A comprehensive yet straightforward machine learning package for biological and biochemical research
To cite in publication
Genuer R, Poggi JM, Tuleau-Malot C. 2010. Variable selection using random forests. Pattern Recognition Letters. 31(14): 2225-2236.
Zhang J, Hadj-Moussa H, Storey KB. 2016. Current progress of high-throughput microRNA differential expression analysis and random forest gene selection for model and non-model systems: an R implementation. J Integr Bioinform. 13: 306. doi: 10.1515/jib-2016-306.
Zhang J, Hadj-Moussa H, Storey KB. 2020. Marine periwinkle stress-responsive microRNAs: a potential factor to reflect anoxia and freezing survival adaptations. GENOMICS. 020 Jul 27: S0888-7543(20)30169-5. doi: 10.1016/j.ygeno.2020.07.036.
Zhang J, Richardson DJ, Dunkley BT. 2020. Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning. Scientific Reports. 10(1): 5937. doi: 10.1038/s41598-020-62713-5.
Installation
-
Install devtools (if not already done)
install.packages("devtools") -
Install bioconductor (if not already done)
if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install() -
Install stable release
devtools::install_github("jzhangc/git_RBioFS/RBioFS", repos = BiocManager::repositories()) -
Install development build
devtools::install_github("jzhangc/git_RBioFS/RBioFS", repos = BiocManager::repositories(), ref = "beta")
Update log
0.7.5-1 (March.24.2022)
- A bug fixed where rbioClass_svm_cv() fails for regresssion study
0.7.5 (May.3.2021)
- Updates to file processing function(s)
- center.scale documentation updated with explanations for Z-score standardization and Min-Max normalization
- Updates to RF-FS function(s)
- A bug fixed for rbioFS(), rbioFS_rf_initialFS() and rbioFS_rf_SFS() where number of cores cannot set for parallel computing
- Updates to SVM function(s)
- Functions rbioClass_svm_roc_auc() and rbioClass_svm_cv_roc_auc() now allow custom file names for export files
- rbioClass_svm_ncv_fs() updated with error handling where no significant features are found when univariate.fs = TRUE: The current impolementation stops the function.
- rbioClass_svm_predcit() updated with user customizable export name prefix
- Also, the default name chagned from "object" string to the "new data string"
- rbioClass_svm_roc_auc() now automatically center.scale the training data when no new data is provided
- (testing, might revert back) rbioClass_svm_predcit() updated with new data matrix converting method
- Updates to the PLS function(s)
- rbioClass_plsda_predcit() updated with user customizable export name prefix
- Also, the default name chagned from "object" string to the "new data string"
- (testing, might revert back) rbioClass_plsda_predcit() updated with new data matrix converting method
- Updates to the PCA function(s)
- rbioFS_PCA() now supports custom export name, via "export.name" argument
- Updates to the Util function(s)
- rbioUtil_classif_accuracy() now outputs confusion matrix and input data labels
- Manual page updated for rbioUtil_classif_accuracy()
- Default argument value fixed for rbioUtil_classplot()
- rbioUtil_classplot() now supports optional customized export file name prefix via export.name argument
- Other upates
- The "prediction" object now contains a newdata.id item
- Citatioin information updated
0.7.4 (Aug.16.2020)
- Updates to the RF-FS function(s):
- Error handling added for input data containing NA/missing data
- Stops the function and suggests imputation when NA/missing data detected
- Data center+scale functionality added for rbioFS() via the new "center.scale"" argument
- Other updates
- Typos fixed for documentations
0.7.3 (Aug.14.2020)
- New SVM function(s)
- rbioClass_svm_cv() added for non-nested cross-validation, without feature selection
- The function generates a "rbiosvm_cv" class object
- General updates
- All functions updated to be compatible with R version 4.0.
- Update to SVM function(s)
- Error handling added for rRF_FS to rbioClass_svm_ncv_fs()
- "rbiosvm_cv" class support added for rbioClass_svm_cv_roc_auc()
- Regression model support removed from rbioClass_svm_roc_auc()
- Regression model support removed from rbioClass_svm_cv_roc_auc()
- rbioClass_svm_perm updated to accommodate SVM models without center.scaledX
- Other updates
- Small fixes
- Additional citations added
0.7.2 (Mar.12.2020)
- Update to SVM function(s)
- rbioClass_svm_ncv_fs() now conducts stratified k-fold CV for CV segmentation for classification modelling
- Fixed a bug for rbioClass_svm_roc_auc() still displays redundant warning messages
- Fixed a bug where rbioClass_svm_roc_auc() and rbioClass_svm_cv_roc_auc() would fail with more than two groups
0.7.1
- Update to SVM function(s)
- rbioClass_svm_roc_auc() plot.lineSize argument added
- rbioClass_svm_cv_roc_auc() now supports regression study
0.7.0
- General update(s)
- rbioUtil_perm_plot() updated to accommodate PLSR functions
- New utility function(s)
- rbioUtil_classif_accuracy(): calculates classification accuracy with new data
- New SVM function(s)
- rbioClass_svm_cv_roc_auc(): ROC-AUC analysis for K-fold cross-validation
- rbioReg_svm_rmse(): calculates RMSE for the SVR model, either with newdata or training data
- rbioReg_svm_r2(): calculate R2 for the SVR model with newdata
- Update to file processing function(s)
- center_scale() updated with more accurate function documentation
- Update to SVM function(s)
- rbioClass_svm_ncv_fs() now includes a limma-based univariate analysis component
- rbioClass_svm_ncv_fs() now outputs the CV models and the sample partitioning status
- rbiosvm_nestedcv class now incluldes nested.cv.models to include full CV models
- rbiosvm_nestedcv class now also inlucdes CV test data within the nested.cv.models item
- rbioClass_svm_ncv_fs() now can use "median" method to select the best CV models for feature selection
- rbiosvm_nestedcv class now incluldes accuracy/RMSE/rsq/fs.count for the "best" cv models selected by the "median" method
- R2 calculation added to rbioClass_svm_ncv_fs for regression study
- print function updated accordingly for rbiosvm_nestedcv class\
- rbioClass_svm_roc_auc() now outputs thresholds values
- The svm_roc_auc class now includes the roc object from the pROC package named "svm.auc_object", with which the stats can be done to compare ROCs.
- The svm_roc_auc class item "svm.auc" now changed to "svm.auc_dataframe"
- rbioClass_svm_roc_auc() now computes 95% CI
- rbioClass_svm_roc_auc() now used predicted probablity for ROC analysis (as opposed to predicted class)
- rbioClass_svm_roc_auc() updated with control/case availability check
- New PLSR function(s)
- rbioReg_plsr() function added for PLS regression analysis
- "rbiomvr" object from this function has model.type = "regression"
- rbioReg_plsr_ncomp_select() added
- rbioReg_plsr_perm() added
- rbioReg_plsr_vip() added
- NOTE: the plot function is the same as the classification model: rbioFS_plsda_vip_plot()
- Updates to PLS-DA function(s)
- Print function for relevant functions to accommodate the new plsr functions
- rbiomvr_vip object now also has a model.type variable
- rbioFS_plsda_vip_plot() fixed for small aesthetic settings
- rbioClass_plsda_roc_auc() now outputs thresholds values
- rbioClass_plsda_roc_auc() now computes 95% CI
- A bug fixed for rbioClass_plsda_perm() where intercept was counted for ncomp
- A bug fixed for rbioFS_plsda_vip() where comps fixed to 1 when set bootstrap OFF
- A bug fixed for rbioFS_plsda_vip() the function would crash when only two groups and when set bootstrap OFF
- Version pump to 0.7.0
0.6.3
- General updates
- match.arg() method added to relevant functions for better user experience
- rbioUtil_classplot() updated accordingly to accommodate the regression study
- Manual pages cominbed for S3 methods
- Updates to the PCA function(s)
- rbioFS_PCA now can display more than six groups
- (not final) rbioFS_PCA now can handle single variable data matrix
- Update to SVM function(s)
- rbioClass_svm() updated with support vector regression analysis support
- "rbiosvm" class updated accordingly with the "model.type" item, to reflect "classification" or "regression"
- The print function for "rbiosvm" class adjusted for better presentation
- rbioClass_svm_ncv_fs() updated with support vector regression analysis support
- "rbiosvm_nestedcv" class updated accordingly with the "model.type" item, to reflect "classification" or "regression"
- The print function for "rbiosvm_nestedcv" updated accordingly with the regression study support
- Parallel module re-written for rbioClass_svm_ncv_fs() for higher stability`
- rbioClass_svm_ncv_fs() now records the run time
- "rbiosvm_nestedcv" class now has a "run.time" item to store the run time
- The print function for "rbiosvm_nestedcv" class updated to display the run time
- rbioClass_svm_ncv_fs() now exports all iteration RF-FS results to both the working directory and the global environment
- rbioClass_svm_perm() now supports regression SVM models
- "rbiosvm_perm" class item names adjusted for the perforamce metric type according to the SVM model type
- "rbiosvm_perm" class now has "model.type" to reflect regression or classification
- rbioClass_svm_perm() now records run time
- "rbiosvm_perm" class now has a "run.time" item to store the run time
- A bug fixed for rbioClass_svm_perm() where parallel computing fails to generate different random resampling results
- A bug fixed for rbioClass_svm_perm() where "by_feature_per_y" method fails to permutate columns
- rbioClass_svm_predict() updated with regression study support. In such case, the function also requires outcome y input and outputs total RMSE
- Accordingly, the "prediction" class updated with new items "model.type", "tot.predict.RMSE", and "newdata.y"
- Accordingly, the print function of the "prediction" adjusted for regression study
- rbioClass_svm_roc_auc() now supports regression study
- Accordingly, and due to the required by ROC-AUC analysis, new argument "y.threshold" and "newdata.y" arguments added to convert continuous variable into categorical
- The output is now a S3 class "svm_roc_auc", with all the appropriate items
- Updates to PLS-DA function(s)
- "rbiomvr" class updated with new item "model.type" for compatibility with the regression study
- The output from rbioClass_plsda_predict() now includes the updated "prediction" class
- rbioClass_plsda_scoreplot() now supports more than six groups
- A bug fixed for rbioClass_plsda_perm() where parallel computing fails to differ random resampling results
- A bug fixed for rbioClass_plsda_perm() where "by_feature_per_y" method fails to permutate columns
- A bug fixed for rbioFS_plsda_vip() where the function will crash if the input object only have one comp
- Updates to RF-FS function(s)
- RF-FS now accepts regression analysis
- The code base significantly improved for rbioFS_rf_initialFS() and rbioFS_rf_sfs()
- Function run time added to the output classes for rbioFS_rf_initialFS() and rbioFS_rf_sfs()
- rbioFS_rf_initialFS() now exports vi_summary into a CSV file
- rbioFS_rf_sfs() now exports error_summary into a CSV file
- New items added to the rf_ifs class: ntree, rf_iteration, initial_FS_run_time
- New items added to the rf_sfs class: ntree, rf_iteration, SFS_run_time
- A bug fixed for rbioFS_rf_SFS_plot() y-axis range
- Small syntax fixes
0.6.2
- Updates to RF-FS functions:
- When imputation option enabled, rbioFS() function now also ouputs impuated data.frame into the enviroment
- Argument "annotVarNames" added so that rbioFS() is able to exclude all the annotation columns from the input data
- Updates to SVM functions
- Additional argument check added to rbioClass_svm_roc_auc(), rbioClass_plsda() and rbioClass_plsda_scoreplot()
- Updates to PLS-DA functions
- The output object from rbioClass_plsda_ncomp_select() now a "rbiomvr_ncomp_select" class
- The "rbiomvr_ncomp_select" class now includes the "ncomp_selected" matrix
- Print function added for the "rbiomvr_ncomp_select" class
- The "newdata.y" argument changed to "newdata.label" for rbioClass_plsda_roc_auc()
- A bug fixed for the verbose functionality for rbioClass_plsda_perm() and rbioClass_plsda_ncomp_select()
- The method "1sd" rbioClass_plsda_ncomp_select() changed to "1err"
- Other updates
- CPU cores now can be set for the functions suppporting parallel computing
- Other bug fixes
0.6.1
- New SVM functions:
- rbioClass_svm_ncv_fs(): nested SVM cross-validation function with feature selection functionality
- New PLS-DA functions:
- rbioFS_plsda_vip_plot(): the function only accepts "rbiomvr_vip" class object
- Updates to SVM functions (non-Shiny):
- Fixed a bug where rbioClass_svm cannot handle group weight in the scenario of not all groups represent in the training data
- S3 print method for relevant functions
- Additional argument checks added for all functions
- Updates to PLS-DA functions (non-Shiny):
- Changes made to rbioClass_plsda() and rbioClass_plsda_perm() to acconmmodate validation = "LOO"
- rbioFS_plsda_VIP() changed to rbioFS_plsda_vip()
- Bootstraping option added for rbioFS_plsda_vip() so that VIP can use bootstrap data for SD/SEM errorbars
- rbioFS_plsda_vip() now outputs a "rbiomvr_vip" class object
- Plot module removed from rbioFS_plsda_vip() and now a separated function: rbioFS_plsda_vip_plot()
- rbioClass_plsda_roc_auc() now accepts custom newdata
- Relevant functions now also output results tst file to the directory
- S3 print method for relevant functions
- Additional argument checks
- Updates to RF-FS functions:
- Boxplot for the rf_ifs object now has a horizontal line indicating the selection result
- rf_ifs object now contains: feature_initial_FS, vi_at_threshold, vi_summary, initial_FS_OOB_err_summary, training_initial_FS
- S3 print method for relevant functions
- Updated method for export a list for rf_ifs and rf_sfs classes
- Updates to the PCA functions
- Loadingplot disabled message when more than 2 PCs are used
- Other updates
- Documentation edits for rbioClass_plsda()
- Functions updated for R Notebook/Markdown compatibility
- Dependency ggplot2 now requires version 3.0.0
- New bioconductor installation instructions added
- Bug fixes
0.6.0
- New generic functions:
- Generic plot function for permutation test: rbioUtil_perm_plot(). Current supported classes: rbiomvr_perm, rbiosvm_perm
- Generic plot function for classification: rbioUtil_classplot(). Current supported class: prediction
- SVM functions added (non-Shiny):
- rbioClass_svm()
- rbioClass_svm_roc_auc()
- rbioClass_svm_perm()
- rbioClass_svm_predict()
- New PLS-DA functions added (non-Shiny):
- rbioClass_plsda_perm(): permutation test for plsda models, with two permutation methods
- Updates to SVM functions:
- Class weight determination functionality added to rbioClass_svm()
- Additional items added for the modelling settings to the SVM model object (i.e. rbiosvm object)
- Updates to PLS-DA functions (non-Shiny):
- All PLS-DA function names updated with new prefix: rbioClass_, except for the VIP function, which is a FS function
- Data centering and scaling argument "center.newdata"" added to rbioClass_plsda_predict()
- When "center.newdata = TRUE", the function applies training data's col.mean and col.sd to the test data
- rbioClass_plsda_predict() new supports data.frame object as newdata
- Additional argument checking added for rbioClass_plsda_perm()
- rbioClass_plsda_roc_auc() now correctly uses the centered data from the rbiomvr object for ROC-AUC analysis
- Argument checking functionality adjusted with correct class checking for all PLS-DA functions
- Output object of rbioClass_class_perm() is now defined as "rbiomvr_perm" object
- rbioClass_plsda_perm() updated with plotting capability, using rbioUtil_perm_plot method for class "rbiomvr_perm"
- rbioClass_plsda_classplot() now changed to a generic function rbioUtil_classplot() applicable to other classifier predictions
- Updates to RF-FS functions:
- All RF-FS function names updated with new prefix: rbioFS_rf_
- verbose argument added for all the relavent functions so that user can silence the messages
- Overall code base optimization
- Bug fixes
0.5.3
- Updates to rbioFS_PCA():
- Legend style adjusted for the sample labels
- Updates to RF-FS functions:
- For consistency, the dashed line indicators now in red in all the relevant functions
- Updates to PLS-DA functions (non-Shiny):
- Bayesian probability option added to rbioFS_plsda_predict()
- The entire probability calculation and classification module of rbioFS_plsda_classification() merged to rbioFS_plsda_predict()
- The prediction object will now contain the following sections: predicted.value, probability.summary, probability.method
- rbioFS_plsda_classification() is now changed to a plotting plot function: rbioFS_plsda_classplot()
- Options to move probability labels out of the pies adedd for rbioFS_plsda_classplot()
- rbioFS_plsda_predict(): the legend adjusted to "within threshold" and "outside of threshold"
- Legend style adjusted for the sample labels for the relevant functions
- Bug fixes
0.5.2
- New PLS-DA functions added (non-Shiny):
- rbioFS_plsda_predict(): use the plsda model to calcualte predicted values for unknown data.
- rbioFS_plsda_classification(): use the predicted values (produced by rbioFS_plsda_predict) to classify. Note: current probability method is "softmax". A "Bayesian" method will be added later.
- Updates to RF-FS functions:
- Plotting module separated from the functions
- Boxplot for the rf_ifs object now horizontal
- Plot file suffix for both VI boxplot and OOB plot now ".rffs.ifs.plot.pdf" and ".rffs.sfs.plot.pdf", respectively
- Classes "rf_ifs" and "rf_sfs" created for the output of rbioRF_initial_FS() and rbioRF_SFS(), respectively
- Display messages added for the functions
- plots title and axis title are in bold
- RF-FS analysis plotting module separated from rbioFS() function as functions:
- rbioRF_initialFS_plot()
- rbioRF_SFS_plot()
- Updates to PLS-DA functions (non-Shiny):
- Smooth functionality added for rbioFS_plsda_roc_auc()
- Legend position now customizable for multi-plot for the relevant functions
- rbioFS_plsda_jackknife(): options added for hiding the x-axis tick labels (useful in the case of many variables)
- rbioFS_plsda_jackknife(): for plotting, x-axis margin adjusted
- rbioFS_plsda_VIP(): options added for hiding the x-axis tick labels (useful in the case of many variables)
- rbioFS_plsda_VIP() how outputs a list with VIP values as well as a vector containing features above the threshold
- Error handling added for all the functions featuring multiplot, excluding the rbioFS_plsda_scoreplot()
- Plot theme adjusted for functions:
- rbioFS_plsda_ncomp_select()
- rbioFS_plsda_tuplot()
- Output (to R environment) object name suffix adjusted for all the relevant functions with added "_plsda"
- xLabelSize and yLabelSize added for the functions
- rbioFS_plsda_scoreplot(): sample labeling functionality added
- Size option added for ggrepel label for the relevant functions
- Bug fixes
0.5.1
- New PLS-DA functions added (non-Shiny):
- rbioFS_plsda_VIP(): VIP, or variable importance in projection, is plsda's version of VI. Can be used independently from plsda functions
- rbioFS_plsda_q2r2(): Q2-R2 calculation and plotting
- rbioFS_plsda_aoc_auc(): ROC and AUC analysis and plotting
- Updates to PLS-DA functions (non-Shiny):
- More information added to the manual page for rbioFS_plsda_ncomp_select()
- Additional arugment checks added to rbioFS_plsda_ncomp_select()
- A bug fixed where rbioFS_plsda_jackknife() fails if no coefs are > (or <) 0
- Small changes made to message display pattern in rbioFS_plsda_jackknife()
- Plot property auguments names unified for function only produce one type of plot
- Updates to RF-FS functions:
- rbioFS() now accepts R objects, in addition to csv files
- Group variable now customizable for rbioFS()
- Arugment check added for rbioFS()
- rbioFS() output element "SFS_matrix" changed to "SFS_training_data_matrix"
- Function message display feature added to rbioFS()
- Bug fixes
0.5.0
- Data preprocessing functions added for modelling precedures such as PLS-DA, sPLS-DA, PCA, SVM, etc.
- center_scale()
- dummy()
- PLS-DA functions added (non-Shiny):
- rbioFS_plsda()
- rbioFS_plsda_ncomp_select()
- rbioFS_plsda_tuplot()
- rbioFS_plsda_scoreplot()
- rbioFS_plsda_jackknife()
- rbioFS_PCA() re-written with the follwoing new functionalities:
- The function now outputs a PCA object to the environment
- PCA scoreplot now supports single component curve
- PCA scoreplot now supports paired matrix, i.e. more than two components
- PCA boxplot y upper limited adjusted for both shiny and non-shiny versions
- PCA score plot now supports sample names for the samples
- Rightside y-axis now uses a function from RBioplot pakcage, which now is a dependency
- Bug fixes
0.4.6
- All the settings return to default upon "clear"
- A bug fixed for rbioFS_app() where plots can't be regenerated on a new dataset upon "clear"
- Other bug fixes
0.4.4 - 0.4.5
- fs_csv_generator() added
- Bug fixes
0.4.3
- Web app verion of rbioFS_PCA() added: rbioFS_PCA_app()
- Code update for rbioFS_PCA() for better input data compatibility
- Parallel computing functionality added for rbioFS_app()
- Quantile normalization functionality added for rbioFS_app()
- Clear screen button added for rbioFS_app()
- Parallel computing modules updated with the more efficient foreach method for rbioFS(), rbioRF_initialFS() and rbioRF_SFS()
- Bug fixes
0.4.1 - 0.4.2
- Progress bar added for rbioFS_app()
- Plotting buttons added for rbioFS_app()
- UI elements re-arranged for a better presentation for rbioFS_app()
- Small icons added for the buttons for rbioFS_app()
- "Summary" tabs re-labelled as "Results" tabs for rbioFS_app()
- NA check added for the initial FS module in rbioFS_app()
- Bug fixes for rbioFS_app()
- Other minor bug fixes
0.4.0
- Web app version of the main fuction rbioFS() added: rbioFS_app()
- A bug fixed for the plot subsetting functionality for rbioRF_SFS()
- Other minor bug fixes
0.3.3
- zzz.R file added
0.3.0 - 0.3.2
- Principal Component Analysis (PCA) and visualization function rbioFS_PCA() added
- Citation information added
- Bug fixes
0.2.0 - 0.2.5
- All-in-one FS function added
- Bug fixes
0.1.11 - 0.1.12
- Output results as txt files functionality added
- Bug fixes
0.1.10
- Random Forest data imputation method added to the data imputation function
- Round down now used for mtry augment
0.1.9
- Text fixes
0.1.7 - 0.1.8
- Name changed to RBioFS
- Bug fixes
0.1.6
- rbioRF_iterOOB() updated
0.1.5
- Iterative OOB error rate computation function added
0.1.4
- Initial FS function completed
0.1.3
- Parallel computing added for rbioRF_vi()
0.1.2
- rbioRF_vi and rbioRF_viplot functions combined to steramline the workflow
0.1.1
- Data imputation function added
- File processing functions added
0.1.0
- Initial commit