Model Gene Dose-Response Curves Across Treatments — compute_multiple_dose

This function fits dose-response models for a set of genes across different treatments using the drc package. It returns EC50 values per gene per treatment.

Usage

compute_multiple_dose_response(
  data,
  genes = NULL,
  normalisation = "limma_voom",
  control_value = "DMSO",
  batch = 1,
  k = 2,
  num_cores = 1
)

Arguments

data: A Seurat or TidySeurat object containing expression data and metadata.
genes: A character vector of gene names to model. If NULL, all significant DE genes across comparisons are used.
normalisation: A character string indicating the normalization method. One of: "raw", "logNorm", "cpm", "clr", "SCT", "DESeq2", "edgeR", "RUVg", "RUVs", "RUVr", "limma_voom", "zinb". Default is "limma_voom".
control_value: A string indicating the control condition in "Treatment_1". Default is "DMSO".
batch: Batch variable to use for normalization if applicable. Default is 1.
k: Number of unwanted factors for RUV normalization. Default is 2.
num_cores: Number of CPU cores to use in parallel model fitting. Default is 1.

Value

A data frame of EC50 values per gene and treatment.

Examples

if (FALSE) { # \dontrun{
rds_file <- system.file("extdata/PMMSq033/PMMSq033.rds", package = "macpie")
mac <- readRDS(rds_file)
res <- compute_multiple_dose_response(
  data = mac,
  genes = c("PTPRA", "MYC"),
  normalisation = "limma_voom",
  treatment_value = "Camptothecin",
  num_cores = 2
)
head(res)
} # }