Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models
Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models
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DOI：
10.1093/bioinformatics/btm639
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发表时间：
2008-04-01
期刊：
影响因子：
5.8
通讯作者：
Miyano, Satoru
中科院分区：
文献类型：
--
作者：
Hirose, Osamu;Yoshida, Ryo;Miyano, Satoru
动机：通过使用时间进程微阵列基因表达谱对基因网络进行统计推断是了解基因调控机制的时间结构的关键一步。不幸的是，目前的大多数研究都局限于分析少数基因，因为时间进程基因表达谱的长度相当短。为了克服这一局限，一种很有希望的方法是通过探索潜在的转录模块来推断基因网络，这些模块是共享相同功能或涉及相同路径的基因集合。结果：本文提出了一种基于状态空间模型的新方法来同时识别转录模块和基于模块的基因网络。状态空间模型有可能从时间进程的基因表达谱中推断出大规模的基因网络，例如10(3)阶。特别是，我们利用酿酒酵母的基因表达谱成功地鉴定了一个细胞周期系统，其时程长度和基因数目分别为24和4382。然而，当分析较短的时间进程数据时，例如长度为10或更短的数据，状态空间模型的参数估计经常由于过度拟合而失败。为了扩展状态空间模型的适用性，我们提供了一种方法来使用基因表达谱的技术副本，这些副本通常被测量为两个或三个副本。对于利用短时间数据实现基因网络的高效推断，技术复制的使用是重要的。通过对生长因子剥夺诱导的人脐静脉内皮细胞(HUVECs)基因表达谱的时程分析，证明了该方法的可行性。
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.