2T和dCSA_r2t)。在我们的模拟评估中，我们人为地停用了一个或多个调节器，并检查了每个算法检测已知金本位调节器的能力。我们发现，所有这些算法都能有效地识别由于调节网络差异而产生的信号，这表明了我们的仿真方案的有效性。在所测试的7种算法中，TED和TFactS在区分准确率和对数据变化的稳健性方面分别排名第一和第二。当应用于两个独立的肺癌数据集时，TED和TFactS都复制了各自差异调控因子的相当大一部分。由于TED和TFactS依赖于转录组数据的两个不同特征，即差异共表达和差异表达，因此两者在实际应用中都可以作为相互参考。
Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases. Several computational algorithms have recently been developed for this purpose by using transcriptome and network data. However, it remains largely unclear which algorithm performs better under a specific condition. Such knowledge is important for both appropriate application and future enhancement of these algorithms. Here, we systematically evaluated seven main algorithms (TED, TDD, TFactS, RIF1, RIF2, dCSA_t2t, and dCSA_r2t), using both simulated and real datasets. In our simulation evaluation, we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators. We found that all these algorithms could effectively discern signals arising from regulatory network differences, indicating the validity of our simulation schema. Among the seven tested algorithms, TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered. When applied to two independent lung cancer datasets, both TED and TFactS replicated a substantial fraction of their respective differential regulators. Since TED and TFactS rely on two distinct features of transcriptome data, namely differential co-expression and differential expression, both may be applied as mutual references during practical application.