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

if (FALSE) { # \dontrun{
rds_file<-system.file("/extdata/PMMSq033/PMMSq033.rds", package = "macpie")
mac<-readRDS(rds_file)
res <- compute_single_dose_response(data = mac,
gene = "PTPRA",
normalisation = "limma_voom",
treatment_value = "Camptothecin")
res$plot
res <- compute_single_dose_response(data = mac,
pathway = "Myc Targets V1",
treatment_value = "Camptothecin")
res$plot
} # }