R/data.R

In christianbromley/raptor: Estimating Renal Anti-Pd(l)1 probabiliTy Of Response scores from gene expression data

#' Coefficients for 4 predictors of immunotherapy benefit
#'
#' The coefficents required to generate the 4 probability of response scores.
#'
#' @format A list of 4 named numeric vectors.
#' \describe{
#'  \item{coefficients}{Numeric values giving the weight of each gene within each signature}
#'
#' }
#' @source
"coefficients"

#' Constants for 4 predictors of immunotherapy benefit
#'
#' The constants required to generate the 4 probability of response scores.
#'
#' @format A list of 4 named numeric vectors.
#' \describe{
#'  \item{constant_list}{Numeric values giving the weight of each gene within each signature. Note the COXIS model contains a couple of 0 coefficients. Ignore these.}
#'
#' }
#' @source
"constant_list"

#' Proportions of response/non-response to stratify based on 4 predictors of immunotherapy benefit
#'
#' The proportions of responders/NRs required to stratify based on the 4 probability of response scores.
#' These proportions derive from cutoffs optimised by take the threshold related to the maximum value of the mean of sensitivity and specificity.
#' @format A list of 4 named numeric vectors.
#' \describe{
#'  \item{proportions}{Numeric values giving the weight of each gene within each signature}
#'
#' }
#' @source
"proportions"

#' Cut-off values of predictor scores based on log2(TPM+1) gene expression data. Should not be used for expression data using other normalisation methods.
#'
#' These cutoff values were derived from the maximum value of the mean of sensitivity and specificity.
#'
#' @format A list of 4 numeric values.
#' \describe{
#'  \item{log2tpmplusone_cutoffs}{Numeric values giving the cutoff to define high vs low probability of response groups}
#'
#' }
#' @source
"log2tpmplusone_cutoffs"

#' Proportions of response/non-response to stratify based on 4 predictors of immunotherapy benefit
#'
#' The proportions of responders/NRs required to stratify based on the 4 probability of response scores.
#' These proportions derive from cutoffs optimised by taking the threshold at the maximum value of specificity whilst sensitivity was equal to 1, thereby minimising false negatives.
#' @format A list of 4 named numeric vectors.
#' \describe{
#'  \item{proportions_sens}{Numeric values giving the weight of each gene within each signature}
#'
#' }
#' @source
"proportions_sens"

#' Cut-off values of predictor scores based on log2(TPM+1) gene expression data. Should not be used for expression data using other normalisation methods.
#'
#' These cutoff values were derived from the maximum value of the specificity, with a sensitivity equal to 1 (so that no false negatives exist)
#'
#' @format A list of 4 numeric values.
#' \describe{
#'  \item{log2tpmplusone_cutoffs_sens}{Numeric values giving the cutoff to define high vs low probability of response groups}
#'
#' }
#' @source
"log2tpmplusone_cutoffs_sens"