R/mc5_mthds.R
In USEPA/CompTox-ToxCast-tcpl: ToxCast Data Analysis Pipeline
Defines functions
mc5_mthds
Documented in
mc5_mthds
#-------------------------------------------------------------------------------
# mc5_mthds: Load list of cutoff methods (to be used at level 5).
#-------------------------------------------------------------------------------
#' @name MC5_Methods
#' @title Load list of level 5 multiple-concentration cutoff methods
#'
#' @description
#' \code{mc5_mthds} returns a list of additional activity cutoff methods to be used during level 5
#' multiple-concentration processing.
#'
#' @param ae Integer of length 1, the assay endpoint id
#'
#' @return A list of functions
#'
#' @seealso \code{\link{mc5}}, \code{\link{Method functions}} to query what methods get applied to
#' each aeid.
#'
#' @details
#' The functions contained in the list returned by \code{mc5_mthds} take \code{aeids}
#' (a numeric vector of aeid values) and returns a list of expressions to be executed in the
#' \code{mc5} (not exported) function environment. The functions are described here for reference
#' purposes, The \code{mc5_mthds} function is not exported, nor is it intended for use.
#'
#' All available methods are described in the "Available Methods" section, listed
#' by the cutoff type in ascending order of cutoff value.
#'
#' @section Available Methods:
#'
#' The methods are broken down into five categories based on the type of cutoff they assign.
#' Different methods are used to define cutoffs for "bmad" (baseline median absolute value), "fc"
#' (fold change), "log" (\eqn{\log_{2}}{log2} or \eqn{\log_{10}}{log10}), "pc" (percent of
#' control), and "other" (uncategorized cutoffs).
#'
#' 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 5 multiple-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 value (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) as defined at Level 4.}
#' \item{bmad4}{Add a cutoff value of 4 multiplied 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{Fold Change Methods}{
#' \describe{
#' \item{fc0.2}{Add a cutoff value of 0.2. Typically for zero centered fold change data.}
#' \item{fc0.25}{Add a cutoff value of 0.25. Typically for zero centered fold change data.}
#' \item{fc0.3}{Add a cutoff value of 0.3. Typically for zero centered fold change data.}
#' \item{fc0.5}{Add a cutoff value of 0.5. Typically for zero centered fold change data.}
#' }
#' }
#'
#' \subsection{Log Methods}{
#' Log Base 2
#' \describe{
#' \item{neglog2_0.88}{Add a cutoff value of \eqn{-\log_{2}{0.88}}{-log2(0.88)}.}
#' \item{log2_1.2}{Add a cutoff value of \eqn{\log_{2}{1.2}}{log2(1.2)}. Typically for fold
#' change data.}
#' \item{log2_2}{Add a cutoff value \eqn{\log_{2}{2}}{log2(2)}. 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.}
#' \item{log10_2}{Add a cutoff value of \eqn{\log_{10}{2}}{log10(2)}. Typically for fold change
#' data.}
#' }
#' }
#'
#' \subsection{Percent of Control Methods}{
#' \describe{
#' \item{pc05}{Add a cutoff value of 5. Typically for percent of control data.}
#' \item{pc10}{Add a cutoff value of 10. 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.}
#' \item{pc40}{Add a cutoff value of 40. Typically for percent of control data.}
#' \item{pc50}{Add a cutoff value of 50. Typically for percent of control data.}
#' \item{pc70}{Add a cutoff value of 70. Typically for percent of control data.}
#' \item{pc95}{Add a cutoff value of 95. Typically for percent of control data.}
#' }
#' }
#'
#' \subsection{Other Methods}{
#' \describe{
#' \item{maxmed20pct}{Add a cutoff value of 20 percent of the maximum of all endpoint maximal
#' average response values (max_med).}
#' \item{coff_2.32}{Add a cutoff value of 2.32.}
#' \item{loec.coff}{Method not yet updated for tcpl implementation. Identify the lowest observed
#' effective concentration (loec) compared to baseline.}
#' \item{ow_bidirectional_loss}{Multiply winning model hitcall (hitc) by -1 for models fit in the
#' positive analysis direction. Typically used for endpoints where only negative responses are
#' biologically relevant.}
#' \item{ow_bidirectional_gain}{Multiply winning model hitcall (hitc) by -1 for models fit in the
#' negative analysis direction. Typically used for endpoints where only positive responses are
#' biologically relevant.}
#' \item{osd_coff_bmr}{Overwrite the osd value so that bmr equals cutoff.}
#' }
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
mc5_mthds <- function(ae) {
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)
},
maxmed20pct = function() {
e1 <- bquote(coff <- c(coff, dat[ , max(max_med)*.20]))
list(e1)
},
pc70 = function() {
e1 <- bquote(coff <- c(coff, 70))
list(e1)
},
pc50 = function() {
e1 <- bquote(coff <- c(coff, 50))
list(e1)
},
log2_2 = function() {
e1 <- bquote(coff <- c(coff, log2(2)))
list(e1)
},
log10_2 = function() {
e1 <- bquote(coff <- c(coff, log10(2)))
list(e1)
},
neglog2_0.88 = function() {
e1 <- bquote(coff <- c(coff, -1*log2(0.88)))
list(e1)
},
coff_2.32 = function() {
e1 <- bquote(coff <- c(coff, 2.32))
list(e1)
},
fc0.2 = function() {
e1 <- bquote(coff <- c(coff, 0.2))
list(e1)
},
fc0.3 = function() {
e1 <- bquote(coff <- c(coff, 0.3))
list(e1)
},
pc25 = function() {
e1 <- bquote(coff <- c(coff, 25))
list(e1)
},
pc30 = function() {
e1 <- bquote(coff <- c(coff, 30))
list(e1)
},
bmad1 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)]))
list(e1)
},
bmad2 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*2]))
list(e1)
},
pc10 = function() {
e1 <- bquote(coff <- c(coff, 10))
list(e1)
},
pc05 = function() {
e1 <- bquote(coff <- c(coff, 5))
list(e1)
},
pc95 = function() {
e1 <- bquote(coff <- c(coff, 95))
list(e1)
},
bmad4 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*4]))
list(e1)
},
fc0.5 = function() {
e1 <- bquote(coff <- c(coff, 0.5))
list(e1)
},
pc16 = function() {
e1 <- bquote(coff <- c(coff, 16))
list(e1)
},
ow_bidirectional_loss = function() {
# get all endpoint sample m4ids where the top param is greater than 0
e1 <- bquote(top.gt0.m4ids <- dat[(hit_param %in% c("tp", "top")) & hit_val > 0, unique(m4id)])
# set hitcall param and hitc to -1 if found in m4id list
e2 <- bquote(dat$hit_val[dat$m4id %in% top.gt0.m4ids & dat$hit_param == "hitcall"] <- dat$hit_val[dat$m4id %in% top.gt0.m4ids & dat$hit_param == "hitcall"] * -1)
e3 <- bquote(dat$hitc[dat$m4id %in% top.gt0.m4ids] <- dat$hitc[dat$m4id %in% top.gt0.m4ids] * -1)
e4 <- bquote(dat$model_type <- 4)
list(e1, e2, e3, e4)
},
ow_bidirectional_gain = function() {
# get all endpoint sample m4ids where the top param is less than 0
e1 <- bquote(top.lt0.m4ids <- dat[(hit_param %in% c("tp", "top")) & hit_val < 0, unique(m4id)])
# set hitcall param and hitc to -1 if found in m4id list
e2 <- bquote(dat$hit_val[dat$m4id %in% top.lt0.m4ids & dat$hit_param == "hitcall"] <- dat$hit_val[dat$m4id %in% top.lt0.m4ids & dat$hit_param == "hitcall"] * -1)
e3 <- bquote(dat$hitc[dat$m4id %in% top.lt0.m4ids] <- dat$hitc[dat$m4id %in% top.lt0.m4ids] * -1)
e4 <- bquote(dat$model_type <- 3)
list(e1, e2, e3, e4)
},
osd_coff_bmr = function() {
# set the osd param so that bmr == coff
e1 <- bquote(dat[ , all_onesd := coff/1.349 ])
list(e1)
},
fc0.25 = function() {
e1 <- bquote(coff <- c(coff, 0.25))
list(e1)
},
pc40 = function() {
e1 <- bquote(coff <- c(coff, 40))
list(e1)
}
)
}
#-------------------------------------------------------------------------------
USEPA/CompTox-ToxCast-tcpl documentation built on May 2, 2024, 2:25 p.m.
R Package Documentation
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Defines functions mc5_mthds
Documented in mc5_mthds
#-------------------------------------------------------------------------------
# mc5_mthds: Load list of cutoff methods (to be used at level 5).
#-------------------------------------------------------------------------------
#' @name MC5_Methods
#' @title Load list of level 5 multiple-concentration cutoff methods
#'
#' @description
#' \code{mc5_mthds} returns a list of additional activity cutoff methods to be used during level 5
#' multiple-concentration processing.
#'
#' @param ae Integer of length 1, the assay endpoint id
#'
#' @return A list of functions
#'
#' @seealso \code{\link{mc5}}, \code{\link{Method functions}} to query what methods get applied to
#' each aeid.
#'
#' @details
#' The functions contained in the list returned by \code{mc5_mthds} take \code{aeids}
#' (a numeric vector of aeid values) and returns a list of expressions to be executed in the
#' \code{mc5} (not exported) function environment. The functions are described here for reference
#' purposes, The \code{mc5_mthds} function is not exported, nor is it intended for use.
#'
#' All available methods are described in the "Available Methods" section, listed
#' by the cutoff type in ascending order of cutoff value.
#'
#' @section Available Methods:
#'
#' The methods are broken down into five categories based on the type of cutoff they assign.
#' Different methods are used to define cutoffs for "bmad" (baseline median absolute value), "fc"
#' (fold change), "log" (\eqn{\log_{2}}{log2} or \eqn{\log_{10}}{log10}), "pc" (percent of
#' control), and "other" (uncategorized cutoffs).
#'
#' 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 5 multiple-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 value (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) as defined at Level 4.}
#' \item{bmad4}{Add a cutoff value of 4 multiplied 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{Fold Change Methods}{
#' \describe{
#' \item{fc0.2}{Add a cutoff value of 0.2. Typically for zero centered fold change data.}
#' \item{fc0.25}{Add a cutoff value of 0.25. Typically for zero centered fold change data.}
#' \item{fc0.3}{Add a cutoff value of 0.3. Typically for zero centered fold change data.}
#' \item{fc0.5}{Add a cutoff value of 0.5. Typically for zero centered fold change data.}
#' }
#' }
#'
#' \subsection{Log Methods}{
#' Log Base 2
#' \describe{
#' \item{neglog2_0.88}{Add a cutoff value of \eqn{-\log_{2}{0.88}}{-log2(0.88)}.}
#' \item{log2_1.2}{Add a cutoff value of \eqn{\log_{2}{1.2}}{log2(1.2)}. Typically for fold
#' change data.}
#' \item{log2_2}{Add a cutoff value \eqn{\log_{2}{2}}{log2(2)}. 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.}
#' \item{log10_2}{Add a cutoff value of \eqn{\log_{10}{2}}{log10(2)}. Typically for fold change
#' data.}
#' }
#' }
#'
#' \subsection{Percent of Control Methods}{
#' \describe{
#' \item{pc05}{Add a cutoff value of 5. Typically for percent of control data.}
#' \item{pc10}{Add a cutoff value of 10. 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.}
#' \item{pc40}{Add a cutoff value of 40. Typically for percent of control data.}
#' \item{pc50}{Add a cutoff value of 50. Typically for percent of control data.}
#' \item{pc70}{Add a cutoff value of 70. Typically for percent of control data.}
#' \item{pc95}{Add a cutoff value of 95. Typically for percent of control data.}
#' }
#' }
#'
#' \subsection{Other Methods}{
#' \describe{
#' \item{maxmed20pct}{Add a cutoff value of 20 percent of the maximum of all endpoint maximal
#' average response values (max_med).}
#' \item{coff_2.32}{Add a cutoff value of 2.32.}
#' \item{loec.coff}{Method not yet updated for tcpl implementation. Identify the lowest observed
#' effective concentration (loec) compared to baseline.}
#' \item{ow_bidirectional_loss}{Multiply winning model hitcall (hitc) by -1 for models fit in the
#' positive analysis direction. Typically used for endpoints where only negative responses are
#' biologically relevant.}
#' \item{ow_bidirectional_gain}{Multiply winning model hitcall (hitc) by -1 for models fit in the
#' negative analysis direction. Typically used for endpoints where only positive responses are
#' biologically relevant.}
#' \item{osd_coff_bmr}{Overwrite the osd value so that bmr equals cutoff.}
#' }
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
mc5_mthds <- function(ae) {
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)
},
maxmed20pct = function() {
e1 <- bquote(coff <- c(coff, dat[ , max(max_med)*.20]))
list(e1)
},
pc70 = function() {
e1 <- bquote(coff <- c(coff, 70))
list(e1)
},
pc50 = function() {
e1 <- bquote(coff <- c(coff, 50))
list(e1)
},
log2_2 = function() {
e1 <- bquote(coff <- c(coff, log2(2)))
list(e1)
},
log10_2 = function() {
e1 <- bquote(coff <- c(coff, log10(2)))
list(e1)
},
neglog2_0.88 = function() {
e1 <- bquote(coff <- c(coff, -1*log2(0.88)))
list(e1)
},
coff_2.32 = function() {
e1 <- bquote(coff <- c(coff, 2.32))
list(e1)
},
fc0.2 = function() {
e1 <- bquote(coff <- c(coff, 0.2))
list(e1)
},
fc0.3 = function() {
e1 <- bquote(coff <- c(coff, 0.3))
list(e1)
},
pc25 = function() {
e1 <- bquote(coff <- c(coff, 25))
list(e1)
},
pc30 = function() {
e1 <- bquote(coff <- c(coff, 30))
list(e1)
},
bmad1 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)]))
list(e1)
},
bmad2 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*2]))
list(e1)
},
pc10 = function() {
e1 <- bquote(coff <- c(coff, 10))
list(e1)
},
pc05 = function() {
e1 <- bquote(coff <- c(coff, 5))
list(e1)
},
pc95 = function() {
e1 <- bquote(coff <- c(coff, 95))
list(e1)
},
bmad4 = function() {
e1 <- bquote(coff <- c(coff, dat[ , unique(bmad)*4]))
list(e1)
},
fc0.5 = function() {
e1 <- bquote(coff <- c(coff, 0.5))
list(e1)
},
pc16 = function() {
e1 <- bquote(coff <- c(coff, 16))
list(e1)
},
ow_bidirectional_loss = function() {
# get all endpoint sample m4ids where the top param is greater than 0
e1 <- bquote(top.gt0.m4ids <- dat[(hit_param %in% c("tp", "top")) & hit_val > 0, unique(m4id)])
# set hitcall param and hitc to -1 if found in m4id list
e2 <- bquote(dat$hit_val[dat$m4id %in% top.gt0.m4ids & dat$hit_param == "hitcall"] <- dat$hit_val[dat$m4id %in% top.gt0.m4ids & dat$hit_param == "hitcall"] * -1)
e3 <- bquote(dat$hitc[dat$m4id %in% top.gt0.m4ids] <- dat$hitc[dat$m4id %in% top.gt0.m4ids] * -1)
e4 <- bquote(dat$model_type <- 4)
list(e1, e2, e3, e4)
},
ow_bidirectional_gain = function() {
# get all endpoint sample m4ids where the top param is less than 0
e1 <- bquote(top.lt0.m4ids <- dat[(hit_param %in% c("tp", "top")) & hit_val < 0, unique(m4id)])
# set hitcall param and hitc to -1 if found in m4id list
e2 <- bquote(dat$hit_val[dat$m4id %in% top.lt0.m4ids & dat$hit_param == "hitcall"] <- dat$hit_val[dat$m4id %in% top.lt0.m4ids & dat$hit_param == "hitcall"] * -1)
e3 <- bquote(dat$hitc[dat$m4id %in% top.lt0.m4ids] <- dat$hitc[dat$m4id %in% top.lt0.m4ids] * -1)
e4 <- bquote(dat$model_type <- 3)
list(e1, e2, e3, e4)
},
osd_coff_bmr = function() {
# set the osd param so that bmr == coff
e1 <- bquote(dat[ , all_onesd := coff/1.349 ])
list(e1)
},
fc0.25 = function() {
e1 <- bquote(coff <- c(coff, 0.25))
list(e1)
},
pc40 = function() {
e1 <- bquote(coff <- c(coff, 40))
list(e1)
}
)
}
#-------------------------------------------------------------------------------
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