R/sc1_mthds.R
In daynefiler/tcpl: ToxCast Data Analysis Pipeline
#-------------------------------------------------------------------------------
# sc1_mthds: Load list of sc1 method functions
#-------------------------------------------------------------------------------
#' @name SC1_Methods
#' @title List of level 1 single-concentration normalization functions
#'
#' @description
#' \code{sc1_mthds} returns a list of functions to be used during level 1
#' single-concentration processing.
#'
#' @return A list functions
#'
#' @seealso \code{\link{sc1}}, \code{\link{Method functions}} to query what
#' methods get applied to each acid
#'
#' @details
#' The functions contained in the list returned by \code{sc1_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{sc1_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 into three types, based on what fields they define.
#' Different methods are used to define "bval" (the baseline value), "pval"
#' (the positive control value), and "resp" (the final response value).
#'
#' Although it does not say so specifically in each description, all methods
#' are applied by acid.
#'
#' More information about the level 3 single-concentration processing is
#' available in the package vignette, "Pipeline_Overview."
#'
#' \subsection{bval Methods}{
#' \describe{
#' \item{bval.apid.nwlls.med}{Calculate bval as the median of rval for
#' wells with wllt equal to "n," by apid.}
#' \item{bval.apid.twlls.med}{Calculate bval as the median of rval for
#' wells with wllt equal to "t," by apid.}
#' \item{bval.apid.tn.med}{Calculate bval as the median of rval for wells
#' with wllt equal to "t" or "n," by apid.}
#' }
#' }
#'
#' \subsection{pval Methods}{
#' \describe{
#' \item{pval.apid.pwlls.med}{Calculate pval as the median of rval for
#' wells with wllt equal to "p," by apid.}
#' \item{pval.apid.mwlls.med}{Calculate pval as the median of rval for
#' wells with wllt equal to "m," by apid.}
#' \item{pval.apid.medpcbyconc.max}{First calculate the median of rval for
#' wells with wllt equal to "p" or "c," by wllt, conc, and apid. Then
#' calculate pval as the maximum of the calculated medians, by apid.}
#' \item{pval.apid.medpcbyconc.min}{First calculate the median of rval for
#' wells with wllt equal to "p" or "c," by wllt, conc, and apid. Then
#' calculate pval as the minimum of the calculated medians, by apid.}
#' \item{pval.apid.medncbyconc.min}{First calculate the median of rval for
#' wells with wllt equal to "m" or "o," by wllt, conc, and apid. Then
#' calculate pval as the minimum of the calculated medians, by apid.}
#' \item{pval.zero}{Define pval as 0.}
#' }
#' }
#'
#' \subsection{resp Methods}{
#' \describe{
#' \item{resp.pc}{Calculate resp as \eqn{\frac{\mathit{rval} -
#' \mathit{bval}}{\mathit{pval} - \mathit{bval}}100}{(rval - bval)/(pval
#' - bval)*100}.}
#' \item{resp.fc}{Calculate resp as \eqn{\mathit{rval}/\mathit{bval}}{
#' rval/bval}.}
#' \item{resp.logfc}{Calculate resp as \eqn{\mathit{rval} - \mathit{bval}}{
#' rval - bval}.}
#' \item{resp.log2}{Take the logarithm of resp with base 2.}
#' \item{resp.multneg1}{Multiply resp by -1.}
#' \item{none}{Do no normalization; make resp equal to rval.}
#' }
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
sc1_mthds <- function() {
list(
bval.apid.nwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
bval := median(rval[wllt == "n"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
bval.apid.twlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
bval := median(rval[wllt == "t"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
bval.apid.tn.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
bval := median(rval[wllt %in% c("t", "n")],
na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
pval.apid.pwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
pval := median(rval[wllt == "p"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
pval.apid.or.aeid.pwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
pval := median(rval[wllt == "p"], na.rm = TRUE),
by = list(aeid, apid)])
e2 <- bquote(dat[J(.(aeids)),
temp := median(pval,
na.rm = TRUE),
by = list(aeid)])
e3 <- bquote(dat[aeid %in% .(aeids) & (is.na(pval) | is.infinite(pval)),
pval := temp,
by = list(aeid)])
e4 <- bquote(dat[ , temp := NULL])
list(e1,e2,e3,e4)
},
pval.apid.mwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
pval := median(rval[wllt == "m"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
pval.apid.medpcbyconc.max = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
temp := median(rval[wllt %in% c("c", "p")],
na.rm = TRUE),
by = list(aeid, apid, wllt, conc)])
e2 <- bquote(dat[J(.(aeids)),
pval := max(temp, na.rm = TRUE),
by = list(aeid, apid)])
e3 <- bquote(dat[ , temp := NULL])
list(e1, e2, e3)
},
pval.apid.medpcbyconc.min = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
temp := median(rval[wllt %in% c("c", "p")],
na.rm = TRUE),
by = list(aeid, apid, wllt, conc)])
e2 <- bquote(dat[J(.(aeids)),
pval := min(temp, na.rm = TRUE),
by = list(aeid, apid)])
e3 <- bquote(dat[ , temp := NULL])
list(e1, e2, e3)
},
pval.apid.medncbyconc.min = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
temp := median(rval[wllt %in% c("m","o")],
na.rm = TRUE),
by = list(aeid, apid, wllt, conc)])
e2 <- bquote(dat[J(.(aeids)),
pval := min(temp, na.rm = TRUE),
by = list(aeid, apid)])
e3 <- bquote(dat[ , temp := NULL])
list(e1, e2, e3)
},
pval.zero = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), pval := 0])
list(e1)
},
resp.pc = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := (rval - bval)/(pval - bval)*100])
list(e1)
},
resp.fc = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := rval/bval])
list(e1)
},
resp.logfc = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := rval - bval])
list(e1)
},
resp.log2 = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := log2(resp)])
list(e1)
},
none = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := rval])
list(e1)
},
resp.multneg1 = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := resp * -1])
list(e1)
}
)
}
#-------------------------------------------------------------------------------
daynefiler/tcpl documentation built on May 15, 2019, 1:18 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#-------------------------------------------------------------------------------
# sc1_mthds: Load list of sc1 method functions
#-------------------------------------------------------------------------------
#' @name SC1_Methods
#' @title List of level 1 single-concentration normalization functions
#'
#' @description
#' \code{sc1_mthds} returns a list of functions to be used during level 1
#' single-concentration processing.
#'
#' @return A list functions
#'
#' @seealso \code{\link{sc1}}, \code{\link{Method functions}} to query what
#' methods get applied to each acid
#'
#' @details
#' The functions contained in the list returned by \code{sc1_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{sc1_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 into three types, based on what fields they define.
#' Different methods are used to define "bval" (the baseline value), "pval"
#' (the positive control value), and "resp" (the final response value).
#'
#' Although it does not say so specifically in each description, all methods
#' are applied by acid.
#'
#' More information about the level 3 single-concentration processing is
#' available in the package vignette, "Pipeline_Overview."
#'
#' \subsection{bval Methods}{
#' \describe{
#' \item{bval.apid.nwlls.med}{Calculate bval as the median of rval for
#' wells with wllt equal to "n," by apid.}
#' \item{bval.apid.twlls.med}{Calculate bval as the median of rval for
#' wells with wllt equal to "t," by apid.}
#' \item{bval.apid.tn.med}{Calculate bval as the median of rval for wells
#' with wllt equal to "t" or "n," by apid.}
#' }
#' }
#'
#' \subsection{pval Methods}{
#' \describe{
#' \item{pval.apid.pwlls.med}{Calculate pval as the median of rval for
#' wells with wllt equal to "p," by apid.}
#' \item{pval.apid.mwlls.med}{Calculate pval as the median of rval for
#' wells with wllt equal to "m," by apid.}
#' \item{pval.apid.medpcbyconc.max}{First calculate the median of rval for
#' wells with wllt equal to "p" or "c," by wllt, conc, and apid. Then
#' calculate pval as the maximum of the calculated medians, by apid.}
#' \item{pval.apid.medpcbyconc.min}{First calculate the median of rval for
#' wells with wllt equal to "p" or "c," by wllt, conc, and apid. Then
#' calculate pval as the minimum of the calculated medians, by apid.}
#' \item{pval.apid.medncbyconc.min}{First calculate the median of rval for
#' wells with wllt equal to "m" or "o," by wllt, conc, and apid. Then
#' calculate pval as the minimum of the calculated medians, by apid.}
#' \item{pval.zero}{Define pval as 0.}
#' }
#' }
#'
#' \subsection{resp Methods}{
#' \describe{
#' \item{resp.pc}{Calculate resp as \eqn{\frac{\mathit{rval} -
#' \mathit{bval}}{\mathit{pval} - \mathit{bval}}100}{(rval - bval)/(pval
#' - bval)*100}.}
#' \item{resp.fc}{Calculate resp as \eqn{\mathit{rval}/\mathit{bval}}{
#' rval/bval}.}
#' \item{resp.logfc}{Calculate resp as \eqn{\mathit{rval} - \mathit{bval}}{
#' rval - bval}.}
#' \item{resp.log2}{Take the logarithm of resp with base 2.}
#' \item{resp.multneg1}{Multiply resp by -1.}
#' \item{none}{Do no normalization; make resp equal to rval.}
#' }
#' }
#'
#' @note
#' This function is not exported and is not intended to be used by the user.
sc1_mthds <- function() {
list(
bval.apid.nwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
bval := median(rval[wllt == "n"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
bval.apid.twlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
bval := median(rval[wllt == "t"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
bval.apid.tn.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
bval := median(rval[wllt %in% c("t", "n")],
na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
pval.apid.pwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
pval := median(rval[wllt == "p"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
pval.apid.or.aeid.pwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
pval := median(rval[wllt == "p"], na.rm = TRUE),
by = list(aeid, apid)])
e2 <- bquote(dat[J(.(aeids)),
temp := median(pval,
na.rm = TRUE),
by = list(aeid)])
e3 <- bquote(dat[aeid %in% .(aeids) & (is.na(pval) | is.infinite(pval)),
pval := temp,
by = list(aeid)])
e4 <- bquote(dat[ , temp := NULL])
list(e1,e2,e3,e4)
},
pval.apid.mwlls.med = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
pval := median(rval[wllt == "m"], na.rm = TRUE),
by = list(aeid, apid)])
list(e1)
},
pval.apid.medpcbyconc.max = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
temp := median(rval[wllt %in% c("c", "p")],
na.rm = TRUE),
by = list(aeid, apid, wllt, conc)])
e2 <- bquote(dat[J(.(aeids)),
pval := max(temp, na.rm = TRUE),
by = list(aeid, apid)])
e3 <- bquote(dat[ , temp := NULL])
list(e1, e2, e3)
},
pval.apid.medpcbyconc.min = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
temp := median(rval[wllt %in% c("c", "p")],
na.rm = TRUE),
by = list(aeid, apid, wllt, conc)])
e2 <- bquote(dat[J(.(aeids)),
pval := min(temp, na.rm = TRUE),
by = list(aeid, apid)])
e3 <- bquote(dat[ , temp := NULL])
list(e1, e2, e3)
},
pval.apid.medncbyconc.min = function(aeids) {
e1 <- bquote(dat[J(.(aeids)),
temp := median(rval[wllt %in% c("m","o")],
na.rm = TRUE),
by = list(aeid, apid, wllt, conc)])
e2 <- bquote(dat[J(.(aeids)),
pval := min(temp, na.rm = TRUE),
by = list(aeid, apid)])
e3 <- bquote(dat[ , temp := NULL])
list(e1, e2, e3)
},
pval.zero = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), pval := 0])
list(e1)
},
resp.pc = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := (rval - bval)/(pval - bval)*100])
list(e1)
},
resp.fc = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := rval/bval])
list(e1)
},
resp.logfc = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := rval - bval])
list(e1)
},
resp.log2 = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := log2(resp)])
list(e1)
},
none = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := rval])
list(e1)
},
resp.multneg1 = function(aeids) {
e1 <- bquote(dat[J(.(aeids)), resp := resp * -1])
list(e1)
}
)
}
#-------------------------------------------------------------------------------
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