Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty.
Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty.
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DOI：
10.12688/f1000research.26128.2
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Hsu L
中科院分区：
文献类型：
--
作者：
Wu QV;Sun W;Hsu L
基因表达数据已用于推断基因-基因网络（GGN），其中两个基因之间的边缘意味着这两个基因在给定所有其他基因的情况下的条件依赖性。 Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation.大多数识别 GGN 的现有方法都采用带有 L1（套索）、L2（岭）或弹性净惩罚的惩罚回归，其范围涵盖 L1 到 L2 惩罚的范围。然而，对于高维基因表达数据，通常需要跨越 L0 和 L1 惩罚范围的惩罚，例如对数惩罚，以保证变量选择的一致性。因此，我们开发了一种在早期网络识别方法空间（稀疏部分相关估计）框架内采用对数惩罚的新颖方法，并将其实现到 R 包 space-log 中。我们表明，空间日志计算效率高（用 C 实现的源代码），并且与其他方法相比具有良好的性能，特别是对于具有集线器的网络。 Space-log 是开源的，可在 GitHub 上获取，https://github.com/wuqian77/SpaceLog
Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs. Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog