| Title: | gDRtestData - R data package with testing dose response data |
|---|---|
| Description: | R package with internal dose-response test data. Package provides functions to generate input testing data that can be used as the input for gDR pipeline. It also contains qs2 files with MAE data processed by gDR. |
| Authors: | Aleksander Chlebowski [aut], Bartosz Czech [aut] (ORCID: <https://orcid.org/0000-0002-9908-3007>), Arkadiusz Gladki [cre, aut] (ORCID: <https://orcid.org/0000-0002-7059-6378>), Marc Hafner [aut] (ORCID: <https://orcid.org/0000-0003-1337-7598>), Marcin Kamianowski [aut], Sergiu Mocanu [aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina Smola [aut], Allison Voung [aut] |
| Maintainer: | Arkadiusz Gladki <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.10.0 |
| Built: | 2026-10-10 11:16:54 UTC |
| Source: | https://github.com/bioc/gDRtestData |
Add concentrations
add_concentration(df_layout, concentrations = 10^(seq(-3, 1, 0.5)))add_concentration(df_layout, concentrations = 10^(seq(-3, 1, 0.5)))
df_layout |
data.table that should contains the cell line, drug, concentration, and replicate columns along with the annotations that needs to be propagated |
concentrations |
vector of numeric concentrations that will be added to df_layout |
data.table with concentrations
cell_lines <- create_synthetic_cell_lines() add_concentration(cell_lines)cell_lines <- create_synthetic_cell_lines() add_concentration(cell_lines)
Add data replicates
add_data_replicates(df_layout)add_data_replicates(df_layout)
df_layout |
data.table that should contains the cell line, drug, concentration, and replicate columns along with the annotations that needs to be propagated |
data.table with replicates
cell_lines <- create_synthetic_cell_lines() add_data_replicates(cell_lines)cell_lines <- create_synthetic_cell_lines() add_data_replicates(cell_lines)
Add data with day 0
add_day0_data(df_merged, noise_level = 0.05)add_day0_data(df_merged, noise_level = 0.05)
df_merged |
data.table with merged data |
noise_level |
numeric scalar with the level of noise added to the data |
data.table with day0 data
cell_lines <- create_synthetic_cell_lines() drugs <- create_synthetic_drugs() df_merged <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ]) df_merged$Duration <- 72 df_merged$ReadoutValue <- 0 add_day0_data(df_merged)cell_lines <- create_synthetic_cell_lines() drugs <- create_synthetic_drugs() df_merged <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ]) df_merged$Duration <- 72 df_merged$ReadoutValue <- 0 add_day0_data(df_merged)
Create data.table with synthetic cell lines
create_synthetic_cell_lines()create_synthetic_cell_lines()
data.table with synthetic cell lines
create_synthetic_cell_lines()create_synthetic_cell_lines()
Create data.table with synthetic drugs
create_synthetic_drugs()create_synthetic_drugs()
data.table with synthetic drugs
create_synthetic_drugs()create_synthetic_drugs()
Calculate E inf metric
generate_e_inf(drugs, cell_lines)generate_e_inf(drugs, cell_lines)
drugs |
data.table with drugs |
cell_lines |
data.table with cell lines |
matrix with random E inf
generate_e_inf(create_synthetic_drugs(), create_synthetic_cell_lines())generate_e_inf(create_synthetic_drugs(), create_synthetic_cell_lines())
Calculate EC50 metric
generate_ec50(drugs, cell_lines)generate_ec50(drugs, cell_lines)
drugs |
data.table with drugs |
cell_lines |
data.table with cell lines |
matrix with random EC50
generate_ec50(create_synthetic_drugs(), create_synthetic_cell_lines())generate_ec50(create_synthetic_drugs(), create_synthetic_cell_lines())
Generate hill coefficient
generate_hill_coef(drugs, cell_lines)generate_hill_coef(drugs, cell_lines)
drugs |
data.table with drugs |
cell_lines |
data.table with cell lines |
matrix with random hill coefficient
generate_hill_coef(create_synthetic_drugs(), create_synthetic_cell_lines())generate_hill_coef(create_synthetic_drugs(), create_synthetic_cell_lines())
Generate response data
generate_response_data(df_layout, noise_level = 0.1)generate_response_data(df_layout, noise_level = 0.1)
df_layout |
data.table that should contains the cell line, drug, concentration, and replicate columns along with the annotations that needs to be propagated |
noise_level |
numeric scalar with the level of noise added to the data |
data.table with response data
cell_lines <- create_synthetic_cell_lines() drugs <- create_synthetic_drugs() df_layout <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ]) generate_response_data(df_layout)cell_lines <- create_synthetic_cell_lines() drugs <- create_synthetic_drugs() df_layout <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ]) generate_response_data(df_layout)
Returns named vector of absolute paths to test datasets.
get_test_dataset_paths(datasets_dir = NULL, pattern = "finalMAE_")get_test_dataset_paths(datasets_dir = NULL, pattern = "finalMAE_")
datasets_dir |
path to directory with datasets (default |
pattern |
used to: (1) filter to qs2 files from the dataset_dir path and (2) prettify the labels of the files |
named vector of absolute paths
Kamil FoltyĆski [email protected]
get_test_dataset_paths() path <- system.file("testdata", package = "gDRtestData", mustWork = TRUE) get_test_dataset_paths(path)get_test_dataset_paths() path <- system.file("testdata", package = "gDRtestData", mustWork = TRUE) get_test_dataset_paths(path)
Create data.table with input co-dilution data containing noise for testing purposes
prepareCodilutionData( cell_lines, drugs, drugsIdx2 = 1, conc = 10^(seq(-3, 1, 0.5)), noise = 0.1 )prepareCodilutionData( cell_lines, drugs, drugsIdx2 = 1, conc = 10^(seq(-3, 1, 0.5)), noise = 0.1 )
cell_lines |
data.table with cell line info |
drugs |
data.table with drug info |
drugsIdx2 |
numeric vector of ids for secondary drug (in |
conc |
vector of doses |
noise |
number indicating level of noise |
data.table with input data for testing
prepareCodilutionData(create_synthetic_cell_lines()[seq_len(2), ], create_synthetic_drugs()[seq_len(4), ])prepareCodilutionData(create_synthetic_cell_lines()[seq_len(2), ], create_synthetic_drugs()[seq_len(4), ])
Create data.table with input combination data containing noise for testing purposes
prepareComboMergedData( cell_lines, drugs, drugsIdx1 = 2:4, drugsIdx2 = c(26, 26, 26), concentration = c(0, 0.2, 1), noise = 0.1, modifyDf2 = FALSE )prepareComboMergedData( cell_lines, drugs, drugsIdx1 = 2:4, drugsIdx2 = c(26, 26, 26), concentration = c(0, 0.2, 1), noise = 0.1, modifyDf2 = FALSE )
cell_lines |
data.table with cell line info |
drugs |
data.table with drug info |
drugsIdx1 |
numeric vector of ids for primary drug |
drugsIdx2 |
numeric vector of ids for secondary drug |
concentration |
numeric vector of doses |
noise |
number indicating level of noise |
modifyDf2 |
Boolean indicating if the table should me modified to keep reverse single agent data |
data.table with input data for testing
prepareComboMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())prepareComboMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())
Create data.table with input data for testing purposes
prepareData(cell_lines, drugs, conc = 10^(seq(-3, 1, 0.5)))prepareData(cell_lines, drugs, conc = 10^(seq(-3, 1, 0.5)))
cell_lines |
data.table with cell line info |
drugs |
data.table with drug info |
conc |
vector of doses |
data.table with input data for testing
prepareData(create_synthetic_cell_lines(), create_synthetic_drugs())prepareData(create_synthetic_cell_lines(), create_synthetic_drugs())
Create data.table with input data containing noise for testing purposes
prepareMergedData(cell_lines, drugs, noise = 0.1)prepareMergedData(cell_lines, drugs, noise = 0.1)
cell_lines |
data.table with cell line info |
drugs |
data.table with drug info |
noise |
number indicating level of noise |
data.table with input data for testing
prepareMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())prepareMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())