Peddada Shyamal D, Lobenhofer Edward K, Li Leping, Afshari Cynthia A, Weinberg Clarice R, Umbach David M
Biostatistics Branch, Laboratory of Molecular Carcinogenesis, Research Triangle Park, NC 27709, USA.
Bioinformatics. 2003 May 1;19(7):834-41. doi: 10.1093/bioinformatics/btg093.
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.
我们提出了一种算法，可利用基因表达数据根据基因的时间进程或剂量反应谱来选择基因并进行聚类。所提出的算法基于统计学中发展起来的序贯受限推理方法。我们描述了用于时间进程实验的方法，尽管它适用于任何有序的处理集。候选时间谱是根据时间点上平均表达水平之间的不等式来定义的。所提出的算法在基因满足基于自展法的统计显著性标准时选择这些基因，并将每个选定的基因分配到最适合的候选谱中。我们使用来自一个cDNA微阵列实验的数据来说明该方法，在该实验中，一种乳腺癌细胞系用雌激素刺激不同的时间间隔。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具有生物学意义的基因。
