The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

asmbPLS: Predicting and Classifying Patient Phenotypes with Multi-Omics Data

Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.8), ggplot2, ggpubr, stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2023-04-17
DOI: 10.32614/CRAN.package.asmbPLS
Author: Runzhi Zhang [aut, cre], Susmita Datta [aut, ths]
Maintainer: Runzhi Zhang <runzhi.zhang at ufl.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: asmbPLS citation info
Materials: README
CRAN checks: asmbPLS results

Documentation:

Reference manual: asmbPLS.pdf
Vignettes: asmbPLS_tutorial

Downloads:

Package source: asmbPLS_1.0.0.tar.gz
Windows binaries: r-devel: asmbPLS_1.0.0.zip, r-release: asmbPLS_1.0.0.zip, r-oldrel: asmbPLS_1.0.0.zip
macOS binaries: r-release (arm64): asmbPLS_1.0.0.tgz, r-oldrel (arm64): asmbPLS_1.0.0.tgz, r-release (x86_64): asmbPLS_1.0.0.tgz, r-oldrel (x86_64): asmbPLS_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=asmbPLS to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.