An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning.
An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning.
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DOI：
10.3389/fgene.2021.760299
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发表时间：
2021
影响因子：
3.7
通讯作者：
Kechris K
中科院分区：
文献类型：
--
作者：
Zhuang Y;Xing F;Ghosh D;Banaei-Kashani F;Bowler RP;Kechris K
生物网络通常通过高斯图形模型（GGMs）推断，仅使用基因或蛋白质表达数据。GGMs通过估计基因或蛋白质之间的精确矩阵来识别条件依赖性。然而，传统的GGM方法往往忽略了蛋白质-蛋白质相互作用（PPI）的先验知识。最近，一些研究小组将GGM扩展到加权图形Lasso （wGlasso）和基于网络的基因集分析（Netgsa），并证明了纳入PPI信息的优势。然而，这些方法要么在计算上难以处理大规模数据，要么忽略PPI网络中的权重。为了解决这些缺点，我们扩展了Netgsa方法，并开发了一种增强的高维图形Lasso （AhGlasso）方法，将已知PPI中的边缘权重与组学数据结合起来，用于全局网络学习。在模拟大规模数据设置的计算时间方面，这种新方法优于基于加权图形lasso的算法，同时实现了更好或相当的节点连接预测精度。在固定样本量（n = 300）的情况下，当图的大小在1000到3000之间时，AhGlasso的总运行时间大约是加权Glasso方法的5倍。ahglassso和加权glassso之间的运行时差异随着图大小的增加而增加。利用一项慢性阻塞性肺疾病研究的蛋白质组学数据，我们证明，与Netgsa方法相比，AhGlasso通过结合PPI信息改善了蛋白质网络推断。
Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to weighted graphical Lasso (wGlasso) and network-based gene set analysis (Netgsa) and have demonstrated the advantages of incorporating PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we extended the Netgsa approach and developed an augmented high-dimensional graphical Lasso (AhGlasso) method to incorporate edge weights in known PPI with omics data for global network learning. This new method outperforms weighted graphical Lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. The total runtime of AhGlasso is approximately five times faster than weighted Glasso methods when the graph size ranges from 1,000 to 3,000 with a fixed sample size (n = 300). The runtime difference between AhGlasso and weighted Glasso increases when the graph size increases. Using proteomic data from a study on chronic obstructive pulmonary disease, we demonstrate that AhGlasso improves protein network inference compared to the Netgsa approach by incorporating PPI information.
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影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
4.6
作者：
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Biancotto A
影响因子：
25
作者：
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通讯作者：
Yang XW
DOI：
10.1164/rccm.201808-1455so
发表时间：
2019-09-15
影响因子：
24.7
作者：
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通讯作者：
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影响因子：
5.8
作者：
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通讯作者：
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