Margolin Adam A, Nemenman Ilya, Basso Katia, Wiggins Chris, Stolovitzky Gustavo, Dalla Favera Riccardo, Califano Andrea
Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
BMC Bioinformatics. 2006 Mar 20;7 Suppl 1(Suppl 1):S7. doi: 10.1186/1471-2105-7-S1-S7.
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.
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。现有的用于此类网络全基因组“反向工程”的计算方法仅在基因组简单的低等真核生物中取得了成功。在此，我们提出了ARACNE，这是一种新颖的算法，它利用微阵列表达谱，专门设计用于扩展到哺乳动物细胞中调控网络的复杂性，同时具有足够的通用性以解决更广泛的网络反卷积问题。该方法采用信息论方法来消除共表达方法推断出的大多数间接相互作用。
我们证明，如果网络拓扑结构中环的影响可忽略不计，ARACNE能（渐近地）精确重建网络，并且我们表明该算法在实际应用中效果良好，即使存在大量环和复杂拓扑结构。我们使用逼真的合成数据集和来自人类B细胞的微阵列数据集评估了ARACNE重建转录调控网络的能力。在合成数据集上，ARACNE实现了非常低的错误率，并且优于诸如相关网络和贝叶斯网络等已确立的方法。将其应用于人类B细胞遗传网络的反卷积，证明了ARACNE推断cMYC原癌基因经过验证的转录靶点的能力。我们还研究了互信息估计错误对网络重建的影响，并表明基于互信息排序的算法对估计错误更具弹性。
ARACNE在识别哺乳动物细胞网络中的直接转录相互作用方面显示出前景，这一问题一直困扰着现有的反向工程算法。这种方法应能增强我们利用微阵列数据阐明细胞过程潜在功能机制以及识别哺乳动物细胞网络中药理化合物分子靶点的能力。
