Function reference • 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