Package index • gDRcore

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).

fit_SE.combinations(): fit_SE for combination screens
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

calculate_GR_value() calculate_time_dep_GR_value() calculate_endpt_GR_value(): Calculate a GR value.

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

Custom fit interface

Generic interface for applying custom fit functions to SummarizedExperiment objects, and reference implementations for drug response metrics and combination synergy scoring.

apply_fit_to_se(): apply_fit_to_se
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
fit_drug_response_metrics(): fit_drug_response_metrics
fit_drug_response_metrics_4p(): fit_drug_response_metrics_4p
bliss_fit_fn(): bliss_fit_fn
hss_fit_fn(): hss_fit_fn

Fit profile registry

Register and retrieve fit profiles that define slicing behaviour for each experiment type.

get_fit_profiles(): Get all registered fit profiles
get_fit_profile(): Get a single fit profile by name
register_fit_profile(): Register or update a fit profile