不同表达谱芯片分析方法在药效机制研究中的应用

APPLICATION OF DIFFERENT MICROARRAY ANALYSIS METHODS IN PHARMACOLOGICAL MECHANISM RESEARCH

Affymetrix表达谱芯片技术是一种研究基因在不同条件下表达变化的高通量分析技术,当前在深度动态挖掘药物作用机制方面的芯片分析方法的应用研究仍比较少。以药物表达谱芯片数据为研究对象,运用不同的算法对芯片数据进行预处理,使用t检验和基因芯片显著性分析的方法筛选差异表达的基因,利用凝集型层次聚类和CPP-SOM聚类的方法对差异表达的基因进行聚类分析,最后运用两类富集分析工具DAVID和GSEA对药效可能涉及的信号通路、生物学过程进行相关生物学功能的富集。结果表明,RMA算法处理药物芯片数据优于MAS5.0算法和GCRMA算法;CPP-SOM聚类方法挖掘的数据信息更丰富;GSEA富集分析工具更适合用于药效机制的研究。本研究为新药研发提供算法支持。

Affymetrix cDNA microarray technology is a high-throughput analysis technology used for studying the variation of genes expression in different conditions.Currently the application researches in microarray analysis methods in the aspects of deep and dynamically mining the mechanism of drug actions are still few.In the paper we take the medicine microarray data as the studying object,use different algorithms to preprocess the microarray data,use the methods of t test and significance analysis of genes microarray （SAM）to sift out the genes in differential expression,and use agglomerative hierarchical clustering and CPP-SOM （component plan presentation integrated self-organising map）clustering method for clustering analysis on the differential expression genes,finally we use two kinds of enrichment analysis tools,DAVID and GSEA （gene set enrichment analysis）to carry out the enrichment of the associated biological functions possibly involved by the pharmacodynamics in signaling pathways and biological process.Results demonstrate that to process medicine microarray data with RMA algorithm is better than using MAS5.0 and GCRMA algorithms,and CPP-SOMclustering method can mine more abounded data information, and GSEA enrichment analysis tools are more appropriate for the researches in regard to pharmacodynamic mechanism.Our study provides the algorithm support for new drug discovery and research.