Ihmels Jan, Friedlander Gilgi, Bergmann Sven, Sarig Ofer, Ziv Yaniv, Barkai Naama
Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel.
Nat Genet. 2002 Aug;31(4):370-7. doi: 10.1038/ng941. Epub 2002 Jul 22.
Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.
标准聚类方法应用于相对较小的数据集时能够成功地对基因进行分类，但在大规模表达数据分析中的应用有限，这主要是由于它们将一个基因分配到单个聚类中。在此，我们提出一种用于全基因组表达数据全局分析的替代方法。我们的方法将基因分配到依赖于上下文且可能重叠的“转录模块”，从而克服了传统聚类方法的主要局限性。我们使用该方法阐明细胞通路的调控特性并对顺式调控元件进行表征。通过将我们的算法系统地应用于酿酒酵母所有可用的表达数据，我们识别出了一组全面的重叠转录模块。我们的结果为众多基因提供了功能预测，识别了模块之间的关系，并呈现了转录网络的全局视图。
