文章基本信息

标题：IPCAPS: an R package for iterative pruning to capture population structure
本地全文：下载
作者：Kridsadakorn Chaichoompu ; Fentaw Abegaz ; Sissades Tongsima 等
期刊名称：Source Code for Biology and Medicine
印刷版ISSN：1751-0473
电子版ISSN：1751-0473
出版年度：2019
卷号：14
期号：1
页码：1-5
DOI：10.1186/s13029-019-0072-6
出版社：BioMed Central
摘要：Resolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target. This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors. IPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts.
关键词：Fine-scale structure ; Iterative pruning ; Population clustering ; Population genetics ; Outlier detection