基于功能模块的基因表达谱聚类分析

Clustering of Gene Expression Profiles Based on Functional Modules

按GENE ONTOLOGY基因功能分类体系,将基因模块化地组织成具有显著生物意义的低维功能模块单元,并将其作为新的分析指标用于分类微阵列疾病样本,从而提出了基于功能表达谱的聚类分析新途径.采用NC I60数据集,通过功能表达谱对组织样本进行聚类分析.结果显示,新算法不但得到高准确度的样本分型结果,而且能够直接从功能水平上给出相应的生物学解释.同时,用基于功能表达谱对组织样本进行聚类分析可以显著降低特征维数,有效地处理高检测误差与基因表达变异问题.

Traditional clustering analysis of gene expression profiles is challenged by high measurement noise,curse of dimensionality and lacking of coherence in biological interpretations. Functional expression profiles （FEP）, which is obtained by organizing the original genes expression profiles onto lowdimension functional modules using Gene Ontology, is proposed as new analysis indexes to cluster microarray disease samples in our novel method to tackle with the above issues. We compare the performance of hierarchical clustering and k-means clustering based on FEP and the conventional gene expression profiles （GEP） using the NCI60 dataset. The analysis results indicate that precise clustering of disease types and biological function comprehension of the analysis results can be achieved directly with FEP. In addition, FEP can also significantly reduce dimension and tackle with high with measurement noise efficiently.