文章基本信息

标题：Principal Component Analyses in Anthropological Genetics
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作者：Xingdong Chen ; Chao Chen ; Li Jin 等
期刊名称：Advances in Anthropology
印刷版ISSN：2163-9353
电子版ISSN：2163-9361
出版年度：2011
卷号：1
期号：2
页码：9-14
DOI：10.4236/aa.2011.12002
出版社：Scientific Research Publishing
摘要：Principal component analyses (PCA) is a statistical method for exploring and making sense of datasets with a large number of measurements (which can be thought of as dimensions) by reducing the dimensions to the few principal components (PCs) that explain the main patterns. Thus, the first PC is the mathematical combination of measurements that accounts for the largest amount of variability in the data. Here, we gave an interpretation about the principle of PCA and its original mathematical algorithm, singular variable decomposition (SVD). PCA can be used in study of gene expression; also PCA has a population genetics interpretation and can be used to identify differences in ancestry among populations and samples, through there are some limitations due to the dynamics of microevolution and historical processes, with advent of molecular techniques, PCA on Y chromosome, mtDNA, and nuclear DNA gave us more accurate interpretations than on classical markers. Furthermore, we list some new extensions and limits of PCA.
关键词：Principal Component Analysis; Singular Value Decomposition; Human Genetics