| asmbPLS-package {asmbPLS} | R Documentation |
Predicting and Classifying Patient Phenotypes with Multi-Omics Data
Description
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.
Details
The DESCRIPTION file:
| Package: | asmbPLS |
| Type: | Package |
| Title: | Predicting and Classifying Patient Phenotypes with Multi-Omics Data |
| Version: | 1.0.0 |
| Date: | 2023-04-13 |
| Authors@R: | c( person("Runzhi", "Zhang", role = c("aut", "cre"), email = "runzhi.zhang@ufl.edu"), person("Susmita", "Datta", role = c("aut", "ths"), email = "susmita.datta@ufl.edu")) |
| Description: | 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. |
| License: | GPL (>= 2) |
| Encoding: | UTF-8 |
| Depends: | R (>= 3.5.0) |
| Imports: | Rcpp (>= 1.0.8), ggplot2, ggpubr, stats |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 7.2.3 |
| LazyData: | true |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Author: | Runzhi Zhang [aut, cre], Susmita Datta [aut, ths] |
| Maintainer: | Runzhi Zhang <runzhi.zhang@ufl.edu> |
| Archs: | x64 |
Index of help topics:
asmbPLS-package Predicting and Classifying Patient Phenotypes
with Multi-Omics Data
asmbPLS.cv Cross-validation for asmbPLS to find the best
combinations of quantiles for prediction
asmbPLS.example Example data for asmbPLS algorithm
asmbPLS.fit asmbPLS for block-structured data
asmbPLS.predict Using an asmbPLS model for prediction of new
samples
asmbPLSDA.cv Cross-validation for asmbPLS-DA to find the
best combinations of quantiles for
classification
asmbPLSDA.example Example data for asmbPLS-DA algorithm
asmbPLSDA.fit asmbPLS-DA for block-structured data
asmbPLSDA.predict Using an asmbPLS-DA model for classification of
new samples
asmbPLSDA.vote.fit asmbPLS-DA vote model fit
asmbPLSDA.vote.predict
Using an asmbPLS-DA vote model for
classification of new samples
mbPLS.fit mbPLS for block-structured data
meanimp Mean imputation for the survival time
plotCor Graphical output for the asmbPLS-DA framework
plotPLS PLS plot for asmbPLS-DA
plotRelevance Relevance plot for asmbPLS-DA
quantileComb Create the quantile combination set for asmbPLS
and asmbPLS-DA
to.categorical Converts a class vector to a binary class
matrix
Author(s)
Runzhi Zhang [aut, cre], Susmita Datta [aut, ths]
Maintainer: Runzhi Zhang <runzhi.zhang@ufl.edu>
References
add later
See Also
asmbPLS.fit, asmbPLS.cv, asmbPLS.predict, mbPLS.fit, meanimp