ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.
ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.
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DOI：
10.1186/1471-2105-7-s1-s7
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发表时间：
2006-03-20
影响因子：
3
通讯作者：
Califano A
中科院分区：
文献类型：
--
作者：
Margolin AA;Nemenman I;Basso K;Wiggins C;Stolovitzky G;Dalla Favera R;Califano A
阐明基因调控网络对于理解正常细胞生理学和复杂病理表型至关重要。现有的全基因组“逆向工程”的计算方法，这种网络已经成功地只有较低的真核生物与简单的基因组。在这里，我们提出了ARACNE，一种新的算法，使用微阵列表达谱，专门设计的规模在哺乳动物细胞中的调控网络的复杂性，但一般足以解决更广泛的网络去卷积问题。该方法使用信息论方法来消除由共表达方法推断的大多数间接相互作用。我们证明，ARACNE重建网络完全（渐近），如果在网络拓扑结构中的循环的影响可以忽略不计，我们表明，该算法在实践中工作良好，即使在存在大量的循环和复杂的拓扑结构。我们评估ARACNE的能力，重建转录调控网络，使用现实的合成数据集和人类B细胞的微阵列数据集。在合成数据集上，ARACNE实现了非常低的错误率，并优于相关网络和贝叶斯网络等已建立的方法。应用于人类B细胞中遗传网络的去卷积证明了ARACNE推断cMYC原癌基因的经验证的转录靶点的能力。我们还研究了误估计互信息对网络重构的影响，并表明基于互信息排序的算法对估计误差更有弹性。ARACNE显示在识别哺乳动物细胞网络中的直接转录相互作用方面的前景，这是一个挑战现有逆向工程算法的问题。这种方法应提高我们的能力，使用微阵列数据来阐明功能机制，细胞过程的基础，并确定在哺乳动物细胞网络中的药理化合物的分子靶点。
Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors. ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.