A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series
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DOI：
10.1089/cmb.2009.0175
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发表时间：
2010-03-01
影响因子：
1.7
通讯作者：
Borgwardt, Karsten M.
中科院分区：
文献类型：
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作者：
Stegle, Oliver;Denby, Katherine J.;Borgwardt, Karsten M.
了解生物体对环境变化反应的调控机制是分子生物学中的一个重要问题。迈向这一目标的第一步也是重要的一步是检测其表达水平受到外部条件变化影响的基因。已经提出了一系列测试差异基因表达的方法，无论是在静态实验中还是在时间过程实验中。虽然这些测试回答了基因是否差异表达的问题，但它们并不明确地解决基因何时差异表达的问题，尽管这些信息可能提供对调控计划的过程和因果结构的洞察。在这篇文章中，我们提出了一种双样本检验来识别微阵列时间序列中差异基因表达的区间。我们的方法基于高斯过程回归，可以处理任意重复次数，并且对异常值具有较强的稳健性。我们应用我们的算法研究了拟南芥基因对真菌病原体感染的响应，使用了一个涵盖24个观察时间点的30,336个基因探针的微阵列时间序列数据集。在分类实验中，我们的测试比现有方法更有利，并为依赖时间的差异表达提供了额外的见解。
Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.