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Pipeline

Run different components of the gDR drug response processing pipeline. Either create a SummarizedExperiment and normalize raw treated and control data (create_and_normalize_SE), average data (average_SE), or fit the processed data (fit_SE).

compute_growth_rates()
Compute per-period growth rates for time-course screens
fit_SE.combinations()
fit_SE for combination screens
fit_SE.timecourse()
fit_SE for time-course (Incucyte) screens
get_period_timepoints()
List the measurement timepoints entering each growth-rate window
growth_rates_to_se()
Convert a growth rate table into the stage 2 SummarizedExperiment
average_SE() create_SE() fit_SE() normalize_SE() create_and_normalize_SE() runDrugResponseProcessingPipeline()
Run drug response processing pipeline

Merging data

merge_data()
merge_data

Combination helpers

calculate_excess()
Calculate the difference between values in two data.tables
calculate_HSA() calculate_Bliss() .calculate_matrix_metric()
Calculate a metric for combination data.
calculate_score()
Calculate score for HSA and Bliss

Annotation

annotate_dt_with_cell_line()
annotate_dt_with_cell_line
annotate_dt_with_drug()
annotate_dt_with_drug
annotate_mae_with_cell_line()
annotate_mae_with_cell_line
annotate_mae_with_drug()
annotate_mae_with_drug
annotate_se_with_cell_line()
annotate_se_with_cell_line
annotate_se_with_drug()
annotate_se_with_drug
get_cell_line_annotation()
get_cell_line_annotation
get_cellline_annotation_from_dt()
Retrieve the cell line annotation from the annotated dt input
get_drug_annotation()
get_drug_annotation
get_drug_annotation_from_dt()
Retrieve the drug annotation from the annotated dt input

Calculating GR

Converting to raw data

convert_mae_to_raw_data()
Transform mae into raw data
convert_se_to_raw_data()
Transform se into raw_data

Data type

identify_data_type()
Identify type of data
process_perturbations()
Cleanup additional perturbations in the data.table
split_raw_data()
Split raw data into list based on the data types

Preparing input

prepare_input(<MultiAssayExperiment>)
Prepare input data common for all experiments
prepare_input()
Prepare input data common for all experiments
prepare_input(<data.table>)
Prepare input data common for all experiments

Mappping

.map_references()
Map references
map_df()
Map treated conditions to their respective references.
map_ids_to_fits()
Get predicted values for a given fit and input.
map_untreated()
Identify untreated rows based on Drug treatment alone

Utils

cleanup_metadata()
cleanup_metadata
data_model()
Detect model of data
data_model(<character>)
Detect model of data from experiment name
data_model(<data.table>)
Detect model of data in data.table
get_assays_per_pipeline_step()
get info about created/present assays in SE at the given pipeline step
get_default_nested_identifiers()
Get default nested identifiers
grr_matches()
Value Matching
identify_keys()
identify_keys
order_result_df()
Order_result_df
replace_conc_with_standardized_conc()
Standardize concentrations.

Test utils

test_synthetic_data()
Testing synthetic data form gDRtestData package

Combination fit interface

Reference implementations for combination synergy scoring, applied through the generic fit interface in gDRutils.

apply_combo_sa_fits()
apply_combo_sa_fits
apply_combo_excess()
apply_combo_excess
apply_combo_isobolograms()
apply_combo_isobolograms
apply_combo_scores()
apply_combo_scores
bliss_fit_fn()
bliss_fit_fn
hss_fit_fn()
hss_fit_fn

Moved to gDRutils

The generic fit interface and the fit profile registry now live in gDRutils. They are re-exported here for one release cycle, together with the two deprecated aliases.