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apply_combo_excess

Source: R/fit_utils.R
apply_combo_excess.Rd

Compute per-point HSA and Bliss excess values for a combination SE, writing the result to the excess assay. This is step 3 of the fit_SE.combinations pipeline.

Usage

apply_combo_excess(
  se,
  series_identifiers = NULL,
  normalization_types = c("GR", "RV"),
  averaged_assay = "Averaged",
  metrics_assay = "Metrics",
  excess_assay = "excess"
)

Arguments

se

SummarizedExperiment with Averaged and Metrics assays.

series_identifiers

character vector of length 2: concentration column names. Defaults to get_default_nested_identifiers(se, ...).

normalization_types

character vector. Default c("GR", "RV").

averaged_assay

string. Default "Averaged".

metrics_assay

string. Default "Metrics".

excess_assay

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

Value

Updated SE with an excess_assay assay containing per-point smooth, hsa_excess, and bliss_excess columns.

Details

Requires the Averaged and Metrics assays to be present (the latter as produced by apply_combo_sa_fits or fit_SE.combinations).

See also

apply_combo_sa_fits, 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")]]
combo_se_excess <- apply_combo_excess(combo_se[1, 1])
#> Loading required namespace: BumpyMatrix
"excess" %in% SummarizedExperiment::assayNames(combo_se_excess)
#> [1] TRUE