Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments.-猫眼课题宝

DOI：

10.1186/1471-2105-6-106

发表时间：

2005-04-25

期刊：

BMC BIOINFORMATICS

影响因子：

通讯作者：

中科院分区：

生物学4区

文献类型：

作者：

Liu, H;Tarima, S;Borders, AS;Getchell, TV;Getchell, ML;Stromberg, AJ

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1127068/?report=classic

聚类分析用于分析微阵列时间过程数据，用于基因发现和模式识别。然而，一般来说，这些方法没有利用时间是连续变量的事实，并且现有的聚类方法通常将生物学上不相关的基因分组在一起。我们提出了一个二次回归方法，用于识别差异表达基因和分类的基因的基础上，他们的时间表达谱的非循环短时间过程的微阵列数据。该方法将时间视为连续变量，从而保留了实际的时间信息。我们应用这种方法的基因表达的微阵列时间过程中的研究，在短时间间隔后的嗅觉受体神经元去传入。九个回归模式已被确定，并显示适合基因表达谱比k均值聚类。EASE分析确定了在每个回归模式和每个k均值聚类中的过度代表的功能组，这进一步证明了回归方法比k均值聚类方法提供了更有生物学意义的基因表达谱分类。与Peddada et al.的顺序限制推理方法表明，我们的方法提供了一个不同的角度对时间基因谱。可靠性研究表明，回归模式具有最高的可靠性。我们的研究结果表明，提出的二次回归方法提高了非循环短时间过程的微阵列数据的基因发现和模式识别。有了一个免费访问的Excel宏，研究人员可以很容易地将这种方法应用于他们的微阵列数据。

Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together. We propose a quadratic regression method for identification of differentially expressed genes and classification of genes based on their temporal expression profiles for non-cyclic short time-course microarray data. This method treats time as a continuous variable, therefore preserves actual time information. We applied this method to a microarray time-course study of gene expression at short time intervals following deafferentation of olfactory receptor neurons. Nine regression patterns have been identified and shown to fit gene expression profiles better than k-means clusters. EASE analysis identified over-represented functional groups in each regression pattern and each k-means cluster, which further demonstrated that the regression method provided more biologically meaningful classifications of gene expression profiles than the k-means clustering method. Comparison with Peddada et al.'s order-restricted inference method showed that our method provides a different perspective on the temporal gene profiles. Reliability study indicates that regression patterns have the highest reliabilities. Our results demonstrate that the proposed quadratic regression method improves gene discovery and pattern recognition for non-cyclic short time-course microarray data. With a freely accessible Excel macro, investigators can readily apply this method to their microarray data.