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hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests

机译：hARACNe：通过高阶数据处理不平等测试提高监管模型逆向工程的准确性

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摘要

A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
机译：系统生物学的主要目标是基于对细胞背景特定的分子相互作用模型的系统性和全基因组理解，阐明与生理和病理表型相关的分子机制。为此，基于大分子谱数据集的可用性，已经使用逆向工程方法来系统地解剖特定组织中的调节相互作用，从而提高了我们对复杂疾病（例如癌症）的机械理解。在本文中，我们介绍了用于重建精确蜂窝网络（hARACNe）的高阶算法，这是ARACNe算法用于转录调控网络解剖的扩展。 ARACNe使用信息论中的数据处理不等式（DPI）来检测和修剪间接交互，这些间接交互不太可能由实际的物理交互来介导。鉴于ARACNe仅考虑一阶间接相互作用，即仅由一个额外的调节剂介导的相互作用，而hARACNe则考虑了通过两个，三个或更多其他调节剂的广义形式的间接相互作用。我们显示，基于转录因子（TF）特定的ChIP芯片数据，以及基于RNAi介导的TF沉默后的基因表达谱，使用高阶DPI可以显着提高性能。

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