Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, for instance, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. In contrast to these test evaluations, applications to gene expression data of human cancers are often limited by fewer samples and more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtain reliable networks, and (2) even for state of the art methods, we expect that between 20 and 80% of edges are caused by copy number changes and cell mixtures rather than transcription factor regulation.

中文翻译：

---

非整倍性和样品杂质对癌症中的基因网络有偏见。

基因调控网络推论是用于从例如基因表达测量中获得结构化调控信息的标准技术。执行此任务的方法已在合成数据集（较小程度上）在真实数据集上得到了广泛评估。与这些测试评估相反，人类癌症的基因表达数据的应用通常受限于更少的样品和更多潜在的调控联系，并且受到拷贝数畸变以及细胞混合物和样品杂质的影响。在这里，我们以TCGA群组推论出的网络为例，来说明（1）转录因子注释对于获得可靠的网络是必不可少的，并且（2）即使对于最先进的方法，