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'.
- Version1.1.8
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- IPCAPS citation info
- Last release01/25/2021
Documentation
Team
Kridsadakorn Chaichoompu
Fentaw Abegaz
Show author detailsRolesAuthorKristel Van Steen
Show author detailsRolesAuthorSissades Tongsima
Show author detailsRolesAuthorPhilip Shaw
Show author detailsRolesAuthorAnavaj Sakuntabhai
Show author detailsRolesAuthorLuisa Pereira
Show author detailsRolesAuthor
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- Imports8 packages
- Suggests1 package