典型文献
Analyzing Multiple Phenotypes Based on Principal Component Analysis
文献摘要：
Joint analysis of multiple phenotypes can have better interpretation of complex diseases and in-crease statistical power to detect more significant single nucleotide polymorphisms(SNPs)compare to traditional single phenotype analysis in genome-wide association analysis.Principle component analysis(PCA),as a pop-ular dimension reduction method,has been broadly used in the analysis of multiple phenotypes.Since PCA transforms the original phenotypes into principal components(PCs),it is natural to think that by analyzing these PCs,we can combine information across phenotypes.Existing PCA-based methods can be divided into two categories,either selecting one particular PC manually or combining information from all PCs.In this pa-per,we propose an adaptive principle component test(APCT)which selects and combines the PCs adaptively by using Cauchy combination method.Our proposed method can be seen as a generalization of traditional PCA based method since it contains two existing methods as special situation.Extensive simulation shows that our method is robust and can generate powerful result in various situations.The real data analysis of stock mice data also demonstrate that our proposed APCT can identify significant SNPs that are missed by traditional methods.
文献关键词：
中图分类号：
作者姓名：
De-liang BU;San-guo ZHANG;Na LI
作者机构：
School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Big Data Mining and Knowledge Management,Chinese Academy of Sciences,Beijing 100049,China;LSC,NCMIS,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China
文献出处：
引用格式：
[1]De-liang BU;San-guo ZHANG;Na LI-.Analyzing Multiple Phenotypes Based on Principal Component Analysis)[J].应用数学学报（英文版）,2022(04):843-860
A类：
Phenotypes,APCT
B类：
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AB值：
0.567386
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