Package: GeneSelectR 0.0.0.9000

GeneSelectR: Comprehensive Feature Selection Worfkflow for Bulk RNAseq Datasets

GeneSelectR is a versatile R package designed for efficient RNA sequencing data analysis. Its key innovation lies in the seamless integration of the Python sklearn machine learning framework with R-based bioinformatics tools. This integration enables GeneSelectR to perform robust ML-driven feature selection while simultaneously leveraging the power of Gene Ontology (GO) enrichment and semantic similarity analyses. By combining these diverse methodologies, GeneSelectR offers a comprehensive workflow that optimizes both the computational aspects of ML and the biological insights afforded by advanced bioinformatics analyses. Ideal for researchers in bioinformatics, GeneSelectR stands out as a unique tool for analyzing complex RNAseq datasets with enhanced precision and relevance.

Authors:Damir Zhakparov [aut, cre]

GeneSelectR_0.0.0.9000.tar.gz
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GeneSelectR.pdf |GeneSelectR.html
GeneSelectR/json (API)

# Install 'GeneSelectR' in R:
install.packages('GeneSelectR', repos = c('https://dzhakparov.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/dzhakparov/geneselectr/issues

On CRAN:

5.76 score 19 stars 7 scripts 196 downloads 23 exports 75 dependencies

Last updated 5 months agofrom:4e7dfd0e15. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 24 2024
R-4.5-winNOTEOct 24 2024
R-4.5-linuxNOTEOct 24 2024
R-4.4-winNOTEOct 24 2024
R-4.4-macNOTEOct 24 2024
R-4.3-winOKOct 24 2024
R-4.3-macOKOct 24 2024

Exports:annotate_gene_listscalculate_overlap_coefficientscalculate_permutation_feature_importancecompute_GO_child_term_metricsconfigure_environmentdefine_sklearn_modulesevaluate_test_metricsGeneSelectRget_feature_importancesGO_enrichment_analysisimport_python_packagesinstall_python_packagesperform_grid_searchpipeline_to_listplot_feature_importanceplot_metricsplot_overlap_heatmapsplot_upsetrun_multiple_simplifyGOrun_simplify_enrichmentset_reticulate_pythonskip_if_no_modulessplit_data

Dependencies:beeswarmbitopsbriocallrcaToolsclicolorspacecowplotcpp11crayondescdiffobjdigestdplyrevaluatefansifarverfsgenericsggplot2ggrepelgluegplotsgtablegtoolshereisobandjsonliteKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepheatmappillarpkgbuildpkgconfigpkgloadplotwidgetsplyrpngpraiseprocessxpspurrrR6rappdirsRColorBrewerRcppRcppTOMLrematch2reshape2reticulaterlangrprojrootscalesstringistringrtagcloudtestthattibbletidyrtidyselecttmodutf8vctrsviridisLitewaldowithrXML

GeneSelectR Tutorial

Rendered fromexample.Rmdusingknitr::rmarkdownon Oct 24 2024.

Last update: 2024-03-07
Started: 2023-06-01

Readme and manuals

Help Manual

Help pageTopics
Aggregate Feature Importancesaggregate_feature_importances
Convert and Annotate Gene Listsannotate_gene_lists
AnnotatedGeneLists classAnnotatedGeneLists-class
Calculate Mean Cross-Validation Scores for Various Feature Selection Methodscalculate_mean_cv_scores
Calculate Overlap and Similarity Coefficients between Feature Listscalculate_overlap_coefficients
Calculate Permutation Feature Importancecalculate_permutation_feature_importance
Retrieve and Plot the Offspring Nodes of GO Termscompute_GO_child_term_metrics
Configure Python Environment for GeneSelectRconfigure_environment
Create a specific Conda environmentcreate_conda_env
Create Pipelinescreate_pipelines
Create a Dataframe of Test Metricscreate_test_metrics_df
Define Python modules and scikit-learn submodulesdefine_sklearn_modules
Enable Multiprocessing in Python Environmentenable_multiprocess
Evaluate Test Metrics for a Grid Search Modelevaluate_test_metrics
GeneList classGeneList-class
Gene Selection and Evaluation with GeneSelectRGeneSelectR
Get Feature Importancesget_feature_importances
Perform gene set enrichment analysis using clusterProfilerGO_enrichment_analysis
Import Python Librariesimport_python_packages
Install necessary Python packages in a specific Conda environmentinstall_python_packages
Perform Grid Search or Random Search for Hyperparameter Tuningperform_grid_search
Convert Scikit-learn Pipeline to Named Listpipeline_to_list
PipelineResults classPipelineResults-class
Plot Feature Importanceplot_feature_importance
Plot Performance Metricsplot_metrics
Generate Heatmaps to Visualize Overlap and Similarity Coefficients between Feature Listsplot_overlap_heatmaps
Plot Feature Overlaps Using UpSet Plotsplot_upset
Run multiple simplifyGOFromMultipleListsrun_multiple_simplifyGO
Run simplifyGOFromMultipleLists with specified measure and methodrun_simplify_enrichment
Set Default Feature Selection Methodsset_default_fs_methods
Set Default Parameter Grids for Feature Selectionset_default_param_grids
Set RETICULATE_PYTHON for the Current Sessionset_reticulate_python
Check if Python Modules are Availableskip_if_no_modules
Split Data into Training and Test Setssplit_data
Convert Steps to Tuplessteps_to_tuples