Balasubramaniyan Rajarajeswari, Hüllermeier Eyke, Weskamp Nils, Kämper Jörg
Max-Planck Institute for Terrestrial Microbiology, Department of Organismic Interactions Karl-von-Frisch-Strasse, 35043 Marburg, Germany.
Bioinformatics. 2005 Apr 1;21(7):1069-77. doi: 10.1093/bioinformatics/bti095. Epub 2004 Oct 28.
Microarray technology enables the study of gene expression in large scale. The application of methods for data analysis then allows for grouping genes that show a similar expression profile and that are thus likely to be co-regulated. A relationship among genes at the biological level often presents itself by locally similar and potentially time-shifted patterns in their expression profiles.
Here, we propose a new method (CLARITY; Clustering with Local shApe-based similaRITY) for the analysis of microarray time course experiments that uses a local shape-based similarity measure based on Spearman rank correlation. This measure does not require a normalization of the expression data and is comparably robust towards noise. It is also able to detect similar and even time-shifted sub-profiles. To this end, we implemented an approach motivated by the BLAST algorithm for sequence alignment. We used CLARITY to cluster the times series of gene expression data during the mitotic cell cycle of the yeast Saccharomyces cerevisiae. The obtained clusters were related to the MIPS functional classification to assess their biological significance. We found that several clusters were significantly enriched with genes that share similar or related functions.
微阵列技术能够大规模地研究基因表达。数据分析方法的应用使得能够对那些显示出相似表达谱且因此可能受到共同调控的基因进行分组。基因在生物学层面的关系通常通过其表达谱中局部相似且可能存在时间偏移的模式表现出来。
在此，我们提出一种用于微阵列时间进程实验分析的新方法（CLARITY；基于局部形状相似性的聚类），该方法使用基于斯皮尔曼等级相关的基于局部形状的相似性度量。这种度量不需要对表达数据进行归一化，并且对噪声具有相当的鲁棒性。它还能够检测相似甚至时间偏移的子谱。为此，我们实现了一种受BLAST序列比对算法启发的方法。我们使用CLARITY对酿酒酵母有丝分裂细胞周期中的基因表达数据时间序列进行聚类。将获得的聚类与MIPS功能分类相关联以评估其生物学意义。我们发现几个聚类显著富集了具有相似或相关功能的基因。
