Analyzing Multiple Phenotypes Based on Principal Component Analysis

doi:10.1007/s10255-022-1019-2

	Analyzing Multiple Phenotypes Based on Principal Component Analysis
	Bu, De-liang 1,2; Zhang, San-guo 1,2; Li, Na 1,3
刊名	ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES
	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
DOI	10.1007/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
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000870252400007
内容类型	期刊论文
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.