Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks
Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks
复制标题
用于识别癌症特征特异性基因调控网络的自适应网络分析器
DOI：
10.1089/cmb.2017.0120
复制
发表时间：
2018
期刊：
影响因子：
1.7
通讯作者：
Miyano S
中科院分区：
文献类型：
--
作者：
Park H;Shimamura T;Imoto S;Miyano S
目前有很多关于样本（患者）特异性基因调控网络识别的讨论，因为有效构建的样本特异性基因网络导致有效的个性化癌症治疗。虽然已经提出了用于推断基因调控网络的统计方法，但是这些方法不能揭示样本特定的特征，因为现有的方法，例如anL 1型正则化，提供了所有样本的平均结果。因此，我们不能揭示转录调控网络中的样品特异性特征。针对这一问题，提出了基于核的L1型正则化的网络分析器。网络分析器基于高斯核函数对每个样本施加权重，以控制样本对目标样本建模的影响，其中权重的大小取决于样本之间癌症特征的相似性。然而，该方法不能很好地对稀疏区域（即，对于目标样本，只有少数样本具有与目标样本相似的特征，其中该特征被认为是样本特异性基因网络构建中的调制器），因为高斯核函数中的恒定带宽不能有效地对样本进行分组以用于在稀疏区域中对目标样本进行建模。癌症特征，如抗癌药物敏感性，通常是不均匀分布的，因此在稀疏区域中对样本进行建模也是一个至关重要的问题。我们提出了一种新的kernel-basedL 1型正则化方法的基础上修改的最近邻（KNN）-高斯核函数，称为自适应NetworkProfiler。通过使用修改后的KNN-高斯核函数，我们的方法提供了对调制器分布的鲁棒结果，并根据癌症特征对样本进行适当分组，以进行样本特定的分析。此外，我们提出了一个样本特定的广义交叉验证选择样本特定的调整参数在kernel-basedL 1-type正则化方法。数值研究表明，所提出的自适应NetworkProfiler有效地执行样本特定的基因网络的建设。我们将提出的统计策略应用于公开的桑格基因组数据分析，并提取抗癌药物敏感性特异性基因调控网络。
There is currently much discussion about sample (patient)-specific gene regulatory network identification, since the efficiently constructed sample-specific gene networks lead to effective personalized cancer therapy. Although statistical approaches have been proposed for inferring gene regulatory networks, the methods cannot reveal sample-specific characteristics because the existing methods, such as anL1-type regularization, provide averaged results for all samples. Thus, we cannot reveal sample-specific characteristics in transcriptional regulatory networks. To settle on this issue, the NetworkProfiler was proposed based on the kernel-basedL1-type regularization. The NetworkProfiler imposes a weight on each sample based on the Gaussian kernal function for controlling effect of samples on modeling a target sample, where the amount of weight depends on similarity of cancer characteristics between samples. The method, however, cannot perform gene regulatory network identification well for a target sample in a sparse region (i.e., for a target sample, there are only a few samples having a similar characteristic of the target sample, where the characteristic is considered as a modulator in sample-specific gene network construction), since a constant bandwidth in the Gaussian kernel function cannot effectively group samples for modeling a target sample in sparse region. The cancer characteristics, such as an anti-cancer drug sensitivity, are usually nonuniformly distributed, and thus modeling for samples in a sparse region is also a crucial issue. We propose a novel kernel-basedL1-type regularization method based on a modifiedk-nearest neighbor (KNN)-Gaussian kernel function, called an adaptive NetworkProfiler. By using the modified KNN-Gaussian kernel function, our method provides robust results against the distribution of modulators, and properly groups samples according to a cancer characteristic for sample-specific analysis. Furthermore, we propose a sample-specific generalized cross-validation for choosing the sample-specific tuning parameters in the kernel-basedL1-type regularization method. Numerical studies demonstrate that the proposed adaptive NetworkProfiler effectively performs sample-specific gene network construction. We apply the proposed statistical strategy to the publicly available Sanger Genomic data analysis, and extract anti-cancer drug sensitivity-specific gene regulatory networks.