Package: structToolbox 1.19.0
Gavin Rhys Lloyd
structToolbox: Data processing & analysis tools for Metabolomics and other omics
An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.
Authors:
structToolbox_1.19.0.tar.gz
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structToolbox.pdf |structToolbox.html✨
structToolbox/json (API)
NEWS
| # Install 'structToolbox' in R: |
| install.packages('structToolbox', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/computational-metabolomics/structtoolbox/issues
On BioConductor:structToolbox-1.19.0(bioc 3.21)structToolbox-1.18.0(bioc 3.20)
workflowstepmetabolomicsbioconductor-packagedimslc-msmachine-learningmultivariate-analysisstatisticsunivariate
Last updated 25 days agofrom:4622d2fd8f. Checks:OK: 4 WARNING: 3. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | WARNING | Oct 31 2024 |
| R-4.5-linux | OK | Oct 31 2024 |
| R-4.4-win | WARNING | Oct 31 2024 |
| R-4.4-mac | OK | Oct 31 2024 |
| R-4.3-win | WARNING | Oct 31 2024 |
| R-4.3-mac | OK | Oct 31 2024 |
Exports:ANOVAas_data_frameAUCautoscalebalanced_accuracyblank_filterblank_filter_histbootstrapcalculatechart_plotclassical_lsqcompare_distconfounders_clsqconfounders_lsq_barchartconfounders_lsq_boxplotconstant_sum_normcorr_coefDatasetExperiment_boxplotDatasetExperiment_distDatasetExperiment_factor_boxplotDatasetExperiment_heatmapDFAdfa_scores_plotdratio_filterequal_splitfeature_boxplotfeature_profilefeature_profile_arrayfilter_by_namefilter_na_countfilter_smetafisher_exactfold_changefold_change_intfold_change_plotforward_selection_by_rankfs_lineglog_opt_plotglog_transformgrid_search_1dgs_lineHCAhca_dendrogramHSDHSDEMkfold_xvalkfoldxcv_gridkfoldxcv_metricknn_imputekw_p_histkw_rank_sumlinear_modellog_transformmean_centremean_of_mediansmixed_effectmodel_applymodel_predictmodel_reversemodel_trainMTBLS79_DatasetExperimentmv_boxplotmv_feature_filtermv_feature_filter_histmv_histogrammv_sample_filtermv_sample_filter_histnroot_transformontology_cacheOPLSDAOPLSRpairs_filterpareto_scalePCApca_biplotpca_correlation_plotpca_dstat_plotpca_loadings_plotpca_scores_plotpca_scree_plotpermutation_testpermutation_test_plotpermute_sample_orderpls_regcoeff_plotpls_scores_plotpls_vip_plotPLSDAplsda_feature_importance_plotplsda_predicted_plotplsda_roc_plotplsda_scores_plotPLSRplsr_cook_distplsr_prediction_plotplsr_qq_plotplsr_residual_histpqn_normpqn_norm_histprop_nar_squaredresampleresample_chartrsd_filterrsd_filter_histrunsb_corrscatter_chartsplit_datastratified_splitSVMsvm_plot_2dtic_charttSNEtSNE_scatterttestvec_normwilcox_p_histwilcox_test
Dependencies:abindaskpassBiobaseBiocGenericsclicolorspacecrayoncurlDelayedArrayevaluatefansifarvergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggthemesgluegridExtragtablehighrhttrhttr2IRangesisobandjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeontologyIndexopensslpillarpkgconfigpurrrR6rappdirsRColorBrewerrlangrolsS4ArraysS4VectorsscalesspSparseArraystringistringrstructSummarizedExperimentsystibbleUCSC.utilsutf8vctrsviridisLitewithrxfunXVectoryamlzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Analysis of Variance | ANOVA |
| Convert to data.frame | as_data_frame as_data_frame,filter_na_count-method as_data_frame,ttest-method as_data_frame,wilcox_test-method |
| Area under ROC curve | AUC |
| Autoscaling | autoscale |
| Balanced Accuracy | balanced_accuracy |
| Blank filter | blank_filter |
| Histogram of blank filter fold changes | blank_filter_hist |
| Bootstrap resampling | bootstrap |
| Calculate metric | calculate calculate,AUC-method calculate,balanced_accuracy-method calculate,r_squared-method |
| chart_plot method | 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 |
| Univariate Classical Least Squares Regression | classical_lsq |
| Compare distributions | compare_dist |
| Check for confounding factors | confounders_clsq |
| Confounding factor relative change barchart | confounders_lsq_barchart |
| Confounding factor relative change boxplot | confounders_lsq_boxplot |
| Normalisation to constant sum | constant_sum_norm |
| Correlation coefficient | corr_coef |
| Feature distribution histogram | DatasetExperiment_boxplot |
| Feature distribution histogram | DatasetExperiment_dist |
| Factor boxplot | DatasetExperiment_factor_boxplot |
| DatasetExperiment heatmap | DatasetExperiment_heatmap |
| Discriminant Factor Analysis | DFA |
| DFA scores plot | dfa_scores_plot |
| Dispersion ratio filter | dratio_filter |
| Equal group sized sampling | equal_split |
| Feature boxplot | feature_boxplot |
| Feature profile | feature_profile |
| Feature profile | feature_profile_array |
| Filter by name | filter_by_name |
| Minimum number of measured values filter | filter_na_count |
| Filter by sample meta data | filter_smeta |
| Fisher Exact Test | fisher_exact |
| Fold change | fold_change |
| Fold change for interactions between factors | fold_change_int |
| Fold change plot | fold_change_plot |
| Forward selection by rank | forward_selection_by_rank |
| Forward selection line plot | fs_line |
| Glog optimisation | glog_opt_plot |
| Generalised logarithmic transform | glog_transform |
| One dimensional grid search | grid_search_1d |
| Grid search line plot | gs_line |
| Hierarchical Cluster Analysis | HCA |
| HCA dendrogram | hca_dendrogram |
| Tukey's Honest Significant Difference | HSD |
| Tukey's Honest Significant Difference using estimated marginal means | HSDEM |
| k-fold cross-validation | kfold_xval |
| k-fold cross validation plot | kfoldxcv_grid |
| kfoldxcv metric plot | kfoldxcv_metric |
| kNN missing value imputation | knn_impute |
| Histogram of p values | kw_p_hist |
| Kruskal-Wallis rank sum test | kw_rank_sum |
| Linear model | linear_model |
| logarithm transform | log_transform |
| Mean centre | mean_centre |
| Mean of medians | mean_of_medians |
| Mixed effects model | mixed_effect |
| Apply method | 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 |
| Model prediction | 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 |
| Reverse preprocessing | model_reverse model_reverse,autoscale,DatasetExperiment-method model_reverse,mean_centre,DatasetExperiment-method |
| Train a model | 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 |
| MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control | MTBLS79_DatasetExperiment |
| Missing value boxplots | mv_boxplot |
| Filter features by missing values | mv_feature_filter |
| Histogram of missing values per feature | mv_feature_filter_hist |
| Missing value histogram | mv_histogram |
| Missing value sample filter | mv_sample_filter |
| Histogram of missing values per sample | mv_sample_filter_hist |
| nth root transform | nroot_transform |
| ontology cache | ontology_cache |
| Orthogonal Partial Least Squares regression | OPLSDA |
| Orthogonal Partial Least Squares regression | OPLSR |
| Pairs filter | pairs_filter |
| Pareto scaling | pareto_scale |
| Principal Component Analysis (PCA) | PCA |
| PCA biplot | pca_biplot |
| PCA correlation plot | pca_correlation_plot |
| d-statistic plot | pca_dstat_plot |
| PCA loadings plot | pca_loadings_plot |
| PCA scores plot | pca_scores_plot |
| Scree plot | pca_scree_plot |
| Permutation test | permutation_test |
| permutation_test_plot class | permutation_test_plot |
| Permute Sample Order | permute_sample_order |
| pls_regcoeff_plot class | pls_regcoeff_plot |
| PLSDA scores plot | plsda_scores_plot pls_scores_plot pls_scores_plot, |
| PLSDA VIP plot | pls_vip_plot |
| Partial least squares discriminant analysis | PLSDA |
| PLSDA feature importance summary plot | plsda_feature_importance_plot |
| PLSDA predicted plot | plsda_predicted_plot |
| PLSDA ROC plot | plsda_roc_plot |
| Partial least squares regression | PLSR |
| Cook's distance barchart | plsr_cook_dist |
| PLSR prediction plot | plsr_prediction_plot |
| PLSR QQ plot | plsr_qq_plot |
| PLSR residuals histogram | plsr_residual_hist |
| Probabilistic Quotient Normalisation (PQN) | pqn_norm |
| PQN coefficient histogram | pqn_norm_hist |
| Fisher's exact test for missing values | prop_na |
| Coefficient of determination (R-squared) | r_squared |
| Data resampling | resample |
| resample_chart class | resample_chart |
| RSD filter | rsd_filter |
| RSD histogram | rsd_filter_hist |
| Runs an iterator, applying the chosen model multiple times. | 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 |
| Signal/batch correction for mass spectrometry data | sb_corr |
| Group scatter chart | scatter_chart |
| Split data | split_data |
| Stratified sampling | stratified_split |
| Support Vector Machine Classifier | SVM |
| SVM scatter plot | svm_plot_2d |
| Total Ion Count chart. | tic_chart |
| tSNE | tSNE |
| Feature boxplot | tSNE_scatter |
| t-test | ttest |
| Vector normalisation | vec_norm |
| Histogram of p values | wilcox_p_hist |
| wilcoxon signed rank test | wilcox_test |
