asmbPLS-package {asmbPLS} | R Documentation |

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.

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

Runzhi Zhang [aut, cre], Susmita Datta [aut, ths]

Maintainer: Runzhi Zhang <runzhi.zhang@ufl.edu>

add later

`asmbPLS.fit`

, `asmbPLS.cv`

, `asmbPLS.predict`

, `mbPLS.fit`

, `meanimp`

[Package *asmbPLS* version 1.0.0 Index]