IPCAPS: Iterative Pruning to Capture Population Structure

An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.

Package details

AuthorKridsadakorn Chaichoompu [aut, cre], Kristel Van Steen [aut], Fentaw Abegaz [aut], Sissades Tongsima [aut], Philip Shaw [aut], Anavaj Sakuntabhai [aut], Luisa Pereira [aut]
MaintainerKridsadakorn Chaichoompu <[email protected]>
LicenseGPL-3
Version1.1.5
URL https://gitlab.com/kris.ccp/ipcaps
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("IPCAPS")

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IPCAPS documentation built on June 15, 2018, 1:05 a.m.