Package: o2plsda 0.0.28

o2plsda: Multiomics Data Integration

Provides functions to do 'O2PLS-DA' analysis for multiple omics data integration. The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" which published by Johan Trygg and Svante Wold at 2003 <doi:10.1002/cem.775>. 'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 <doi:10.1002/cem.1388>; Löfstedt et al., 2012 <doi:10.1016/j.aca.2013.06.026>) from the systematic sources of variance being specific for each data set separately.

Authors:Kai Guo [aut, cre], Junguk Hur [aut], Eva Feldman [aut]

o2plsda_0.0.28.tar.gz
o2plsda_0.0.28.zip(r-4.7)o2plsda_0.0.28.zip(r-4.6)o2plsda_0.0.28.zip(r-4.5)
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o2plsda_0.0.28.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
o2plsda/json (API)

# Install 'o2plsda' in R:

install.packages('o2plsda', repos = c('https://guokai8.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/guokai8/o2plsda/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

integration multi-omics o2pls omics plsda openblas cpp openmp

4.92 score 8 stars 21 scripts 424 downloads 122 exports 72 dependencies

Last updated from:56d3024a3c. Checks:11 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	ERROR	162
linux-devel-x86_64	ERROR	141
source / vignettes	OK	227
linux-release-arm64	ERROR	160
linux-release-x86_64	ERROR	151
macos-release-arm64	ERROR	114
macos-release-x86_64	ERROR	213
macos-oldrel-arm64	ERROR	115
macos-oldrel-x86_64	ERROR	219
windows-devel	ERROR	92
windows-release	ERROR	97
windows-oldrel	ERROR	85
wasm-release	OK	169

Exports:apply_sparsity AtA auto_determine_keepX auto_determine_keepY calculate_centroid_distances calculate_classification_error calculate_mahalanobis_distances calculate_max_distances calculate_prediction_probabilities calculate_regression_error calculate_sparse_vip colsds column_sums compare_models create_stratified_folds cv_sparse_o2pls_single cv_sparse_plsda eigenmult eigenthree evaluate_sparse_classification extract_sparse_orthogonal_X extract_sparse_orthogonal_Y find_optimal_parameters find_optimal_sparse_params fit_sparse_pls get_sparsity_info_internal getMCCV_cpp identify_stable_variables loadings loadings.O2pls loadings.o2plsda loadings.plsda o2cv o2cv_enhanced o2cv_sparse o2pls opls oplsda order_cpp order_str perform_single_bootstrap plot_opt_params plot_s4_biplot plot_s4_comparison plot_s4_contribution plot_s4_diagnostic plot_s4_loadings plot_s4_scores plot_s4_selection plot_s4_sparsity plot_sp_classification plot_sp_loadings plot_sp_scores plot_sp_selection plot_sp_vip plot_stab_barplot plot_stab_heatmap plot_stab_network plot_stab_summary plot_stab_threshold plot_tune_compare plot_tune_heatmap plot_tune_line plot_tune_surface plot.O2pls plot.o2plsda plot.plsda plot.sparse_plsda plot.SparseO2pls plot.stability_selection plot.TuneResult plsda predict predict_o2pls_classification predict_o2pls_regression predict.sparse_plsda predict.SparseO2pls preprocess_matrix print.O2pls print.plsda print.sparse_o2pls print.sparse_plsda print.SparseO2pls Q rcpp_rmse s2 sample_cpp sample_lapply scad_threshold scores scores.O2pls scores.o2plsda scores.plsda selected_var_names selected_var_names.sparse_o2pls selected_var_names.sparse_plsda selected_var_names.SparseO2pls selected_vars selected_vars.default selected_vars.sparse_o2pls selected_vars.sparse_plsda selected_vars.SparseO2pls sort_str sparse_o2pls sparse_plsda sparsity_info sparsity_info.sparse_o2pls sparsity_info.sparse_plsda sparsity_info.SparseO2pls split_str stability_selection summary.O2pls summary.plsda summary.sparse_o2pls summary.sparse_plsda summary.SparseO2pls tune_sparse_keepX tune_sparse_o2pls unlist_cpp validate_sparse_inputs validate_tuning_inputs vip

Dependencies:askpass base64enc bslib cachem cli cpp11 crosstalk curl data.table digest dplyr evaluate farver fastmap fontawesome fs generics ggplot2 ggrepel glue gridExtra gtable highr htmltools htmlwidgets httr igraph isoband jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle magrittr Matrix memoise mime openssl otel pillar pkgconfig plotly plyr promises purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo reshape2 rlang rmarkdown S7 sass scales stringi stringr sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml

Omics data integration with o2plsda

Rendered fromo2plsda.Rmdusingknitr::knitr

Last update: 2025-12-02
Started: 2025-12-02

Help page	Topics
Extract elements from sparse_plsda objects	$,sparse_plsda-method
Extract elements from SparseO2pls objects	$,SparseO2pls-method
Enhanced sparsity application function (FIXED VERSION)	apply_sparsity
Calculate Mahalanobis distances for classification	calculate_mahalanobis_distances
Calculate prediction probabilities from distances	calculate_prediction_probabilities
Compare multiple O2PLS models	compare_models
Extract the loadings from an O2PLS fit	loadings loadings.O2pls
extract the loading value from the O2PLSDA analysis	loadings.o2plsda
extract the loading value from the PLSDA analysis	loadings.plsda
Cross validation for O2PLS	o2cv
Enhanced sparse cross-validation that integrates with existing o2cv	o2cv_sparse
fit O2PLS model with best nc, nx, ny	o2pls
Class "O2pls" This class represents the Annotation information	O2pls-class
Orthogonal partial least squares discriminant analysis	oplsda
Score or loading plot for the O2PLS results	plot.O2pls
Score, VIP or loading plot for the O2PLS results	plot.o2plsda
Score, VIP or loading plot for the plsda results	plot.plsda
Plot method for Sparse PLS-DA results	plot.sparse_plsda
Plot method for SparseO2pls S4 objects	plot.SparseO2pls
Enhanced plot method for stability_selection objects	plot.stability_selection
Enhanced plot method for TuneResult objects	plot.TuneResult
Partial least squares discriminant analysis	plsda
Predict method for sparse_plsda objects	predict,sparse_plsda-method
Predict method for SparseO2pls objects	predict,SparseO2pls-method
Enhanced predict method for sparse PLS-DA	predict.sparse_plsda
Enhanced predict method for sparse O2PLS	predict.SparseO2pls
Print the summary of O2PLS results.	print.O2pls
Print the summary of plsda results.	print.plsda
Print method for sparse O2PLS results	print.SparseO2pls
Enhanced SCAD thresholding function	scad_threshold
Extract the scores from an O2PLS fit	scores
Extract the scores from an O2PLS fit	scores.O2pls
Extract the scores from an O2PLS DA analysis	scores.o2plsda
Extract the scores PLSDA analysis	scores.plsda
Get names of selected variables	selected_var_names
Extract selected variables from sparse models	selected_vars
Default method for selected_vars	selected_vars.default
Extract selected variables from sparse PLS-DA models	selected_vars.sparse_plsda
Extract selected variables from SparseO2pls S4 objects	selected_vars.SparseO2pls
Sparse Two-way Orthogonal Partial Least Squares	sparse_o2pls
Sparse Partial Least Squares Discriminant Analysis	sparse_plsda
Class "SparseO2pls" This class represents sparse O2PLS analysis results	SparseO2pls-class
Get sparsity information from sparse models	sparsity_info sparsity_info,SparseO2pls-method sparsity_info,sparse_plsda-method sparsity_info.SparseO2pls sparsity_info.sparse_o2pls sparsity_info.sparse_plsda
Stability Selection for Sparse Methods	stability_selection
Summary of an O2PLS object	summary.O2pls
Summary of an plsda object	summary.plsda
Summary of sparse PLS-DA results	summary.sparse_plsda
Summary method for sparse O2PLS results	summary.SparseO2pls
Cross-validation for Sparse PLS-DA	tune_sparse_keepX
Cross-validation for Sparse O2PLS	tune_sparse_o2pls
Class "TuneResult"	TuneResult-class
Extract the VIP values from the O2PLS-DA object	vip

Package: o2plsda 0.0.28

o2plsda: Multiomics Data Integration

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