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目的:研究混合效应模型(Mixed Effects Model)在肿瘤表达谱基因芯片数据分析中的检验效能,并探讨其分析效果。方法:采用混合效应模型分析肿瘤实例基因芯片数据,并以基因集富集分析方法(GSEA)作为参照比较分析结果的有效性和科学性,探讨其检验效果。结果:通过混合效应模型和基因集富集分析(GSEA)两种方法对肿瘤基因芯片数据的分析和比较,两种方法筛选出共同的差异表达通路外,混合效应模型额外地筛选出来GSEA未能检验到的8条差异表达通路,且得到文献支持;混和效应模型筛选出的前10个差异表达通路中有6个已有生物学证明而基因集富集分析方法(GSEA)筛选出的前10个差异表达通路中仅有4个已有生物学证明。结论:混合效应模型作为top-down方法中的典型代表,其优势在于通过构建潜变量达到降维目的,可有效地减少多个复杂的变异来源从而保证了结果的准确性和科学性,其检验效能优于基因集富集分析方法(GSEA),是一种行之有效的筛选肿瘤基因芯片数据的分析方法。
OBJECTIVE: To study the effectiveness of the mixed effects model in the data analysis of tumor expression microarray data and to analyze its analytical results. Methods: The mixed-effect model was used to analyze the tumor gene microarray data, and the validity and scientificity of the results were analyzed by gene enrichment assay (GSEA). Results: The mixed-effect model and gene enrichment analysis (GSEA) were used to analyze and compare the data of tumor microarray data, and the two methods screened out the common differential expression pathways. The mixed effect model was additionally screened out by GSEA Eight differentially expressed pathways were identified and supported by the literature. Six of the top 10 differentially expressed pathways screened by the mixed-effect model were bioassayed and the first ten (10) selected by the Gene Enrichment Assay (GSEA) Only four of the differential expression pathways have been shown to have biological evidence. CONCLUSIONS: As a typical representative of top-down method, the hybrid effect model has the advantage of reducing the number of latent variables by constructing latent variables and effectively reducing the number of complex sources of variation so as to ensure the accuracy and scientificness of the results. The test The method is superior to the method of gene enrichment analysis (GSEA) and is an effective method of screening tumor gene chip data.