Learning gene regulatory networks from next generation sequencing data.
Learning gene regulatory networks from next generation sequencing data.
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DOI：
10.1111/biom.12682
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发表时间：
2017-12
期刊：
影响因子：
1.9
通讯作者：
Liang F
中科院分区：
文献类型：
--
作者：
Jia B;Xu S;Xiao G;Lamba V;Liang F
近年来，下一代测序（NGS）已逐渐取代微阵列作为测量基因表达的主要平台。与微阵列相比，NGS具有噪声小、通量高等优点。然而，NGS数据的离散性也对现有的统计方法提出了挑战。特别是，仍然缺乏一个适当的统计方法来重建基因调控网络使用NGS数据在文献中。现有的局部泊松图模型方法不具有一致性，只能推断网络的某些局部结构。在本文中，我们提出了一个基于随机效应模型的转换连续NGS数据，然后我们通过半参数变换连续的数据转换为高斯和应用一个等效的偏相关选择方法来重建基因调控网络。所提出的方法是一致的。数值结果表明，该方法可以导致更准确的基因调控网络的推理比局部泊松图模型和其他现有的方法。本文提出的数据连续化转换方法填补了离散数据到连续数据转换的理论空白，方便了NGS数据分析。所提出的数据连续化转换也使得在基因调控网络的重建中集成不同类型的数据（例如微阵列和RNA-seq数据）成为可能。
In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network. In this paper, we propose a random effect model-based transformation to continuize NGS data, and then we transform the continuized data to Gaussian via a semiparametric transformation and apply an equivalent partial correlation selection method to reconstruct gene regulatory networks. The proposed method is consistent. The numerical results indicate that the proposed method can lead to much more accurate inference of gene regulatory networks than the local Poisson graphical model and other existing methods. The proposed data-continuized transformation fills the theoretical gap for how to transform discrete data to continuous data and facilitates NGS data analysis. The proposed data-continuized transformation also makes it feasible to integrate different types of data, such as microarray and RNA-seq data, in reconstruction of gene regulatory networks.
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