Package: IPCAPS 1.1.8

Kridsadakorn Chaichoompu

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'.

Authors:Kridsadakorn Chaichoompu [aut, cre], Kristel Van Steen [aut], Fentaw Abegaz [aut], Sissades Tongsima [aut], Philip Shaw [aut], Anavaj Sakuntabhai [aut], Luisa Pereira [aut]

IPCAPS_1.1.8.tar.gz
IPCAPS_1.1.8.tar.gz(r-4.5-noble)IPCAPS_1.1.8.tar.gz(r-4.4-noble)
IPCAPS_1.1.8.tgz(r-4.4-emscripten)IPCAPS_1.1.8.tgz(r-4.3-emscripten)
IPCAPS.pdf |IPCAPS.html
IPCAPS/json (API)
NEWS

# Install 'IPCAPS' in R:
install.packages('IPCAPS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • PC - Synthetic dataset containing the top 10 principal components (PC) from the dataset 'raw.data'
  • label - Synthetic dataset containing population labels for the dataset 'raw.data'
  • raw.data - Synthetic dataset containing single nucleotide polymorphisms

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 10 scripts 463 downloads 2 mentions 9 exports 24 dependencies

Last updated 4 years agofrom:38cbcceb25. Checks:OK: 1 ERROR: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-linuxERROROct 26 2024

Exports:export.groupsget.node.infoipcapssave.eigenplots.htmlsave.htmlsave.plotssave.plots.cluster.htmlsave.plots.label.htmltop.discriminator

Dependencies:apclusterclassclusterDEoptimRdiptestexpmflexmixfpckernlabKRISlatticeLPCMMASSMatrixmclustmodeltoolsnnetprabclusrARPACKRcppRcppEigenRmixmodrobustbaseRSpectra

Readme and manuals

Help Manual

Help pageTopics
(Internal function) Calculae a vector of EigenFit values, internally used for parallelizationcal.eigen.fit
(Internal function) Check whether the IPCAPS process meets the stopping criterion.check.stopping
(Internal function) Perform the clustering process of IPCAPSclustering
(Internal function) Select a clustering method to be used for the IPCAPS process.clustering.mode
(Internal function) Calculate a vector of different values from a vector of EigenFit values, internally used for parallelizationdiff.eigen.fit
(Internal function) Check the different value of X and Y, internally used for parallelizationdiff.xy
(Internal function) Perform regression models, internally used for parallelizationdo.glm
Export the IPCAPS result to a text fileexport.groups
Get the information for specified nodeget.node.info
Perform unsupervised clustering to capture population structure based on iterative pruningipcaps
Synthetic dataset containing population labels for the dataset 'raw.data'label
(Internal object) The HTML output template for IPCAPSoutput.template
(Internal function) Manipulate categorical input filespasre.categorical.data
Synthetic dataset containing the top 10 principal components (PC) from the dataset 'raw.data'PC
(Internal function) Perform the post-processing step of IPCAPSpostprocess
(Internal function) Perform the pre-processing step of IPCAPSpreprocess
(Internal function) Perform the iterative process for each nodeprocess.each.node
Synthetic dataset containing single nucleotide polymorphisms (SNP)raw.data
(Internal function) Replace missing values by specified values, internally used for parallelizationreplace.missing
Generate HTML file for EigenFit plotssave.eigenplots.html
Generate HTML file for clustering result in text modesave.html
Workflow to generate HTML files for all kinds of plotssave.plots
Generate HTML file for scatter plots which all data points are highlighted by IPCAPS clusterssave.plots.cluster.html
Generate HTML file for scatter plots which data points are highlighted by given labelssave.plots.label.html
Detecting top discriminators between two groupstop.discriminator