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Analyzing Multiple Phenotypes Based on Principal Component Analysis

文献类型：期刊论文

作者Bu, De-liang1,2; Zhang, San-guo1,2; Li, Na1,3
刊名ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES
出版日期2022-10-01
卷号38期号:4页码:843-860
关键词multiple phenotypes principal component analysis cauchy combination method
ISSN号0168-9673
DOI10.1007/s10255-022-1019-2
英文摘要Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase 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 popular 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 paper, 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.
资助项目Key Program of Joint Funds of the National Natural Science Foundation of China[U19B2040] ; Fundamental Research Funds for Central Universities ; University of Chinese Academy of Sciences[Y95401TXX2] ; Beijing Natural Science Foundation[Z190004]
WOS研究方向Mathematics
语种英语
WOS记录号WOS:000870252400007
出版者SPRINGER HEIDELBERG
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/60823]  
专题中国科学院数学与系统科学研究院
通讯作者Li, Na
作者单位1.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, LSC, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Bu, De-liang,Zhang, San-guo,Li, Na. Analyzing Multiple Phenotypes Based on Principal Component Analysis[J]. ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES,2022,38(4):843-860.
APA Bu, De-liang,Zhang, San-guo,&Li, Na.(2022).Analyzing Multiple Phenotypes Based on Principal Component Analysis.ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES,38(4),843-860.
MLA Bu, De-liang,et al."Analyzing Multiple Phenotypes Based on Principal Component Analysis".ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES 38.4(2022):843-860.

入库方式： OAI收割

来源：数学与系统科学研究院

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