Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference
Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference
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DOI：
10.1093/bioinformatics/btg093
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发表时间：
2003-05-01
期刊：
影响因子：
5.8
通讯作者：
Umbach, DM
中科院分区：
文献类型：
--
作者：
Peddada, SD;Lobenhofer, EK;Umbach, DM
我们提出了一种使用基因表达数据根据基因的时间过程或剂量反应曲线选择和聚类基因的算法。所提出的算法基于统计学中开发的阶数限制推理方法。我们描述了时间过程实验的方法，尽管它适用于任何有序的治疗组。候选时间概况是根据时间点的平均表达水平之间的不平等来定义的。当基因满足基于引导程序的统计显着性标准时，所提出的算法会选择基因，并将每个选定的基因分配给最合适的候选配置文件。我们使用 cDNA 微阵列实验的数据来说明该方法，其中用雌激素刺激乳腺癌细胞系不同的时间间隔。在这个例子中，我们的方法能够识别出先前分析未能揭示的几个生物学上有趣的基因。
We propose an algorithm for selecting and clustering genes according to their time-course or dose-response profiles using gene expression data. The proposed algorithm is based on the order-restricted inference methodology developed in statistics. We describe the methodology for time-course experiments although it is applicable to any ordered set of treatments. Candidate temporal profiles are defined in terms of inequalities among mean expression levels at the time points. The proposed algorithm selects genes when they meet a bootstrap-based criterion for statistical significance and assigns each selected gene to the best fitting candidate profile. We illustrate the methodology using data from a cDNA microarray experiment in which a breast cancer cell line was stimulated with estrogen for different time intervals. In this example, our method was able to identify several biologically interesting genes that previous analyses failed to reveal.