kridsadakorn/ipcaps.bioc: Iterative Pruning to Capture Population Structure

An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. Ipcaps supports ordinal data which can be applied directly to SNP data to identify fine-level population structure. Ipcaps is built and upgraded from the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>, Limpiti et al (2011) <doi:10.1186/1471-2105-12-255> and IPCAPS from Chaichoompu et al. (2019) <doi:10.1186/s13029-019-0072-6>. In this version, Ipcaps supports not only SNP data, but also continuous data, e.g. gene expression etc. 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'.

install.packages("remotes")
remotes::install_github("kridsadakorn/ipcaps.bioc")