Bremer Martina, Doerge R W
Department of Mathematics, San Jose State University, One Washington Square, San Jose, CA 95192, USA.
Adv Bioinformatics. 2009;2009:284251. doi: 10.1155/2009/284251. Epub 2009 Oct 7.
We present a statistical method to rank observed genes in gene expression time series experiments according to their degree of regulation in a biological process. The ranking may be used to focus on specific genes or to select meaningful subsets of genes from which gene regulatory networks can be built. Our approach is based on a state space model that incorporates hidden regulators of gene expression. Kalman (K) smoothing and maximum (M) likelihood estimation techniques are used to derive optimal estimates of the model parameters upon which a proposed regulation criterion is based. The statistical power of the proposed algorithm is investigated, and a real data set is analyzed for the purpose of identifying regulated genes in time dependent gene expression data. This statistical approach supports the concept that meaningful biological conclusions can be drawn from gene expression time series experiments by focusing on strong regulation rather than large expression values.
我们提出了一种统计方法，用于在基因表达时间序列实验中，根据观察到的基因在生物过程中的调控程度对其进行排名。该排名可用于聚焦特定基因，或从其中选择有意义的基因子集，从而构建基因调控网络。我们的方法基于一个状态空间模型，该模型纳入了基因表达的隐藏调节因子。卡尔曼（K）平滑和最大（M）似然估计技术用于推导模型参数的最优估计值，而提出的调控标准正是基于这些参数。我们研究了所提算法的统计功效，并分析了一个真实数据集，目的是在时间依赖性基因表达数据中识别受调控的基因。这种统计方法支持这样一种观点，即通过关注强调控而非大表达值，可以从基因表达时间序列实验中得出有意义的生物学结论。
