Inferring gene expression networks with hubs using a degree weighted Lasso approach
Inferring gene expression networks with hubs using a degree weighted Lasso approach
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DOI：
10.1093/bioinformatics/bty716
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发表时间：
2019-03-15
期刊：
影响因子：
5.8
通讯作者：
Koeppl, Heinz
中科院分区：
文献类型：
--
作者：
Sulaimanov, Nurgazy;Kumar, Sunil;Koeppl, Heinz
基因组规模的基因网络包含称为枢纽的调控基因，这些基因有许多相互作用的伙伴。这些基因通常在基因调控和细胞过程中发挥重要作用。尽管最近在高通量技术方面取得了进展，但从高维数据中推断具有枢纽基因的基因网络仍然是一个具有挑战性的问题。新的统计网络推理方法是必要的高效和准确的重建枢纽网络从高维data.Results为了解决这一挑战，我们提出了DW-Lasso，度加权Lasso（最小绝对收缩和选择算子）的方法，推断基因网络与枢纽的低样本量设置下有效地。我们的网络重建方法被制定为一个两阶段的过程：第一，网络的程度迭代估计，第二，基因调控网络的重建使用度信息。所提出的方法的一个有用的属性是，它自然有利于枢纽基因周围的邻居的积累，从而有助于在假设基础网络表现出枢纽结构的情况下准确建模的高通量数据。在模拟研究中，我们证明了良好的预测性能相比，传统的Lasso型方法在推断枢纽和无标度图的方法。我们显示了我们的方法的有效性，在应用程序中的大肠杆菌和肾透明细胞癌的RNA测序数据的微阵列数据从癌症基因组Atlas datasets.Availability和实施GNU通用公共许可证在https://cran.r-project.org/package=DWLasso.Supplementary信息补充数据可在生物信息学在线。
Motivation Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from high-dimensional data still remains a challenging problem. Novel statistical network inference methods are needed for efficient and accurate reconstruction of hub networks from high-dimensional data.Results To address this challenge we propose DW-Lasso, a degree weighted Lasso (least absolute shrinkage and selection operator) method which infers gene networks with hubs efficiently under the low sample size setting. Our network reconstruction approach is formulated as a two stage procedure: first, the degree of networks is estimated iteratively, and second, the gene regulatory network is reconstructed using degree information. A useful property of the proposed method is that it naturally favors the accumulation of neighbors around hub genes and thereby helps in accurate modeling of the high-throughput data under the assumption that the underlying network exhibits hub structure. In a simulation study, we demonstrate good predictive performance of the proposed method in comparison to traditional Lasso type methods in inferring hub and scale-free graphs. We show the effectiveness of our method in an application to microarray data of Escherichia coli and RNA sequencing data of Kidney Clear Cell Carcinoma from The Cancer Genome Atlas datasets.Availability and implementation Under the GNU General Public Licence at https://cran.r-project.org/package=DWLasso.Supplementary informationSupplementary data are available at Bioinformatics online.