Model Gene Dose-Response Curve Using drc

Usage

compute_single_dose_response(
  data,
  gene = NULL,
  pathway = NULL,
  normalisation = "limma_voom",
  treatment_value,
  control_value = "DMSO",
  batch = 1,
  k = 2
)

Arguments

data: A Seurat or TidySeurat object containing expression and metadata.
gene: A gene name (must match a row name in the object).
pathway: A character string present in the list of enriched pathways.
normalisation: One of "raw", "logNorm", "cpm", "clr", "SCT", "DESeq2", "edgeR", "RUVg", "RUVs", "RUVr", "limma_voom", "zinb". If empty, defaults to cpm
treatment_value: A character string matching one value in metadata column "Treatment_1".
control_value: A character string matching one value in metadata column "Treatment_1".
batch: Either empty, a single value, or a vector corresponding to the number of samples
k: Parameter k for RUVSeq methods, check RUVSeq tutorial

Value

A list with drc model, predicted values, and ggplot curve

Examples

# \donttest{
data(mini_mac)
res <- compute_single_dose_response(data = mini_mac,
gene = "FSHR",
normalisation = "limma_voom",
treatment_value = "Camptothecin")
#> 
#> Estimated effective doses
#> 
#>        Estimate Std. Error   Lower   Upper
#> e:1:50   2.6528     2.3796 -2.2585  7.5641
res$plot

res <- compute_single_dose_response(data = mini_mac,
pathway = "p53 Pathway",
treatment_value = "Camptothecin")
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> 
#> Estimated effective doses
#> 
#>        Estimate Std. Error Lower Upper
#> e:1:50   3.4395    10.0000   NaN   NaN
res$plot

# }