{ "_id": "6a1b004b1d7bb097a09ff4c8", "Package": "structToolbox", "Type": "Package", "Title": "Data processing & analysis tools for Metabolomics and other\nomics", "Version": "1.25.0", "Authors@R": "c(\nperson(\nc(\"Gavin\",\"Rhys\"),\n\"Lloyd\",\nrole=c(\"aut\",\"cre\"),\nemail=\"g.r.lloyd@bham.ac.uk\",\ncomment = c(ORCID = \"0000-0001-7989-6695\")\n),\nperson(\nc(\"Ralf\",\"Johannes\", \"Maria\"),\n\"Weber\",\nrole=c(\"aut\"),\nemail=\"r.j.weber@bham.ac.uk\")\n)", "Description": "An extensive set of data (pre-)processing and analysis\nmethods and tools for metabolomics and other omics, with a\nstrong emphasis on statistics and machine learning. This\ntoolbox allows the user to build extensive and standardised\nworkflows for data analysis. The methods and tools have been\nimplemented using class-based templates provided by the struct\n(Statistics in R Using Class-based Templates) package. The\ntoolbox includes pre-processing methods (e.g. signal drift and\nbatch correction, normalisation, missing value imputation and\nscaling), univariate (e.g. ttest, various forms of ANOVA,\nKruskal–Wallis test and more) and multivariate statistical\nmethods (e.g. PCA and PLS, including cross-validation and\npermutation testing) as well as machine learning methods (e.g.\nSupport Vector Machines). Ontology terms have been integrated\nto provide standardised definitions for the different methods,\ninputs and outputs.", "License": "GPL-3", "Encoding": "UTF-8", "Collate": "'AUC_metric_class.R' 'entity_objects.R' 'DFA_class.R' 'zzz.R'\n'anova_class.R' 'HSD_class.R' 'mixed_effect_class.R'\n'HSDEM_class.R' 'MTBLS79_dataset_class.R' 'PCA_class.R'\n'scatter_chart_class.R' 'PCA_plotfcns.R' 'PLSR_class.R'\n'PLSDA_class.R' 'PLSDA_charts.R' 'as_data_frame_doc.R'\n'autoscale_class.R' 'balanced_accuracy_class.R'\n'blank_filter_class.R' 'bootstrap_class.R' 'calculate_doc.R'\n'chart_plot_doc.R' 'classical_lsq_class.R'\n'confounders_clsq_class.R' 'constant_sum_norm_class.R'\n'corr_coef_class.R' 'd_ratio_filter_class.R'\n'dataset_chart_classes.R' 'split_data_class.R'\n'equal_split_class.R' 'factor_barchart_class.R'\n'feature_plot_array_class.R' 'feature_profile_class.R'\n'filter_by_name_class.R' 'filter_na_count.R'\n'filter_smeta_class.R' 'fisher_exact_class.R'\n'fold_change_class.R' 'fold_change_int_class.R'\n'forward_selection_by_rank_class.R' 'ggplot_theme_pub.R'\n'glog_class.R' 'grid_search_1d_class.R' 'hca_class.R'\n'kfold_xval_class.R' 'kfold_xval_charts.R' 'knn_impute_class.R'\n'kw_rank_sum_class.R' 'linear_model_class.R' 'log_transform.R'\n'mean_centre_class.R' 'mean_of_medians.R' 'model_apply_doc.R'\n'model_predict_doc.R' 'model_reverse_doc.R' 'model_train_doc.R'\n'mv_feature_filter_class.R' 'mv_sample_filter_class.R'\n'nroot_transform_class.R' 'oplsr_class.R' 'oplsda_class.R'\n'pairs_filter_class.R' 'paretoscale_class.R'\n'permutation_test_class.R' 'permute_sample_order_class.R'\n'plsda_feature_significance_chart.R' 'pqn_norm_method_class.R'\n'prop_na_class.R' 'r_squared_class.R' 'resample_class.R'\n'rsd_filter.R' 'run_doc.R' 'sb_corr.R'\n'stratified_split_class.R' 'structToolbox.R'\n'svm_classifier_class.R' 'tSNE_class.R' 'tic_chart_class.R'\n'ttest_class.R' 'vec_norm_class.R' 'wilcox_test_class.R'", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "biocViews": "WorkflowStep, Metabolomics", "URL": "https://github.com/computational-metabolomics/structToolbox,\nhttps://computational-metabolomics.github.io/structToolbox/", "Roxygen": "list(markdown = TRUE)", "Config/Needs/website": "rmarkdown", "Config/pak/sysreqs": "libicu-dev libssl-dev zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:51:44 UTC", "RemoteUrl": "https://github.com/bioc/structToolbox", "RemoteRef": "HEAD", "RemoteSha": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-30 09:53:22 UTC", "User": "root" }, "Author": "Gavin Rhys Lloyd [aut, cre] (ORCID:\n),\nRalf Johannes Maria Weber [aut]", "Maintainer": "Gavin Rhys Lloyd ", "MD5sum": "a6c2709e709b7d72dd7747432fc3f5c8", "_user": "bioc", "_type": "src", "_file": "structToolbox_1.25.0.tar.gz", "_fileid": "83abf9c6278d2788e0ff2783685e3b63ed8c691b6d548e8dcbcba72d8bac4c73", "_filesize": 5140489, "_sha256": "83abf9c6278d2788e0ff2783685e3b63ed8c691b6d548e8dcbcba72d8bac4c73", "_created": "2026-05-30T09:53:22.000Z", "_published": "2026-05-30T15:20:43.544Z", "_jobs": [ { "job": 78640961225, "time": 225, "config": "bioc-checks", "r": "4.6.0", "check": "WARNING", "artifact": "7307585786" }, { "job": 78640961240, "time": 575, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "7307620790" }, { "job": 78640961231, "time": 598, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "7307622870" }, { "job": 78640961235, "time": 385, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "7309195357" }, { "job": 78640961229, "time": 399, "config": "macos-release-arm64", "r": "4.6.0", "check": "NOTE", "artifact": "7309197179" }, { "job": 78639886057, "time": 711, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7307330220" }, { "job": 78640961227, "time": 149, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7307577720" }, { "job": 78640961233, "time": 409, "config": "windows-devel", "r": "4.7.0", "check": "ERROR", "artifact": "7307602770" }, { "job": 78640961238, "time": 472, "config": "windows-oldrel", "r": "4.5.3", "check": "NOTE", "artifact": "7307609938" }, { "job": 78640961226, "time": 527, "config": "windows-release", "r": "4.6.0", "check": "NOTE", "artifact": "7307613642" } ], "_bioccheck": { "error": 0, "warning": 1, "note": 11 }, "_buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/bioc/structToolbox", "_commit": { "id": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "author": "A Wokaty ", "committer": "A Wokaty ", "message": "bump x.y.z version to odd y following creation of RELEASE_3_23 branch\n", "time": 1777380704 }, "_maintainer": { "name": "Gavin Rhys Lloyd", "email": "g.r.lloyd@bham.ac.uk", "login": "grlloyd", "orcid": "0000-0001-7989-6695", "description": "Experimental Officer in Biostatistics, Phenome Centre Birmingham", "uuid": 12222440 }, "_distro": "noble", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.0", "role": "Depends" }, { "package": "struct", "version": ">= 1.5.1", "role": "Depends" }, { "package": "ggplot2", "role": "Imports" }, { "package": "ggthemes", "role": "Imports" }, { "package": "grid", "role": "Imports" }, { "package": "gridExtra", "role": "Imports" }, { "package": "httr", "role": "Imports" }, { "package": "jsonlite", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "sp", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "limma", "role": "Imports" }, { "package": "agricolae", "role": "Suggests" }, { "package": "BiocFileCache", "role": "Suggests" }, { "package": "BiocStyle", "role": "Suggests" }, { "package": "car", "role": "Suggests" }, { "package": "covr", "role": "Suggests" }, { "package": "cowplot", "role": "Suggests" }, { "package": "e1071", "role": "Suggests" }, { "package": "emmeans", "role": "Suggests" }, { "package": "ggdendro", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "magick", "role": "Suggests" }, { "package": "nlme", "role": "Suggests" }, { "package": "openxlsx", "role": "Suggests" }, { "package": "pls", "role": "Suggests" }, { "package": "pmp", "role": "Suggests" }, { "package": "reshape2", "role": "Suggests" }, { "package": "ropls", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "Rtsne", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "rappdirs", "role": "Suggests" } ], "_owner": "bioc", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-25", "n": 4 }, { "week": "2026-08", "n": 10 }, { "week": "2026-16", "n": 2 }, { "week": "2026-18", "n": 2 } ], "_tags": [], "_bioc": [ { "branch": "devel", "version": "1.25.0", "bioc": "3.24" }, { "branch": "release", "version": "1.24.0", "bioc": "3.23" } ], "_topics": [ "workflowstep", "metabolomics", "bioconductor-package", "dims", "lc-ms", "machine-learning", "multivariate-analysis", "statistics", "univariate" ], "_stars": 11, "_contributors": [ { "user": "grlloyd", "count": 470, "uuid": 12222440 }, { "user": "rjmw", "count": 21, "uuid": 7114586 }, { "user": "jwokaty", "count": 14, "uuid": 1744257 }, { "user": "nturaga", "count": 10, "uuid": 2746443 } ], "_userbio": { "uuid": 2286807, "type": "organization", "name": "Bioconductor", "description": "Software for the analysis and comprehension of high-throughput genomic data" }, "_downloads": { "count": 474, "source": "https://www.bioconductor.org/packages/stats/bioc/structToolbox" }, "_mentions": 2, "_devurl": "https://github.com/computational-metabolomics/structtoolbox", "_pkgdown": "https://computational-metabolomics.github.io/structToolbox/", "_searchresults": 28, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/structToolbox.html", "manual.pdf" ], "_homeurl": "https://github.com/computational-metabolomics/structtoolbox", "_realowner": "bioc", "_cranurl": false, "_exports": [ "ANOVA", "as_data_frame", "AUC", "autoscale", "balanced_accuracy", "balanced_error", "blank_filter", "blank_filter_hist", "bootstrap", "calculate", "chart_plot", "classical_lsq", "compare_dist", "confounders_clsq", "confounders_lsq_barchart", "confounders_lsq_boxplot", "constant_sum_norm", "corr_coef", "DatasetExperiment_boxplot", "DatasetExperiment_dist", "DatasetExperiment_factor_boxplot", "DatasetExperiment_heatmap", "DFA", "dfa_scores_plot", "dratio_filter", "equal_split", "feature_boxplot", "feature_profile", "feature_profile_array", "filter_by_name", "filter_na_count", "filter_smeta", "fisher_exact", "fold_change", "fold_change_int", "fold_change_plot", "forward_selection_by_rank", "fs_line", "glog_opt_plot", "glog_transform", "grid_search_1d", "gs_line", "HCA", "hca_dendrogram", "HSD", "HSDEM", "kfold_xval", "kfoldxcv_grid", "kfoldxcv_metric", "knn_impute", "kw_p_hist", "kw_rank_sum", "linear_model", "log_transform", "mean_centre", "mean_of_medians", "mixed_effect", "model_apply", "model_predict", "model_reverse", "model_train", "MTBLS79_DatasetExperiment", "mv_boxplot", "mv_feature_filter", "mv_feature_filter_hist", "mv_histogram", "mv_sample_filter", "mv_sample_filter_hist", "nroot_transform", "ontology_cache", "OPLSDA", "OPLSR", "pairs_filter", "pareto_scale", "PCA", "pca_biplot", "pca_correlation_plot", "pca_dstat_plot", "pca_loadings_plot", "pca_scores_plot", "pca_scree_plot", "permutation_test", "permutation_test_plot", "permute_sample_order", "pls_regcoeff_plot", "pls_scores_plot", "pls_vip_plot", "PLSDA", "plsda_feature_importance_plot", "plsda_predicted_plot", "plsda_roc_plot", "plsda_scores_plot", "PLSR", "plsr_cook_dist", "plsr_prediction_plot", "plsr_qq_plot", "plsr_residual_hist", "pqn_norm", "pqn_norm_hist", "prop_na", "r_squared", "resample", "resample_chart", "rsd_filter", "rsd_filter_hist", "run", "sb_corr", "scatter_chart", "split_data", "stratified_split", "SVM", "svm_plot_2d", "tic_chart", "tSNE", "tSNE_scatter", "ttest", "vec_norm", "wilcox_p_hist", "wilcox_test" ], "_help": [ { "page": "ANOVA", "title": "Analysis of Variance", "topics": [ "ANOVA" ] }, { "page": "as_data_frame", "title": "Convert to data.frame", "topics": [ "as_data_frame", "as_data_frame,filter_na_count-method", "as_data_frame,ttest-method", "as_data_frame,wilcox_test-method" ] }, { "page": "AUC", "title": "Area under ROC curve", "topics": [ "AUC" ] }, { "page": "autoscale", "title": "Autoscaling", "topics": [ "autoscale" ] }, { "page": "balanced_accuracy", "title": "Balanced Accuracy", "topics": [ "balanced_accuracy" ] }, { "page": "balanced_error", "title": "Balanced error", "topics": [ "balanced_error" ] }, { "page": "blank_filter", "title": "Blank filter", "topics": [ "blank_filter" ] }, { "page": "blank_filter_hist", "title": "Histogram of blank filter fold changes", "topics": [ "blank_filter_hist" ] }, { "page": "bootstrap", "title": "Bootstrap resampling", "topics": [ "bootstrap" ] }, { "page": "calculate", "title": "Calculate metric", "topics": [ "calculate", "calculate,AUC-method", "calculate,balanced_accuracy-method", "calculate,balanced_error-method", "calculate,r_squared-method" ] }, { "page": "chart_plot", "title": "chart_plot method", "topics": [ "chart_plot", "chart_plot,blank_filter_hist,blank_filter-method", "chart_plot,compare_dist,DatasetExperiment-method", "chart_plot,confounders_lsq_barchart,confounders_clsq-method", "chart_plot,confounders_lsq_boxplot,confounders_clsq-method", "chart_plot,DatasetExperiment_boxplot,DatasetExperiment-method", "chart_plot,DatasetExperiment_dist,DatasetExperiment-method", "chart_plot,DatasetExperiment_factor_boxplot,DatasetExperiment-method", "chart_plot,DatasetExperiment_heatmap,DatasetExperiment-method", "chart_plot,dfa_scores_plot,DFA-method", "chart_plot,feature_boxplot,DatasetExperiment-method", "chart_plot,feature_profile,DatasetExperiment-method", "chart_plot,feature_profile,sb_corr-method", "chart_plot,feature_profile_array,DatasetExperiment-method", "chart_plot,fold_change_plot,fold_change-method", "chart_plot,fs_line,forward_selection_by_rank-method", "chart_plot,glog_opt_plot,glog_transform-method", "chart_plot,gs_line,grid_search_1d-method", "chart_plot,hca_dendrogram,HCA-method", "chart_plot,kfoldxcv_grid,kfold_xval-method", "chart_plot,kfoldxcv_metric,kfold_xval-method", "chart_plot,kw_p_hist,kw_rank_sum-method", "chart_plot,mv_boxplot,DatasetExperiment-method", "chart_plot,mv_feature_filter_hist,mv_feature_filter-method", "chart_plot,mv_histogram,DatasetExperiment-method", "chart_plot,mv_sample_filter_hist,mv_sample_filter-method", "chart_plot,pca_biplot,PCA-method", "chart_plot,pca_correlation_plot,PCA-method", "chart_plot,pca_dstat_plot,PCA-method", "chart_plot,pca_loadings_plot,PCA-method", "chart_plot,pca_scores_plot,PCA-method", "chart_plot,pca_scree_plot,PCA-method", "chart_plot,permutation_test_plot,permutation_test-method", "chart_plot,plsda_feature_importance_plot,PLSDA-method", "chart_plot,plsda_predicted_plot,PLSDA-method", "chart_plot,plsda_roc_plot,PLSDA-method", "chart_plot,plsr_cook_dist,PLSR-method", "chart_plot,plsr_prediction_plot,PLSR-method", "chart_plot,plsr_qq_plot,PLSR-method", "chart_plot,plsr_residual_hist,PLSR-method", "chart_plot,pls_regcoeff_plot,PLSR-method", "chart_plot,pls_scores_plot,PLSR-method", "chart_plot,pls_vip_plot,PLSR-method", "chart_plot,pqn_norm_hist,pqn_norm-method", "chart_plot,resample_chart,resample-method", "chart_plot,rsd_filter_hist,rsd_filter-method", "chart_plot,scatter_chart,DatasetExperiment-method", "chart_plot,svm_plot_2d,SVM-method", "chart_plot,tic_chart,DatasetExperiment-method", "chart_plot,tSNE_scatter,tSNE-method", "chart_plot,wilcox_p_hist,wilcox_test-method" ] }, { "page": "classical_lsq", "title": "Univariate Classical Least Squares Regression", "topics": [ "classical_lsq" ] }, { "page": "compare_dist", "title": "Compare distributions", "topics": [ "compare_dist" ] }, { "page": "confounders_clsq", "title": "Check for confounding factors", "topics": [ "confounders_clsq" ] }, { "page": "confounders_lsq_barchart", "title": "Confounding factor relative change barchart", "topics": [ "confounders_lsq_barchart" ] }, { "page": "confounders_lsq_boxplot", "title": "Confounding factor relative change boxplot", "topics": [ "confounders_lsq_boxplot" ] }, { "page": "constant_sum_norm", "title": "Normalisation to constant sum", "topics": [ "constant_sum_norm" ] }, { "page": "corr_coef", "title": "Correlation coefficient", "topics": [ "corr_coef" ] }, { "page": "DatasetExperiment_boxplot", "title": "Feature distribution histogram", "topics": [ "DatasetExperiment_boxplot" ] }, { "page": "DatasetExperiment_dist", "title": "Feature distribution histogram", "topics": [ "DatasetExperiment_dist" ] }, { "page": "DatasetExperiment_factor_boxplot", "title": "Factor boxplot", "topics": [ "DatasetExperiment_factor_boxplot" ] }, { "page": "DatasetExperiment_heatmap", "title": "DatasetExperiment heatmap", "topics": [ "DatasetExperiment_heatmap" ] }, { "page": "DFA", "title": "Discriminant Factor Analysis", "topics": [ "DFA" ] }, { "page": "dfa_scores_plot", "title": "DFA scores plot", "topics": [ "dfa_scores_plot" ] }, { "page": "dratio_filter", "title": "Dispersion ratio filter", "topics": [ "dratio_filter" ] }, { "page": "equal_split", "title": "Equal group sized sampling", "topics": [ "equal_split" ] }, { "page": "feature_boxplot", "title": "Feature boxplot", "topics": [ "feature_boxplot" ] }, { "page": "feature_profile", "title": "Feature profile", "topics": [ "feature_profile" ] }, { "page": "feature_profile_array", "title": "Feature profile", "topics": [ "feature_profile_array" ] }, { "page": "filter_by_name", "title": "Filter by name", "topics": [ "filter_by_name" ] }, { "page": "filter_na_count", "title": "Minimum number of measured values filter", "topics": [ "filter_na_count" ] }, { "page": "filter_smeta", "title": "Filter by sample meta data", "topics": [ "filter_smeta" ] }, { "page": "fisher_exact", "title": "Fisher Exact Test", "topics": [ "fisher_exact" ] }, { "page": "fold_change", "title": "Fold change", "topics": [ "fold_change" ] }, { "page": "fold_change_int", "title": "Fold change for interactions between factors", "topics": [ "fold_change_int" ] }, { "page": "fold_change_plot", "title": "Fold change plot", "topics": [ "fold_change_plot" ] }, { "page": "forward_selection_by_rank", "title": "Forward selection by rank", "topics": [ "forward_selection_by_rank" ] }, { "page": "fs_line", "title": "Forward selection line plot", "topics": [ "fs_line" ] }, { "page": "glog_opt_plot", "title": "Glog optimisation", "topics": [ "glog_opt_plot" ] }, { "page": "glog_transform", "title": "Generalised logarithmic transform", "topics": [ "glog_transform" ] }, { "page": "grid_search_1d", "title": "One dimensional grid search", "topics": [ "grid_search_1d" ] }, { "page": "gs_line", "title": "Grid search line plot", "topics": [ "gs_line" ] }, { "page": "HCA", "title": "Hierarchical Cluster Analysis", "topics": [ "HCA" ] }, { "page": "hca_dendrogram", "title": "HCA dendrogram", "topics": [ "hca_dendrogram" ] }, { "page": "HSD", "title": "Tukey's Honest Significant Difference", "topics": [ "HSD" ] }, { "page": "HSDEM", "title": "Tukey's Honest Significant Difference using estimated marginal means", "topics": [ "HSDEM" ] }, { "page": "kfold_xval", "title": "k-fold cross-validation", "topics": [ "kfold_xval" ] }, { "page": "kfoldxcv_grid", "title": "k-fold cross validation plot", "topics": [ "kfoldxcv_grid" ] }, { "page": "kfoldxcv_metric", "title": "kfoldxcv metric plot", "topics": [ "kfoldxcv_metric" ] }, { "page": "knn_impute", "title": "kNN missing value imputation", "topics": [ "knn_impute" ] }, { "page": "kw_p_hist", "title": "Histogram of p values", "topics": [ "kw_p_hist" ] }, { "page": "kw_rank_sum", "title": "Kruskal-Wallis rank sum test", "topics": [ "kw_rank_sum" ] }, { "page": "linear_model", "title": "Linear model", "topics": [ "linear_model" ] }, { "page": "log_transform", "title": "logarithm transform", "topics": [ "log_transform" ] }, { "page": "mean_centre", "title": "Mean centre", "topics": [ "mean_centre" ] }, { "page": "mean_of_medians", "title": "Mean of medians", "topics": [ "mean_of_medians" ] }, { "page": "mixed_effect", "title": "Mixed effects model", "topics": [ "mixed_effect" ] }, { "page": "model_apply", "title": "Apply method", "topics": [ "model_apply", "model_apply,ANOVA,DatasetExperiment-method", "model_apply,classical_lsq,DatasetExperiment-method", "model_apply,confounders_clsq,DatasetExperiment-method", "model_apply,constant_sum_norm,DatasetExperiment-method", "model_apply,corr_coef,DatasetExperiment-method", "model_apply,equal_split,DatasetExperiment-method", "model_apply,filter_smeta,DatasetExperiment-method", "model_apply,fisher_exact,DatasetExperiment-method", "model_apply,fold_change,DatasetExperiment-method", "model_apply,fold_change_int,DatasetExperiment-method", "model_apply,HCA,DatasetExperiment-method", "model_apply,HSD,DatasetExperiment-method", "model_apply,HSDEM,DatasetExperiment-method", "model_apply,knn_impute,DatasetExperiment-method", "model_apply,kw_rank_sum,DatasetExperiment-method", "model_apply,log_transform,DatasetExperiment-method", "model_apply,mean_of_medians,DatasetExperiment-method", "model_apply,mixed_effect,DatasetExperiment-method", "model_apply,nroot_transform,DatasetExperiment-method", "model_apply,pairs_filter,DatasetExperiment-method", "model_apply,prop_na,DatasetExperiment-method", "model_apply,rsd_filter,DatasetExperiment-method", "model_apply,sb_corr,DatasetExperiment-method", "model_apply,split_data,DatasetExperiment-method", "model_apply,stratified_split,DatasetExperiment-method", "model_apply,tSNE,DatasetExperiment-method", "model_apply,ttest,DatasetExperiment-method", "model_apply,vec_norm,DatasetExperiment-method", "model_apply,wilcox_test,DatasetExperiment-method" ] }, { "page": "model_predict", "title": "Model prediction", "topics": [ "model_predict", "model_predict,autoscale,DatasetExperiment-method", "model_predict,blank_filter,DatasetExperiment-method", "model_predict,constant_sum_norm,DatasetExperiment-method", "model_predict,DFA,DatasetExperiment-method", "model_predict,dratio_filter,DatasetExperiment-method", "model_predict,filter_by_name,DatasetExperiment-method", "model_predict,filter_na_count,DatasetExperiment-method", "model_predict,filter_smeta,DatasetExperiment-method", "model_predict,glog_transform,DatasetExperiment-method", "model_predict,linear_model,DatasetExperiment-method", "model_predict,mean_centre,DatasetExperiment-method", "model_predict,mv_feature_filter,DatasetExperiment-method", "model_predict,mv_sample_filter,DatasetExperiment-method", "model_predict,OPLSDA,DatasetExperiment-method", "model_predict,OPLSR,DatasetExperiment-method", "model_predict,pareto_scale,DatasetExperiment-method", "model_predict,PCA,DatasetExperiment-method", "model_predict,PLSDA,DatasetExperiment-method", "model_predict,PLSR,DatasetExperiment-method", "model_predict,pqn_norm,DatasetExperiment-method", "model_predict,SVM,DatasetExperiment-method", "model_predict,vec_norm,DatasetExperiment-method" ] }, { "page": "model_reverse", "title": "Reverse preprocessing", "topics": [ "model_reverse", "model_reverse,autoscale,DatasetExperiment-method", "model_reverse,mean_centre,DatasetExperiment-method" ] }, { "page": "model_train", "title": "Train a model", "topics": [ "model_train", "model_train,autoscale,DatasetExperiment-method", "model_train,blank_filter,DatasetExperiment-method", "model_train,constant_sum_norm,DatasetExperiment-method", "model_train,DFA,DatasetExperiment-method", "model_train,dratio_filter,DatasetExperiment-method", "model_train,filter_by_name,DatasetExperiment-method", "model_train,filter_na_count,DatasetExperiment-method", "model_train,filter_smeta,DatasetExperiment-method", "model_train,glog_transform,DatasetExperiment-method", "model_train,linear_model,DatasetExperiment-method", "model_train,mean_centre,DatasetExperiment-method", "model_train,mv_feature_filter,DatasetExperiment-method", "model_train,mv_sample_filter,DatasetExperiment-method", "model_train,OPLSDA,DatasetExperiment-method", "model_train,OPLSR,DatasetExperiment-method", "model_train,pareto_scale,DatasetExperiment-method", "model_train,PCA,DatasetExperiment-method", "model_train,PLSDA,DatasetExperiment-method", "model_train,PLSR,DatasetExperiment-method", "model_train,pqn_norm,DatasetExperiment-method", "model_train,SVM,DatasetExperiment-method", "model_train,vec_norm,DatasetExperiment-method" ] }, { "page": "MTBLS79_DatasetExperiment", "title": "MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control", "topics": [ "MTBLS79_DatasetExperiment" ] }, { "page": "mv_boxplot", "title": "Missing value boxplots", "topics": [ "mv_boxplot" ] }, { "page": "mv_feature_filter", "title": "Filter features by missing values", "topics": [ "mv_feature_filter" ] }, { "page": "mv_feature_filter_hist", "title": "Histogram of missing values per feature", "topics": [ "mv_feature_filter_hist" ] }, { "page": "mv_histogram", "title": "Missing value histogram", "topics": [ "mv_histogram" ] }, { "page": "mv_sample_filter", "title": "Missing value sample filter", "topics": [ "mv_sample_filter" ] }, { "page": "mv_sample_filter_hist", "title": "Histogram of missing values per sample", "topics": [ "mv_sample_filter_hist" ] }, { "page": "nroot_transform", "title": "nth root transform", "topics": [ "nroot_transform" ] }, { "page": "ontology_cache", "title": "ontology cache", "topics": [ "ontology_cache" ] }, { "page": "OPLSDA", "title": "Orthogonal Partial Least Squares regression", "topics": [ "OPLSDA" ] }, { "page": "OPLSR", "title": "Orthogonal Partial Least Squares regression", "topics": [ "OPLSR" ] }, { "page": "pairs_filter", "title": "Pairs filter", "topics": [ "pairs_filter" ] }, { "page": "pareto_scale", "title": "Pareto scaling", "topics": [ "pareto_scale" ] }, { "page": "PCA", "title": "Principal Component Analysis (PCA)", "topics": [ "PCA" ] }, { "page": "pca_biplot", "title": "PCA biplot", "topics": [ "pca_biplot" ] }, { "page": "pca_correlation_plot", "title": "PCA correlation plot", "topics": [ "pca_correlation_plot" ] }, { "page": "pca_dstat_plot", "title": "d-statistic plot", "topics": [ "pca_dstat_plot" ] }, { "page": "pca_loadings_plot", "title": "PCA loadings plot", "topics": [ "pca_loadings_plot" ] }, { "page": "pca_scores_plot", "title": "PCA scores plot", "topics": [ "pca_scores_plot" ] }, { "page": "pca_scree_plot", "title": "Scree plot", "topics": [ "pca_scree_plot" ] }, { "page": "permutation_test", "title": "Permutation test", "topics": [ "permutation_test" ] }, { "page": "permutation_test_plot", "title": "permutation_test_plot class", "topics": [ "permutation_test_plot" ] }, { "page": "permute_sample_order", "title": "Permute Sample Order", "topics": [ "permute_sample_order" ] }, { "page": "pls_regcoeff_plot", "title": "pls_regcoeff_plot class", "topics": [ "pls_regcoeff_plot" ] }, { "page": "pls_scores_plot", "title": "PLSDA scores plot", "topics": [ "plsda_scores_plot", "pls_scores_plot", "pls_scores_plot," ] }, { "page": "pls_vip_plot", "title": "PLSDA VIP plot", "topics": [ "pls_vip_plot" ] }, { "page": "PLSDA", "title": "Partial least squares discriminant analysis", "topics": [ "PLSDA" ] }, { "page": "plsda_feature_importance_plot", "title": "PLSDA feature importance summary plot", "topics": [ "plsda_feature_importance_plot" ] }, { "page": "plsda_predicted_plot", "title": "PLSDA predicted plot", "topics": [ "plsda_predicted_plot" ] }, { "page": "plsda_roc_plot", "title": "PLSDA ROC plot", "topics": [ "plsda_roc_plot" ] }, { "page": "PLSR", "title": "Partial least squares regression", "topics": [ "PLSR" ] }, { "page": "plsr_cook_dist", "title": "Cook's distance barchart", "topics": [ "plsr_cook_dist" ] }, { "page": "plsr_prediction_plot", "title": "PLSR prediction plot", "topics": [ "plsr_prediction_plot" ] }, { "page": "plsr_qq_plot", "title": "PLSR QQ plot", "topics": [ "plsr_qq_plot" ] }, { "page": "plsr_residual_hist", "title": "PLSR residuals histogram", "topics": [ "plsr_residual_hist" ] }, { "page": "pqn_norm", "title": "Probabilistic Quotient Normalisation (PQN)", "topics": [ "pqn_norm" ] }, { "page": "pqn_norm_hist", "title": "PQN coefficient histogram", "topics": [ "pqn_norm_hist" ] }, { "page": "prop_na", "title": "Fisher's exact test for missing values", "topics": [ "prop_na" ] }, { "page": "r_squared", "title": "Coefficient of determination (R-squared)", "topics": [ "r_squared" ] }, { "page": "resample", "title": "Data resampling", "topics": [ "resample" ] }, { "page": "resample_chart", "title": "resample_chart class", "topics": [ "resample_chart" ] }, { "page": "rsd_filter", "title": "RSD filter", "topics": [ "rsd_filter" ] }, { "page": "rsd_filter_hist", "title": "RSD histogram", "topics": [ "rsd_filter_hist" ] }, { "page": "run", "title": "Runs an iterator, applying the chosen model multiple times.", "topics": [ "run", "run,bootstrap,DatasetExperiment,metric-method", "run,forward_selection_by_rank,DatasetExperiment,metric-method", "run,grid_search_1d,DatasetExperiment,metric-method", "run,kfold_xval,DatasetExperiment,metric-method", "run,permutation_test,DatasetExperiment,metric-method", "run,permute_sample_order,DatasetExperiment,metric-method", "run,resample,DatasetExperiment,metric-method" ] }, { "page": "sb_corr", "title": "Signal/batch correction for mass spectrometry data", "topics": [ "sb_corr" ] }, { "page": "scatter_chart", "title": "Group scatter chart", "topics": [ "scatter_chart" ] }, { "page": "split_data", "title": "Split data", "topics": [ "split_data" ] }, { "page": "stratified_split", "title": "Stratified sampling", "topics": [ "stratified_split" ] }, { "page": "SVM", "title": "Support Vector Machine Classifier", "topics": [ "SVM" ] }, { "page": "svm_plot_2d", "title": "SVM scatter plot", "topics": [ "svm_plot_2d" ] }, { "page": "tic_chart", "title": "Total Ion Count chart.", "topics": [ "tic_chart" ] }, { "page": "tSNE", "title": "tSNE", "topics": [ "tSNE" ] }, { "page": "tSNE_scatter", "title": "Feature boxplot", "topics": [ "tSNE_scatter" ] }, { "page": "ttest", "title": "t-test", "topics": [ "ttest" ] }, { "page": "vec_norm", "title": "Vector normalisation", "topics": [ "vec_norm" ] }, { "page": "wilcox_p_hist", "title": "Histogram of p values", "topics": [ "wilcox_p_hist" ] }, { "page": "wilcox_test", "title": "wilcoxon signed rank test", "topics": [ "wilcox_test" ] } ], "_readme": "https://github.com/bioc/structToolbox/raw/HEAD/README.md", "_rundeps": [ "abind", "askpass", "Biobase", "BiocGenerics", "cli", "cpp11", "curl", "DelayedArray", "evaluate", "farver", "generics", "GenomicRanges", "ggplot2", "ggthemes", "glue", "gridExtra", "gtable", "highr", "httr", "httr2", "IRanges", "isoband", "jsonlite", "knitr", "labeling", "lattice", "lifecycle", "limma", "magrittr", "Matrix", "MatrixGenerics", "matrixStats", "mime", "openssl", "pillar", "pkgconfig", "purrr", "R6", "rappdirs", "RColorBrewer", "rlang", "S4Arrays", "S4Vectors", "S7", "scales", "Seqinfo", "sp", "SparseArray", "statmod", "stringi", "stringr", "struct", "SummarizedExperiment", "sys", "tibble", "utf8", "vctrs", "viridisLite", "withr", "xfun", "XVector", "yaml" ], "_vignettes": [ { "source": "data_analysis_omics_using_the_structtoolbox.Rmd", "filename": "data_analysis_omics_using_the_structtoolbox.html", "title": "Data analysis of metabolomics and other omics datasets using the structToolbox", "author": "Gavin R Lloyd, Andris Jankevics, Ralf J Weber", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Getting started", "Case Studies", "Tutorials", "Session Info" ], "created": "2020-04-22 20:15:10", "modified": "2026-02-21 12:45:44", "commits": 11 } ], "_score": 6.567731962548069, "_indexed": true, "_nocasepkg": "structtoolbox", "_universes": [ "bioc", "grlloyd", "computational-metabolomics" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.25.0", "date": "2026-05-30T10:40:47.000Z", "distro": "noble", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "f2ec5f08701a7b89f46827c4f21c1f4ab4cb5f7e23604935aa59ea8607d5f49a", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" }, { "r": "4.6.0", "os": "linux", "version": "1.25.0", "date": "2026-05-30T10:40:52.000Z", "distro": "noble", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "334025ffbdd67068189a55678c71cc53f79308cd701a1e2e8a235be35c5b8e31", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" }, { "r": "4.5.3", "os": "mac", "version": "1.25.0", "date": "2026-05-30T15:15:03.000Z", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "5a5570342d9cc1a885caea3b04d557140d4d4f3150cc496957984b9e380f15da", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" }, { "r": "4.6.0", "os": "mac", "version": "1.25.0", "date": "2026-05-30T15:15:07.000Z", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "bb16909c3e0de15dfb73918dbe40903f00564305645f521de4037eff30b9d03c", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" }, { "r": "4.6.0", "os": "wasm", "version": "1.25.0", "date": "2026-05-30T10:39:46.000Z", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "376552b287e7b84b0a82e71680d199928a54e219ec07554f33a1e7918c9129e4", "status": "success", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" }, { "r": "4.7.0", "os": "win", "version": "1.25.0", "date": "2026-05-30T10:38:23.000Z", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "1a4e12ce885e13c5a9d5c2d59f811c39d013e6a8a0a8e0c7c33d94f2026926fd", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" }, { "r": "4.5.3", "os": "win", "version": "1.25.0", "date": "2026-05-30T10:38:26.000Z", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "17453701a4b7d24bd2fcffde587881033db25ac3575e549b8f5aaf2851b4afad", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" }, { "r": "4.6.0", "os": "win", "version": "1.25.0", "date": "2026-05-30T10:38:19.000Z", "commit": "9774192686e85e7bd905de3f2f7fe1cb2a330195", "fileid": "06a35970bdf30430e1bb33629b104c9da3fc8698d23cdc19dfc03c6bf5afe54a", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/26676668806" } ] }