Yu Tianwei, Sun Wei, Yuan Shinsheng, Li Ker-Chau
Department of Statistics, University of California Los Angeles, CA90095-1554, USA.
Bioinformatics. 2005 Sep 15;21(18):3651-7. doi: 10.1093/bioinformatics/bti599. Epub 2005 Aug 2.
Cellular processes are not isolated groups of events. Nevertheless, in most microarray analyses, they tend to be treated as standalone units. To shed light on how various parts of the interlocked biological processes are coordinated at the transcription level, there is a need to study the between-unit expressional relationship directly.
We approach this issue by constructing an index of correlation function to convey the global pattern of coexpression between genes from one process and genes from the entire genome. Processes with similar signatures are then identified and projected to a process-to-process association graph. This top-down method allows for detailed gene-level analysis between linked processes to follow up. Using the cell-cycle gene-expression profiles for Saccharomyces cerevisiae, we report well-organized networks of biological processes that would be difficult to find otherwise. Using another dataset, we report a sharply different network structure featuring cellular responses under environmental stress.
http://kiefer.stat.ucla.edu/lap2/download/KL_supplement.pdf.
细胞过程并非孤立的事件组。然而，在大多数微阵列分析中，它们往往被视为独立的单元。为了阐明相互关联的生物过程的各个部分如何在转录水平上协调，有必要直接研究单元间的表达关系。
我们通过构建一个相关函数指数来解决这个问题，以传达来自一个过程的基因与来自整个基因组的基因之间共表达的全局模式。然后识别具有相似特征的过程，并将其投影到一个过程到过程的关联图中。这种自上而下的方法允许对相关过程之间进行详细的基因水平分析，以便后续跟进。使用酿酒酵母的细胞周期基因表达谱，我们报告了组织良好的生物过程网络，否则很难找到这些网络。使用另一个数据集，我们报告了一种截然不同的网络结构，其特征是环境压力下的细胞反应。
http://kiefer.stat.ucla.edu/lap2/download/KL_supplement.pdf 。
