A Log-Linear Graphical Model for inferring genetic networks from high-throughput sequencing data
A Log-Linear Graphical Model for inferring genetic networks from high-throughput sequencing data
复制标题
用于从高通量测序数据推断遗传网络的对数线性图形模型
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Zhandong Liu
中科院分区：
文献类型：
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作者：
Genevera I. Allen;Zhandong Liu
高斯图形模型通常用于基于微阵列表达数据推断基因网络。然而，许多科学家已经开始使用高通量测序技术来测量基因表达。由于所得的高维计数数据由每个基因的测序读数的计数组成，因此高斯图形模型对于基于该离散数据对基因网络进行建模不是最佳的。我们开发了一种用于估计高维泊松图模型的新方法，即对数线性图模型，使我们能够基于高通量测序数据推断网络。我们的模型假设了一个成对的马尔可夫性质：在所有其他变量的条件下，每个变量都是泊松。我们估计我们的模型局部通过邻域选择拟合1-范数惩罚对数线性模型。此外，我们开发了一个快速的并行算法，使我们能够适应我们的图形模型，高维基因组数据集。我们通过模拟和乳腺癌microRNA网络的案例研究说明了我们的方法从计数数据中恢复网络结构的有效性。
Gaussian graphical models are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies to measure gene expression. As the resulting high-dimensional count data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for modeling gene networks based on this discrete data. We develop a novel method for estimating high-dimensional Poisson graphical models, the Log-Linear Graphical Model, allowing us to infer networks based on high-throughput sequencing data. Our model assumes a pair-wise Markov property: conditional on all other variables, each variable is Poisson. We estimate our model locally via neighborhood selection by fitting 1-norm penalized log-linear models. Additionally, we develop a fast parallel algorithm permitting us to fit our graphical model to high-dimensional genomic data sets. We illustrate the effectiveness of our methods for recovering network structure from count data through simulations and a case study on breast cancer microRNA networks.
影响因子：
7
作者：
Marioni, John C.;Mason, Christopher E.;Gilad, Yoav
通讯作者：
Gilad, Yoav
影响因子：
2.1
作者：
Li, Jun;Witten, Daniela M.;Tibshirani, Robert
通讯作者：
Tibshirani, Robert