R/sc2_mthds.R
In USEPA/CompTox-ToxCast-tcpl: ToxCast Data Analysis Pipeline
Defines functions
sc2_mthds
Documented in
sc2_mthds
#-------------------------------------------------------------------------------
# sc2_mthds: Load list of sc2 method functions
#-------------------------------------------------------------------------------
#' @name SC2_Methods
#' @title List of level 2 single-concentration hit-call functions
#'
#' @description
#' \code{sc2_mthds} returns a list of functions to be used during level 2
#' single-concentration processing.
#'
#' @return A list functions
#'
#' @seealso \code{\link{sc2}}, \code{\link{Method functions}} to query what
#' methods get applied to each acid
#'
#' @details
#' The functions contained in the list returned by \code{sc2_mthds} return
#' a list of expressions to be executed in the \code{sc2} (not exported)
#' function environment. The functions are described here for reference
#' purposes, The \code{sc2_mthds} function is not exported, nor is it
#' intended for use.
#'
#' All available methods are described in the Available Methods section, listed
#' by the function/method name.
#'
#' @section Available Methods:
#' The methods are broken down into four categories based on the type of cutoff they assign.
#' Different methods are used to define cutoffs for "bmad" (baseline median absolute value), "pc"
#' (percent of control), "pc or bmad", "log" (\eqn{\log_{2}}{log2} or \eqn{\log_{10}}{log10}), and
#' "other" (uncategorized methods).
#'
#' All methods are applied by aeid.
#'
#' Although there are method exceptions (notably within the “other” category), only highest
#' calculated cutoff value based on assigned methods will be selected for hitcalling. Therefore,
#' only the largest cutoff method per method type should be assigned.
#'
#' More information about the level 2 single-concentration processing is available in the package
#' vignette, "Data_processing."
#'
#' \subsection{BMAD Methods}{
#' \describe{
#' \item{bmad1}{Add a cutoff value of 1 multiplied by baseline median absolute deviation (bmad).
#' By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad1.5}{Add a cutoff value of 1.5 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad2}{Add a cutoff value of 2 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad3}{Add a cutoff value of 3 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad5}{Add a cutoff value of 5 multiplied the baseline median absolute deviation (bmad).
#' By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad6}{Add a cutoff value of 6 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad10}{Add a cutoff value of 10 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' }
#'}
#'
#' \subsection{Percent of Control Methods}{
#' \describe{
#' \item{pc0.88}{Add a cutoff value of 0.88. Typically for percent of control data.}
#' \item{pc16}{Add a cutoff value of 16. Typically for percent of control data.}
#' \item{pc20}{Add a cutoff value of 20. Typically for percent of control data.}
#' \item{pc25}{Add a cutoff value of 25. Typically for percent of control data.}
#' \item{pc30}{Add a cutoff value of 30. Typically for percent of control data.}
#' }
#' }
#'
#' \subsection{Percent of Control or BMAD Methods}{
#' \describe{
#' \item{pc30orbmad3}{Add a cutoff value of either 30 or 3 multiplied by the baseline median
#' absolute deviation (bmad), whichever is less. By default, bmad is calculated using test
#' compound wells (wllt = t) for the endpoint.}
#' }
#' }
#'
#' \subsection{Log Methods}{
#' Log Base 2
#' \describe{
#' \item{log2_0.76}{Add a cutoff value of 0.76 for log2-transformed data. This was a custom
#' threshold value set for endpoint id 1690 (formerly aeid 1691).}
#' \item{log2_1.2}{Add a cutoff value of \eqn{log_{2}{1.2}}{log2(1.2)}. Typically for fold change
#' data.}
#' \item{log2_1.5}{Add a cutoff value of \eqn{log_{2}{1.5}}{log2(1.5)}. Typically for fold change
#' data.}
#' }
#' Log Base 10
#' \describe{
#' \item{log10_1.2}{Add a cutoff value of \eqn{log_{10}{1.2}}{log10(1.2)}. Typically for fold
#' change data.}
#' }
#' }
#'
#' \subsection{Other Methods}{
#' \describe{
#' \item{ow_bmad_nwells}{Overwrite the default baseline median absolute value (bmad) with a bmad
#' calculated using neutral control wells (wllt = n).}
#' \item{ow_bidirectional_gain}{Where responses only in the positive direction are biologically
#' relevant, overwrite the max_med and max_tmp values, which were calculated using absolute
#' value, to a calculation using a true maximum for uni-directional data.}
#' \item{ow_bidirectional_loss}{Where responses only in the negative direction are biologically
#' relevant, overwrite the max_med and max_tmp values, which were calculated using absolute
#' value, to a calculation using a true minimum for uni-directional data.}
#' }
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
sc2_mthds <- function() {
list(
bmad3 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*3]))
list(e1)
},
pc20 = function() {
e1 <- bquote(coff <- c(coff, 20))
list(e1)
},
log2_1.2 = function() {
e1 <- bquote(coff <- c(coff, log2(1.2)))
list(e1)
},
log10_1.2 = function() {
e1 <- bquote(coff <- c(coff, log10(1.2)))
list(e1)
},
bmad5 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*5]))
list(e1)
},
bmad6 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*6]))
list(e1)
},
bmad10 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*10]))
list(e1)
},
pc30orbmad3 = function() {
e1 <- bquote(coff <- c(coff, dat[ , min(30, unique(bmad)*3)]))
list(e1)
},
pc0.88 = function() {
e1 <- bquote(coff <- c(coff, 0.88))
list(e1)
},
log2_1.5 = function() {
e1 <- bquote(coff <- c(coff, log2(1.5)))
list(e1)
},
pc25 = function() {
e1 <- bquote(coff <- c(coff, 25))
list(e1)
},
ow_bmad_nwells = function() {
e1 <- bquote(dat[ , bmad := mad(resp[wllt == "n"], na.rm = TRUE)])
list(e1)
},
ow_bidirectional_gain = function() {
e1 <- bquote(dat[ , c("max_med","max_tmp") := list(max(tmp), tmp[which.max(tmp)]), by = spid])
list(e1)
},
ow_bidirectional_loss = function() {
e1 <- bquote(dat[ , c("max_med","max_tmp") := list(abs(min(tmp)), tmp[which.min(tmp)]), by = spid])
list(e1)
},
bmad2 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*2]))
list(e1)
},
log2_0.76 = function() {
e1 <- bquote(coff <- c(coff, 0.76))
list(e1)
},
bmad1 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)]))
list(e1)
},
pc30 = function() {
e1 <- bquote(coff <- c(coff, 30))
list(e1)
},
bmad1.5 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*1.5]))
list(e1)
},
pc16 = function() {
e1 <- bquote(coff <- c(coff, 16))
list(e1)
}
)
}
#-------------------------------------------------------------------------------
USEPA/CompTox-ToxCast-tcpl documentation built on April 22, 2024, 12:37 p.m.
R Package Documentation
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Defines functions sc2_mthds
Documented in sc2_mthds
#-------------------------------------------------------------------------------
# sc2_mthds: Load list of sc2 method functions
#-------------------------------------------------------------------------------
#' @name SC2_Methods
#' @title List of level 2 single-concentration hit-call functions
#'
#' @description
#' \code{sc2_mthds} returns a list of functions to be used during level 2
#' single-concentration processing.
#'
#' @return A list functions
#'
#' @seealso \code{\link{sc2}}, \code{\link{Method functions}} to query what
#' methods get applied to each acid
#'
#' @details
#' The functions contained in the list returned by \code{sc2_mthds} return
#' a list of expressions to be executed in the \code{sc2} (not exported)
#' function environment. The functions are described here for reference
#' purposes, The \code{sc2_mthds} function is not exported, nor is it
#' intended for use.
#'
#' All available methods are described in the Available Methods section, listed
#' by the function/method name.
#'
#' @section Available Methods:
#' The methods are broken down into four categories based on the type of cutoff they assign.
#' Different methods are used to define cutoffs for "bmad" (baseline median absolute value), "pc"
#' (percent of control), "pc or bmad", "log" (\eqn{\log_{2}}{log2} or \eqn{\log_{10}}{log10}), and
#' "other" (uncategorized methods).
#'
#' All methods are applied by aeid.
#'
#' Although there are method exceptions (notably within the “other” category), only highest
#' calculated cutoff value based on assigned methods will be selected for hitcalling. Therefore,
#' only the largest cutoff method per method type should be assigned.
#'
#' More information about the level 2 single-concentration processing is available in the package
#' vignette, "Data_processing."
#'
#' \subsection{BMAD Methods}{
#' \describe{
#' \item{bmad1}{Add a cutoff value of 1 multiplied by baseline median absolute deviation (bmad).
#' By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad1.5}{Add a cutoff value of 1.5 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad2}{Add a cutoff value of 2 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad3}{Add a cutoff value of 3 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad5}{Add a cutoff value of 5 multiplied the baseline median absolute deviation (bmad).
#' By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad6}{Add a cutoff value of 6 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' \item{bmad10}{Add a cutoff value of 10 multiplied by the baseline median absolute deviation
#' (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.}
#' }
#'}
#'
#' \subsection{Percent of Control Methods}{
#' \describe{
#' \item{pc0.88}{Add a cutoff value of 0.88. Typically for percent of control data.}
#' \item{pc16}{Add a cutoff value of 16. Typically for percent of control data.}
#' \item{pc20}{Add a cutoff value of 20. Typically for percent of control data.}
#' \item{pc25}{Add a cutoff value of 25. Typically for percent of control data.}
#' \item{pc30}{Add a cutoff value of 30. Typically for percent of control data.}
#' }
#' }
#'
#' \subsection{Percent of Control or BMAD Methods}{
#' \describe{
#' \item{pc30orbmad3}{Add a cutoff value of either 30 or 3 multiplied by the baseline median
#' absolute deviation (bmad), whichever is less. By default, bmad is calculated using test
#' compound wells (wllt = t) for the endpoint.}
#' }
#' }
#'
#' \subsection{Log Methods}{
#' Log Base 2
#' \describe{
#' \item{log2_0.76}{Add a cutoff value of 0.76 for log2-transformed data. This was a custom
#' threshold value set for endpoint id 1690 (formerly aeid 1691).}
#' \item{log2_1.2}{Add a cutoff value of \eqn{log_{2}{1.2}}{log2(1.2)}. Typically for fold change
#' data.}
#' \item{log2_1.5}{Add a cutoff value of \eqn{log_{2}{1.5}}{log2(1.5)}. Typically for fold change
#' data.}
#' }
#' Log Base 10
#' \describe{
#' \item{log10_1.2}{Add a cutoff value of \eqn{log_{10}{1.2}}{log10(1.2)}. Typically for fold
#' change data.}
#' }
#' }
#'
#' \subsection{Other Methods}{
#' \describe{
#' \item{ow_bmad_nwells}{Overwrite the default baseline median absolute value (bmad) with a bmad
#' calculated using neutral control wells (wllt = n).}
#' \item{ow_bidirectional_gain}{Where responses only in the positive direction are biologically
#' relevant, overwrite the max_med and max_tmp values, which were calculated using absolute
#' value, to a calculation using a true maximum for uni-directional data.}
#' \item{ow_bidirectional_loss}{Where responses only in the negative direction are biologically
#' relevant, overwrite the max_med and max_tmp values, which were calculated using absolute
#' value, to a calculation using a true minimum for uni-directional data.}
#' }
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
sc2_mthds <- function() {
list(
bmad3 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*3]))
list(e1)
},
pc20 = function() {
e1 <- bquote(coff <- c(coff, 20))
list(e1)
},
log2_1.2 = function() {
e1 <- bquote(coff <- c(coff, log2(1.2)))
list(e1)
},
log10_1.2 = function() {
e1 <- bquote(coff <- c(coff, log10(1.2)))
list(e1)
},
bmad5 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*5]))
list(e1)
},
bmad6 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*6]))
list(e1)
},
bmad10 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*10]))
list(e1)
},
pc30orbmad3 = function() {
e1 <- bquote(coff <- c(coff, dat[ , min(30, unique(bmad)*3)]))
list(e1)
},
pc0.88 = function() {
e1 <- bquote(coff <- c(coff, 0.88))
list(e1)
},
log2_1.5 = function() {
e1 <- bquote(coff <- c(coff, log2(1.5)))
list(e1)
},
pc25 = function() {
e1 <- bquote(coff <- c(coff, 25))
list(e1)
},
ow_bmad_nwells = function() {
e1 <- bquote(dat[ , bmad := mad(resp[wllt == "n"], na.rm = TRUE)])
list(e1)
},
ow_bidirectional_gain = function() {
e1 <- bquote(dat[ , c("max_med","max_tmp") := list(max(tmp), tmp[which.max(tmp)]), by = spid])
list(e1)
},
ow_bidirectional_loss = function() {
e1 <- bquote(dat[ , c("max_med","max_tmp") := list(abs(min(tmp)), tmp[which.min(tmp)]), by = spid])
list(e1)
},
bmad2 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*2]))
list(e1)
},
log2_0.76 = function() {
e1 <- bquote(coff <- c(coff, 0.76))
list(e1)
},
bmad1 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)]))
list(e1)
},
pc30 = function() {
e1 <- bquote(coff <- c(coff, 30))
list(e1)
},
bmad1.5 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*1.5]))
list(e1)
},
pc16 = function() {
e1 <- bquote(coff <- c(coff, 16))
list(e1)
}
)
}
#-------------------------------------------------------------------------------
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