% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/module5_drc.R
\name{plot_ic50_auc_cor}
\alias{plot_ic50_auc_cor}
\title{IC50 versus AUC scatter}
\usage{
plot_ic50_auc_cor(
  params,
  auc_col = "auc",
  ic50_col = "ic50",
  add_trend = TRUE,
  annotate_cor = TRUE
)
}
\arguments{
\item{params}{A data frame from \code{\link[=extract_drc_params]{extract_drc_params()}} joined with an \code{auc}
column (for example \code{merge(extract_drc_params(drc), calc_drc_auc(drc)$meta)}),
or the \code{meta} of an \code{hts_drc} after \code{\link[=calc_drc_auc]{calc_drc_auc()}} plus IC50.}

\item{auc_col, ic50_col}{Column names. Default \code{"auc"}, \code{"ic50"}.}

\item{add_trend}{Add a linear regression trend line with a 95\% confidence
band. Default \code{TRUE}.}

\item{annotate_cor}{Annotate the Pearson correlation coefficient and p-value
on the plot. Default \code{TRUE}.}
}
\value{
A \link[ggplot2:ggplot]{ggplot2::ggplot} object.
}
\description{
Scatter plot of IC50 against AUC across curves, a compact view for triaging
strong, moderate and weak compounds. IC50 marks the potency (the
concentration for half-maximal effect) while AUC integrates potency \emph{and}
efficacy over the whole tested range; plotting them together shows whether the
two agree and flags compounds that are potent but only weakly efficacious (or
vice versa). A regression trend line and the Pearson correlation (computed on
the log10 IC50, matching the log x-axis) are drawn on the figure by default.
}
\examples{
drc <- calc_drc_auc(fit_4pl_curve(import_dose_response(hts_dose_response,
  response_col = "viability", group_cols = "cell_line"),
  group_cols = "cell_line"))
params <- merge(extract_drc_params(drc), drc$meta[, c("curve_id", "auc")])
plot_ic50_auc_cor(params)

}
\seealso{
\code{\link[=extract_drc_params]{extract_drc_params()}}, \code{\link[=calc_drc_auc]{calc_drc_auc()}}
}
