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✨
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:

Conda:

integrationmulti-omicso2plsomicsplsdaopenblascppopenmp

4.90 score 8 stars 20 scripts 272 downloads 122 exports 72 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR190
linux-devel-x86_64ERROR139
source / vignettesOK276
linux-release-arm64ERROR150
linux-release-x86_64ERROR170
macos-release-arm64ERROR136
macos-release-x86_64ERROR220
macos-oldrel-arm64ERROR168
macos-oldrel-x86_64ERROR321
windows-develERROR429
windows-releaseERROR83
windows-oldrelERROR562
wasm-releaseOK139

Exports:apply_sparsityAtAauto_determine_keepXauto_determine_keepYcalculate_centroid_distancescalculate_classification_errorcalculate_mahalanobis_distancescalculate_max_distancescalculate_prediction_probabilitiescalculate_regression_errorcalculate_sparse_vipcolsdscolumn_sumscompare_modelscreate_stratified_foldscv_sparse_o2pls_singlecv_sparse_plsdaeigenmulteigenthreeevaluate_sparse_classificationextract_sparse_orthogonal_Xextract_sparse_orthogonal_Yfind_optimal_parametersfind_optimal_sparse_paramsfit_sparse_plsget_sparsity_info_internalgetMCCV_cppidentify_stable_variablesloadingsloadings.O2plsloadings.o2plsdaloadings.plsdao2cvo2cv_enhancedo2cv_sparseo2plsoplsoplsdaorder_cpporder_strperform_single_bootstrapplot_opt_paramsplot_s4_biplotplot_s4_comparisonplot_s4_contributionplot_s4_diagnosticplot_s4_loadingsplot_s4_scoresplot_s4_selectionplot_s4_sparsityplot_sp_classificationplot_sp_loadingsplot_sp_scoresplot_sp_selectionplot_sp_vipplot_stab_barplotplot_stab_heatmapplot_stab_networkplot_stab_summaryplot_stab_thresholdplot_tune_compareplot_tune_heatmapplot_tune_lineplot_tune_surfaceplot.O2plsplot.o2plsdaplot.plsdaplot.sparse_plsdaplot.SparseO2plsplot.stability_selectionplot.TuneResultplsdapredictpredict_o2pls_classificationpredict_o2pls_regressionpredict.sparse_plsdapredict.SparseO2plspreprocess_matrixprint.O2plsprint.plsdaprint.sparse_o2plsprint.sparse_plsdaprint.SparseO2plsQrcpp_rmses2sample_cppsample_lapplyscad_thresholdscoresscores.O2plsscores.o2plsdascores.plsdaselected_var_namesselected_var_names.sparse_o2plsselected_var_names.sparse_plsdaselected_var_names.SparseO2plsselected_varsselected_vars.defaultselected_vars.sparse_o2plsselected_vars.sparse_plsdaselected_vars.SparseO2plssort_strsparse_o2plssparse_plsdasparsity_infosparsity_info.sparse_o2plssparsity_info.sparse_plsdasparsity_info.SparseO2plssplit_strstability_selectionsummary.O2plssummary.plsdasummary.sparse_o2plssummary.sparse_plsdasummary.SparseO2plstune_sparse_keepXtune_sparse_o2plsunlist_cppvalidate_sparse_inputsvalidate_tuning_inputsvip

Dependencies:askpassbase64encbslibcachemclicpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2ggrepelgluegridExtragtablehighrhtmltoolshtmlwidgetshttrigraphisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMatrixmemoisemimeopensslotelpillarpkgconfigplotlyplyrpromisespurrrR6rappdirsRColorBrewerRcppRcppArmadilloreshape2rlangrmarkdownS7sassscalesstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Omics data integration with o2plsda

Kai Guo, University of Michigan

Rendered fromo2plsda.Rmdusingknitr::knitron May 31 2026.

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

Readme and manuals

Help Manual

Help pageTopics
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 classificationcalculate_mahalanobis_distances
Calculate prediction probabilities from distancescalculate_prediction_probabilities
Compare multiple O2PLS modelscompare_models
Extract the loadings from an O2PLS fitloadings loadings.O2pls
extract the loading value from the O2PLSDA analysisloadings.o2plsda
extract the loading value from the PLSDA analysisloadings.plsda
Cross validation for O2PLSo2cv
Enhanced sparse cross-validation that integrates with existing o2cvo2cv_sparse
fit O2PLS model with best nc, nx, nyo2pls
Class "O2pls" This class represents the Annotation informationO2pls-class
Orthogonal partial least squares discriminant analysisoplsda
Score or loading plot for the O2PLS resultsplot.O2pls
Score, VIP or loading plot for the O2PLS resultsplot.o2plsda
Score, VIP or loading plot for the plsda resultsplot.plsda
Plot method for Sparse PLS-DA resultsplot.sparse_plsda
Plot method for SparseO2pls S4 objectsplot.SparseO2pls
Enhanced plot method for stability_selection objectsplot.stability_selection
Enhanced plot method for TuneResult objectsplot.TuneResult
Partial least squares discriminant analysisplsda
Predict method for sparse_plsda objectspredict,sparse_plsda-method
Predict method for SparseO2pls objectspredict,SparseO2pls-method
Enhanced predict method for sparse PLS-DApredict.sparse_plsda
Enhanced predict method for sparse O2PLSpredict.SparseO2pls
Print the summary of O2PLS results.print.O2pls
Print the summary of plsda results.print.plsda
Print method for sparse O2PLS resultsprint.SparseO2pls
Enhanced SCAD thresholding functionscad_threshold
Extract the scores from an O2PLS fitscores
Extract the scores from an O2PLS fitscores.O2pls
Extract the scores from an O2PLS DA analysisscores.o2plsda
Extract the scores PLSDA analysisscores.plsda
Get names of selected variablesselected_var_names
Extract selected variables from sparse modelsselected_vars
Default method for selected_varsselected_vars.default
Extract selected variables from sparse PLS-DA modelsselected_vars.sparse_plsda
Extract selected variables from SparseO2pls S4 objectsselected_vars.SparseO2pls
Sparse Two-way Orthogonal Partial Least Squaressparse_o2pls
Sparse Partial Least Squares Discriminant Analysissparse_plsda
Class "SparseO2pls" This class represents sparse O2PLS analysis resultsSparseO2pls-class
Get sparsity information from sparse modelssparsity_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 Methodsstability_selection
Summary of an O2PLS objectsummary.O2pls
Summary of an plsda objectsummary.plsda
Summary of sparse PLS-DA resultssummary.sparse_plsda
Summary method for sparse O2PLS resultssummary.SparseO2pls
Cross-validation for Sparse PLS-DAtune_sparse_keepX
Cross-validation for Sparse O2PLStune_sparse_o2pls
Class "TuneResult"TuneResult-class
Extract the VIP values from the O2PLS-DA objectvip