Iterative Pruning to Capture Population Structure
(Internal function) Calculae a vector of EigenFit values, internally u...
(Internal function) Check whether the IPCAPS process meets the stoppin...
(Internal function) Select a clustering method to be used for the IPCA...
(Internal function) Perform the clustering process of IPCAPS
(Internal function) Calculate a vector of different values from a vect...
(Internal function) Check the different value of X and Y, internally u...
(Internal function) Perform regression models, internally used for par...
Export the IPCAPS result to a text file
Get the information for specified node
IPCAPS : Iterative Pruning to CApture Population Structure
Perform unsupervised clustering to capture population structure based ...
Synthetic dataset containing population labels for the dataset `raw.da...
(Internal object) The HTML output template for IPCAPS
(Internal function) Manipulate categorical input files
Synthetic dataset containing the top 10 principal components (PC) from...
(Internal function) Perform the post-processing step of IPCAPS
(Internal function) Perform the pre-processing step of IPCAPS
(Internal function) Perform the iterative process for each node
Synthetic dataset containing single nucleotide polymorphisms (SNP)
(Internal function) Replace missing values by specified values, intern...
Generate HTML file for EigenFit plots
Generate HTML file for clustering result in text mode
Generate HTML file for scatter plots which all data points are highlig...
Generate HTML file for scatter plots which data points are highlighted...
Workflow to generate HTML files for all kinds of plots
Detecting top discriminators between two groups
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'.