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apply_combo_sa_fits

Source: R/fit_utils.R
apply_combo_sa_fits.Rd

Fit single-agent dose-response curves for each co-treatment concentration in a combination SE and store the results in the Metrics assay. This is step 1+2 of the fit_SE.combinations pipeline:

Usage

apply_combo_sa_fits(
  se,
  series_identifiers = NULL,
  normalization_types = c("GR", "RV"),
  averaged_assay = "Averaged",
  metrics_assay = "Metrics",
  fit_source = "gDR"
)

Arguments

se

SummarizedExperiment with an Averaged assay (combination experiment).

series_identifiers

character vector of length 2: column names for the two concentration axes (e.g. c("Concentration", "Concentration_2")). Defaults to get_default_nested_identifiers(se, data_model("combination")).

normalization_types

character vector of normalization types to process. Default c("GR", "RV").

averaged_assay

string; name of the input assay. Default "Averaged".

metrics_assay

string; name of the output assay. Default "Metrics".

fit_source

string recorded in the fit_source column. Default "gDR".

Value

Updated SummarizedExperiment with a metrics_assay assay containing SA fit parameters per co-treatment concentration.

Details

  1. Standardise concentrations and build the complete dose-response matrix.

  2. Fit SA curves per co-treatment concentration via fit_combo_cotreatments() and fit_combo_codilutions().

  3. Compute smooth SA predictions at every combo point via map_ids_to_fits() and store them together with the SA fit parameters.

The resulting Metrics assay contains dilution_drug, cotrt_value, ec50, h, x_inf, x_0, and normalization_type — the same schema as produced by fit_SE.combinations().

See also

apply_combo_excess, apply_combo_isobolograms, apply_combo_scores, fit_SE.combinations

Examples

mae <- gDRutils::get_synthetic_data("finalMAE_combo_matrix_small")
combo_se <- mae[[gDRutils::get_supported_experiments("combo")]]
SummarizedExperiment::assays(combo_se) <-
  SummarizedExperiment::assays(combo_se)["Averaged"]
combo_se_fitted <- apply_combo_sa_fits(combo_se[1, 1])
#> Warning: overriding original x_0 argument '1' with '1' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '1' with '1' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.937720959525358' with '0.9563' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.411661143403392' with '0.4075' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '-0.466087101282658' with '-0.4678' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '-0.638711813664539' with '-0.5972' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '-0.65250302581797' with '-0.6296' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '-0.653485583857411' with '-0.692' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '-0.653555270837867' with '-0.7039' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '-0.653560191563928' with '-0.7046' (only 1 normalized value detected, setting constant fit)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: overriding original x_0 argument '1' with '1' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '1' with '1' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.960100165903769' with '0.966' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.578859775899146' with '0.577' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.123503648078634' with '0.1259' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.0689925257550951' with '0.0814' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.0658336504167383' with '0.0714' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.0656619818287103' with '0.0535' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.0656526460997796' with '0.0503' (only 1 normalized value detected, setting constant fit)
#> Warning: overriding original x_0 argument '0.0656521405542176' with '0.0501' (only 1 normalized value detected, setting constant fit)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
"Metrics" %in% SummarizedExperiment::assayNames(combo_se_fitted)
#> [1] TRUE