Package: gDRcore 1.11.18

Arkadiusz Gladki

gDRcore: Processing functions and interface to process and analyze drug dose-response data

This package contains core functions to process and analyze drug response data. The package provides tools for normalizing, averaging, and calculation of gDR metrics data. All core functions are wrapped into the pipeline function allowing analyzing the data in a straightforward way.

Authors:Bartosz Czech [aut], Arkadiusz Gladki [cre, aut], Marc Hafner [aut], Pawel Piatkowski [aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Marcin Kamianowski [aut], Allison Vuong [aut]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gDRcore/json (API)

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

Bug tracker:https://github.com/gdrplatform/gdrcore/issues

Pkgdown/docs site:https://gdrplatform.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On BioConductor:gDRcore-1.11.18(bioc 3.24)gDRcore-1.10.0(bioc 3.23)

softwareshinyappscpp

7.93 score 2 stars 1 packages 38 scripts 346 downloads 61 exports 110 dependencies

Last updated from:04cc3e026e. Checks:1 WARNING, 13 NOTE, 2 OK. Indexed: yes.

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Exports:annotate_dt_with_cell_lineannotate_dt_with_drugannotate_mae_with_cell_lineannotate_mae_with_drugannotate_se_with_cell_lineannotate_se_with_drugapply_combo_excessapply_combo_isobologramsapply_combo_sa_fitsapply_combo_scoresapply_custom_fitapply_custom_fitsapply_fitapply_fit_to_seapply_fitsaverage_SEbliss_fit_fncalculate_Blisscalculate_endpt_GR_valuecalculate_excesscalculate_GR_valuecalculate_HSAcalculate_scorecalculate_time_dep_GR_valuecleanup_metadatacompute_growth_ratesconvert_mae_to_raw_dataconvert_se_to_raw_datacreate_and_normalize_SEcreate_SEdata_modelfit_drug_response_metricsfit_drug_response_metrics_4pfit_SEfit_SE.combinationsfit_SE.timecourseget_cell_line_annotationget_cellline_annotation_from_dtget_default_nested_identifiersget_drug_annotationget_drug_annotation_from_dtget_fit_profileget_fit_profilesget_period_timepointsgrowth_rates_to_segrr_matcheshss_fit_fnidentify_data_typeidentify_keysmap_dfmap_ids_to_fitsmap_untreatedmerge_datanormalize_SEprepare_inputprocess_perturbationsregister_fit_profilereplace_conc_with_standardized_concrunDrugResponseProcessingPipelinesplit_raw_datatest_synthetic_data

Dependencies:abindbackportsBHBiobaseBiocBaseUtilsBiocGenericsBiocParallelbootbroomBumpyMatrixcarcarDatacheckmateclicodetoolscolorspacecowplotcpp11curldata.tableDelayedArrayDerivdigestdoBydplyrdrcfarverforecastformatRFormulafracdifffutile.loggerfutile.optionsgDRutilsgenericsGenomicRangesggplot2gluegtablegtoolsIRangesisobandjsonlitejsonvalidatelabelinglambda.rlatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmgcvminqamodelrmultcompMultiAssayExperimentmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplotrixpurrrqs2quantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreformulasrlangS4ArraysS4VectorsS7sandwichscalesSeqinfosnowSparseArraySparseMstringfishstringistringrSummarizedExperimentsurvivalTH.datatibbletidyrtidyselecttimeDateurcautf8V8vctrsviridisLitewithrXVectorzoo

Custom fitting with apply_fit()
Overview | Data setup | The fit_fn contract | Minimal example | Single-agent: apply_fit() | Writing to the Metrics assay | Idempotent merge | Coexisting fit sources | Reference Hill fit: fit_drug_response_metrics() | Numerical equivalence with fit_SE() | summary_fn: cell-level aggregation | Combination data: synergy scores | What fit_SE.combinations() does internally | Running all steps individually | Skipping a step | Two approaches for combination scoring in the new API | Approach 1: apply_combo_scores() — replicate fit_SE.combinations exactly | Approach 2: bliss_fit_fn / hss_fit_fn — simplified, no SA fits needed | Efficient multi-fit: apply_fits() | Shared pre-computation pattern | Chaining with pipe | Error handling | Time-course data: fit_SE.timecourse() | Inputs | Output structure | Custom fitting for time-course data | Key design notes | Quick reference | Function signatures | fit_fn contract | summary_fn contract | Reference implementations | Single-agent fit functions (for apply_fit()) | Combination scoring | Composable step functions | Lower-level scoring functions (for apply_fit()) | SessionInfo
Rendered fromcustom-fitting.Rmdusingknitr::rmarkdown

Last update: 2026-09-22
Started: 2026-08-12

gDRcore
Overview | Introduction | Data model | Drug processing | Required columns | gDR pipeline | Use Cases | Data preprocessing | Running gDR pipeline | Data extraction | Custom fitting | apply_custom_fit() — generic interface | apply_fit_to_se() — single-agent convenience wrapper | Key features | Reference implementations | SessionInfo
Rendered fromgDRcore.Rmdusingknitr::rmarkdown

Last update: 2026-08-12
Started: 2023-03-31

gDR -- data model
Introduction | General overview of the data model | Supported Experiments: | SummarizedExperiment objects: | MultiAssayExperiment object | SummarizedExperiment object | Assays | rowData | colData | metadata | Session info
Rendered fromgDR-data-model.Rmdusingknitr::rmarkdown

Last update: 2026-05-28
Started: 2024-02-05

gDR annotation
Data Annotation Process for gDR Pipeline | Introduction | Annotation Files | Annotation File Locations | Annotation Requirements | Drug Annotation Requirements | Cell Line Annotation Requirements | Annotating SummarizedExperiment and MultiAssayExperiment Objects | Additional Information for Genentech/Roche Users | Conclusion | SessionInfo
Rendered fromgDR-annotation.Rmdusingknitr::rmarkdown

Last update: 2026-02-17
Started: 2024-04-23

Readme and manuals

Help Manual

Help pageTopics
annotate_dt_with_cell_lineannotate_dt_with_cell_line
annotate_dt_with_drugannotate_dt_with_drug
annotate_mae_with_cell_lineannotate_mae_with_cell_line
annotate_mae_with_drugannotate_mae_with_drug
annotate_se_with_cell_lineannotate_se_with_cell_line
annotate_se_with_drugannotate_se_with_drug
apply_combo_excessapply_combo_excess
apply_combo_isobologramsapply_combo_isobolograms
apply_combo_sa_fitsapply_combo_sa_fits
apply_combo_scoresapply_combo_scores
Run drug response processing pipelineaverage_SE create_and_normalize_SE create_SE fit_SE normalize_SE runDrugResponseProcessingPipeline runDrugResponseProcessingPipelineFxns
bliss_fit_fnbliss_fit_fn
Calculate the difference between values in two data.tablescalculate_excess
Calculate a GR value.calculate_endpt_GR_value calculate_GR_value calculate_time_dep_GR_value
Calculate score for HSA and Blisscalculate_score
cleanup_metadatacleanup_metadata
Compute per-period growth rates for time-course screenscompute_growth_rates
Transform mae into raw dataconvert_mae_to_raw_data
Transform se into raw_dataconvert_se_to_raw_data
Detect model of datadata_model
Detect model of data from experiment namedata_model.character
Detect model of data in data.tabledata_model.data.table
fit_SE for combination screensfit_SE.combinations
fit_SE for time-course (Incucyte) screensfit_SE.timecourse
get info about created/present assays in SE at the given pipeline stepget_assays_per_pipeline_step
get_cell_line_annotationget_cell_line_annotation
Retrieve the cell line annotation from the annotated dt inputget_cellline_annotation_from_dt
Get default nested identifiersget_default_nested_identifiers get_default_nested_identifiers.data.table get_default_nested_identifiers.SummarizedExperiment
get_drug_annotationget_drug_annotation
Retrieve the drug annotation from the annotated dt inputget_drug_annotation_from_dt
List the measurement timepoints entering each growth-rate windowget_period_timepoints
Convert a growth rate table into the stage 2 SummarizedExperimentgrowth_rates_to_se
Value Matchinggrr_matches
hss_fit_fnhss_fit_fn
Identify type of dataidentify_data_type
identify_keysidentify_keys
Map treated conditions to their respective references.map_df
Get predicted values for a given fit and input.map_ids_to_fits
Identify untreated rows based on Drug treatment alonemap_untreated
merge_datamerge_data
Prepare input data common for all experimentsprepare_input
Prepare input data common for all experimentsprepare_input.data.table
Prepare input data common for all experimentsprepare_input.MultiAssayExperiment
Cleanup additional perturbations in the data.tableprocess_perturbations
Standardize concentrations.replace_conc_with_standardized_conc
Split raw data into list based on the data typessplit_raw_data
Testing synthetic data form gDRtestData packagetest_synthetic_data