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Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models

机译：通过使用状态空间模型从时程基因表达谱中基于转录模块的基因网络进行统计推断

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Motivation: Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. Results: In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis.
机译：动机：通过使用时程微阵列基因表达谱对基因网络进行统计推断，是理解基因调控机制的时间结构的重要步骤。不幸的是，由于时程基因表达谱的长度相当短，当前的大多数研究都局限于分析少数基因。克服这种局限性的一种有前途的方法是通过探索潜在的转录模块来推断基因网络，这些转录模块是一组具有共同功能或参与同一途径的基因。结果：在本文中，我们提出了一种基于状态空间模型的新方法，可以同时识别转录模块和基于模块的基因网络。状态空间模型具有推断大规模基因网络的潜力，例如时程基因表达谱中的10（3）阶。特别地，我们通过使用酿酒酵母的基因表达谱成功鉴定了细胞周期系统，其中时间过程的长度和基因数分别为24和4382。但是，当分析较短的时程数据时，例如如果长度为10或更短，状态空间模型的参数估计通常会由于过度拟合而失败。为了扩展状态空间模型的适用性，我们提供了一种使用基因表达谱的技术复制品的方法，该技术复制品通常以一式两份或一式三份进行测量。使用技术复制品对于利用短时程数据实现基因网络的高效推断非常重要。通过时程分析人类脐静脉内皮细胞（HUVECs）经历生长因子剥夺诱导的细胞凋亡的基因表达谱，证明了该方法的潜力。

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