CRAN/E | asmbPLS

asmbPLS

Predicting and Classifying Patient Phenotypes with Multi-Omics Data

Installation

About

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.

Citation asmbPLS citation info

Key Metrics

Version 1.0.0
R ≥ 3.5.0
Published 2023-04-17 347 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks asmbPLS results

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Maintainer

Maintainer

Runzhi Zhang

runzhi.zhang@ufl.edu

Authors

Runzhi Zhang

aut / cre

Susmita Datta

aut / ths

Material

README
Reference manual
Package source

Vignettes

asmbPLS_tutorial

macOS

r-devel

arm64

r-release

arm64

r-oldrel

arm64

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 3.5.0

Imports

Rcpp ≥ 1.0.8
ggplot2
ggpubr
stats

Suggests

knitr
rmarkdown

LinkingTo

Rcpp
RcppArmadillo