Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks

IF 0.9 4区 数学 Q3 Mathematics Statistical Applications in Genetics and Molecular Biology Pub Date : 2016-06-01 DOI:10.1515/sagmb-2014-0075
V. Vinciotti, L. Augugliaro, A. Abbruzzo, E. Wit
{"title":"Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks","authors":"V. Vinciotti, L. Augugliaro, A. Abbruzzo, E. Wit","doi":"10.1515/sagmb-2014-0075","DOIUrl":null,"url":null,"abstract":"Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"15 1","pages":"193 - 212"},"PeriodicalIF":0.9000,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2014-0075","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Applications in Genetics and Molecular Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1515/sagmb-2014-0075","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 19

Abstract

Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
阶乘高斯图形模型的模型选择及其在动态调节网络中的应用
因子高斯图模型(fGGMs)最近被提出用于从基因组高通量数据推断动态基因调控网络。在巨大的可能网络空间中寻找真正的调节关系时,这些模型允许对这些关系的动态性质施加某些限制,例如低阶的马尔可夫依赖性-精度矩阵的一些条目是先验的零-或跨时间滞后的相等依赖强度-精度矩阵的一些条目被假设为相等。然后通过11惩罚的最大似然估计精度矩阵,对其条目的绝对值施加进一步的约束,从而产生稀疏网络。选择最优的稀疏度级别是这类方法的主要挑战。在本文中,我们通过两个模拟现实生物过程的调节网络来评估fGGMs的一些模型选择标准的性能。分析表明,与其他推断动态网络的方法和KLCV准则相比,fGGMs具有良好的性能,特别是在模型选择方面。最后,我们介绍了一种高分辨率时程微阵列数据的应用,该数据来自脑膜炎奈瑟菌,一种危及生命的感染,如脑膜炎的病原体。本文中描述的方法是在R软件包sglassso中实现的,可以在CRAN上免费获得,http://CRAN.R-project.org/package=sglasso。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
期刊最新文献
Empirically adjusted fixed-effects meta-analysis methods in genomic studies. A CNN-CBAM-BIGRU model for protein function prediction. A heavy-tailed model for analyzing miRNA-seq raw read counts. Flexible model-based non-negative matrix factorization with application to mutational signatures. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1