Network-based modular latent structure analysis.
Network-based modular latent structure analysis.
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DOI：
10.1186/1471-2105-15-s13-s6
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发表时间：
2014
影响因子：
3
通讯作者：
Bai Y
中科院分区：
文献类型：
--
作者：
Yu T;Bai Y
高通量表达数据，例如基因表达和代谢组学数据，呈现出模块结构。每个模块中的特征组遵循一个潜在因子模型，而在模块之间，潜在因子是准独立的。恢复潜在因子能够揭示表达的隐藏调控模式。检测此类模块以及恢复潜在因子的困难在于数据的高维度以及模块成员信息的缺乏。
在此我们描述一种基于共表达网络中群落检测的方法。它包括基于推断的网络构建、模块检测以及从模块中进行相互作用的潜在因子检测。
在模拟中，当输入信号不是高斯分布时，该方法优于基于投影的模块潜在因子发现方法。我们还展示了该方法在实际数据分析中的价值。
新方法nMLSA（基于网络的模块潜在结构分析）在检测潜在结构方面是有效的，并且易于扩展到非线性情况。该方法的R代码可在http://web1.sph.emory.edu/users/tyu8/nMLSA/获取。
High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership. Here we describe a method based on community detection in the co-expression network. It consists of inference-based network construction, module detection, and interacting latent factor detection from modules. In simulations, the method outperformed projection-based modular latent factor discovery when the input signals were not Gaussian. We also demonstrate the method's value in real data analysis. The new method nMLSA (network-based modular latent structure analysis) is effective in detecting latent structures, and is easy to extend to non-linear cases. The method is available as R code at http://web1.sph.emory.edu/users/tyu8/nMLSA/.
影响因子：
1.3
作者：
Yu, Tianwei;Zhao, Yize;Shen, Shihao
通讯作者：
Shen, Shihao