R/Bliss.R

In hly89/synergyfinder: Calculate and Visualize Synergy Scores for Drug Combinations

#' Synergy score based on Bliss model
#'
#' A function to calculate synergy score based on Bliss model
#'
#' @param response.mat a dose-response matrix with concentrations as row names and column names
#' @param correction a parameter to specify if baseline correction is used or not. Defaults to TRUE.
#' @param Emin the minimal effect of the drug used in the 4-parameter log-logistic function to fit the dose-response 
#' curve. If it is not NA, it is fixed the value assigned by the user. Defaults to NA. It is used only when correction
#' is required.
#' @param Emax the maximal effect of the drug used in the 4-parameter log-logistic function to fit the dose-response 
#' curve. If it is not NA, it is fixed the value assigned by the user. Defaults to NA. It is used only when correction
#' is required.
#' @param nan.handle a parameter to specify if L.4 function or LL.4 function is used when fitting with LL.4 produces
#' NaNs.
#' @return A matrix of Bliss synergy scores for all the dose pairs for a drug combination. For a does pair with at least one zero concentration, 0 is used as the synergy score.
#' @author Liye He \email{liye.he@helsinki.fi}
#' @references Yadav B, Wennerberg K, Aittokallio T, Tang J. Searching for Drug Synergy in Complex Dose-Response Landscape Using an Interaction Potency Model.
#' Computational and Structural Biotechnology Journal 2015; 13: 504-513.
#' @examples
#' data("mathews_screening_data")
#' data <- ReshapeData(mathews_screening_data)
#' delta.score <- Bliss(data$dose.response.mats[[1]])
Bliss <- function(response.mat, correction = TRUE, Emin = NA, Emax = NA, nan.handle = c("LL4", "L4")) {
  if(correction) {
    # correct the response data
    response.mat <- BaselineCorrectionSD(response.mat, Emin = Emin, Emax = Emax, nan.handle)$corrected.mat
  }


  drug1.response <- response.mat[, 1]
  drug2.response <- response.mat[1, ]
  # reference matrix
  ref.mat <- response.mat
  for (i in 2:nrow(response.mat)) {
    for (j in 2:ncol(response.mat)) {
      ref.mat[i, j] <- drug1.response[i] + drug2.response[j] -
                              drug1.response[i] * drug2.response[j]/100
    }
  }
  # synergy matrix
  syn.mat <- response.mat - ref.mat
  syn.mat
}