Nothing
R/gridClassify.R
In twoddpcr: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules
#' @import methods
NULL
#' @title Use a 'grid' to create training data for classification algorithms.
#'
#' @description Classify droplets as "NN", "NP", "PN" or "PP". The
#' classification is based on upper bounds for negative readings and lower
#' bounds for positive readings; see the details and parameters for more
#' detail. If required (see the \code{trainingData} parameter), droplets that
#' are not classified will be given the label "N/A".
#'
#' @param droplets A \code{\link{ddpcrWell}} object or a data frame of droplet
#' amplitudes with columns \code{Ch1.Amplitude} and \code{Ch2.Amplitude}.
#' @param ch1NNThreshold The channel 1 upper bound for the NN class. Defaults
#' to 6500.
#' @param ch2NNThreshold The channel 2 upper bound for the NN class. Defaults
#' to 1900.
#' @param ch1NPThreshold The channel 1 upper bound for the NP class. Defaults
#' to 6500.
#' @param ch2NPThreshold The channel 2 lower bound for the NP class. Defaults
#' to 5000.
#' @param ch1PNThreshold The channel 1 lower bound for the PN class. Defaults
#' to 10000.
#' @param ch2PNThreshold The channel 2 upper bound for the PN class. Defaults
#' to 2900.
#' @param ch1PPThreshold The channel 1 lower bound for the PP class. Defaults
#' to 7500.
#' @param ch2PPThreshold The channel 2 lower bound for the PP class. Defaults
#' to 5000.
#' @param ... Other options depending on the type of \code{droplets}.
#'
#' @details The \code{threshold} parameters correspond to those in the
#' following diagram:
#' \preformatted{Ch1 ^ | |
#' | | |
#' | PN | |
#' | | | PP
#' e|________| |
#' g|........:..|_________
#' | : :
#' c|.............._______
#' a|______ : : |
#' | | : : | NP
#' | NN | : : |
#' | | : : |
#' --------------------->
#' b f h d Ch2}
#' Specifically:
#' \describe{
#' \item{a:}{\code{ch1NNThreshold},}
#' \item{b:}{\code{ch2NNThreshold},}
#' \item{c:}{\code{ch1PNThreshold},}
#' \item{d:}{\code{ch2PNThreshold},}
#' \item{e:}{\code{ch1NPThreshold},}
#' \item{f:}{\code{ch2NPThreshold},}
#' \item{g:}{\code{ch1PPThreshold},}
#' \item{h:}{\code{ch2PPThreshold}.}
#' }
#'
#' @name gridClassify
#'
#' @seealso \code{\link{thresholdClassify}} is a special case of this
#' function.
#' @seealso \code{\link{removeDropletClasses}} retrieves a data frame with the
#' "N/A" (and "Rain") droplets removed. This can used for transforming
#' a grid-like classification into usable training data.
#'
#' @author Anthony Chiu, \email{anthony.chiu@cruk.manchester.ac.uk}
#'
#' @examples
#' ## Use a grid to set training data for a data frame.
#' sgCl <- gridClassify(KRASdata[["E03"]],
#' ch1NNThreshold=5700, ch2NNThreshold=1700,
#' ch1NPThreshold=5400, ch2NPThreshold=5700,
#' ch1PNThreshold=9700, ch2PNThreshold=2050,
#' ch1PPThreshold=7200, ch2PPThreshold=4800)
#' str(sgCl)
#'
#' ## For data frame only, we can set the trainingData flag to FALSE so that
#' ## the unclassified droplets are retained but labelled as "N/A"
#' sgCl <- gridClassify(KRASdata[["E03"]],
#' ch1NNThreshold=5700, ch2NNThreshold=1700,
#' ch1NPThreshold=5400, ch2NPThreshold=5700,
#' ch1PNThreshold=9700, ch2PNThreshold=2050,
#' ch1PPThreshold=7200, ch2PPThreshold=4800,
#' trainingData=FALSE)
#' dropletPlot(sgCl, cMethod="class")
#'
#' ## The same works for ddpcrWell objects.
#' aWell <- ddpcrWell(well=KRASdata[["E03"]])
#' aWell <- gridClassify(aWell,
#' ch1NNThreshold=5700, ch2NNThreshold=1700,
#' ch1NPThreshold=5400, ch2NPThreshold=5700,
#' ch1PNThreshold=9700, ch2PNThreshold=2050,
#' ch1PPThreshold=7200, ch2PPThreshold=4800)
#' str(aWell)
#'
#' ## ddpcrPlate objects work in exactly the same way.
#' krasPlate <- ddpcrPlate(wells=KRASdata)
#' krasPlate <- gridClassify(krasPlate)
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' ## The default classification method (column name) is 'gridClassify',
#' ## which may be a bit long. It can be changed.
#' krasPlate <- gridClassify(krasPlate, classMethodLabel="training")
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' @export
setGeneric(
"gridClassify",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
...) {
standardGeneric("gridClassify")
}
)
#' @rdname gridClassify
#'
#' @param trainingData Whether to use the output as training data. If
#' \code{TRUE}, returns the _full table_ with the "N/A" entries removed; if
#' \code{FALSE}, the "N/A" entries are retained. Taken to be \code{FALSE} if
#' \code{fullTable} is set to \code{FALSE}. Defaults to \code{TRUE}. Ignored
#' if \code{droplets} is not a data frame.
#' @param fullTable Whether to return a data frame including amplitude
#' figures. If \code{TRUE}, a data frame with columns \code{Ch1.Amplitude},
#' \code{Ch1.Amplitude} and \code{class} is returned. If \code{FALSE}, a factor
#' with levels in \code{ddpcr$classesRain} is returned, where each entry
#' corresponds to each row in \code{droplets} (and \code{trainingData} is
#' automatically set to \code{FALSE}). Defaults to \code{TRUE}. Ignored if
#' \code{droplets} is not a data frame.
#' @param naLabel The label to use for unclassified droplets. Should be either
#' ddpcr$na ("N/A") or ddpcr$rain ("Rain"). Defaults to ddpcr$rain.
#'
#' @return If \code{droplets} is a data frame, return a data frame or factor
#' (depending on the \code{trainingData} and \code{fullTable} parameters) with
#' a classification for droplets in the chosen regions.
#'
#' @exportMethod gridClassify
setMethod(
"gridClassify", "data.frame",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
trainingData=TRUE, fullTable=TRUE,
naLabel=ddpcr$rain) {
# Check that the thresholds are sensible.
thresholds <- c(ch1NNThreshold, ch2NNThreshold,
ch1NPThreshold, ch2NPThreshold,
ch1PNThreshold, ch2PNThreshold,
ch1PPThreshold, ch2PPThreshold)
if(!is.numeric(thresholds) || any(thresholds < 0)) {
stop("Thresholds should be nonnegative.")
}
if(ch1NNThreshold > ch1PNThreshold || ch2NNThreshold > ch2NPThreshold ||
(ch1NNThreshold > ch1PPThreshold && ch2NNThreshold > ch2PPThreshold) ||
(ch1NPThreshold > ch1PNThreshold && ch2NPThreshold < ch2PNThreshold) ||
ch1NPThreshold > ch1PPThreshold ||
ch2PNThreshold > ch2PPThreshold) {
stop("The thresholds overlap.")
}
# Set the classification of droplets that lie above or below the given
# thresholds.
df <- droplets[, c("Ch1.Amplitude", "Ch2.Amplitude")]
df$class <- ifelse(
df$Ch1.Amplitude < ch1NNThreshold & df$Ch2.Amplitude < ch2NNThreshold,
ddpcr$nn,
ifelse(
df$Ch1.Amplitude > ch1PNThreshold & df$Ch2.Amplitude < ch2PNThreshold,
ddpcr$pn,
ifelse(
df$Ch1.Amplitude < ch1NPThreshold & df$Ch2.Amplitude > ch2NPThreshold,
ddpcr$np,
ifelse(
df$Ch1.Amplitude > ch1PPThreshold & df$Ch2.Amplitude > ch2PPThreshold,
ddpcr$pp,
naLabel))))
# If we are setting training data, remove all rows not in the grid.
if(!fullTable) {
return(factor(df$class, levels=ddpcr$classesRain))
} else if(trainingData) {
df <- df[df$class != ddpcr$na, ]
}
df$class <- factor(df$class, levels=ddpcr$classesRain)
df
}
)
#' @rdname gridClassify
#'
#' @param classMethodLabel A name (as a character string) of the classification
#' method. Defaults to "grid".
#'
#' @return If \code{droplets} is a \code{ddpcrWell} object, return
#' a \code{ddpcrWell} object with the appropriate classification.
#'
#' @exportMethod gridClassify
setMethod("gridClassify", "ddpcrWell",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
classMethodLabel="grid",
naLabel=ddpcr$rain) {
df <- gridClassify(amplitudes(droplets),
ch1NNThreshold=ch1NNThreshold,
ch2NNThreshold=ch2NNThreshold,
ch1NPThreshold=ch1NPThreshold,
ch2NPThreshold=ch2NPThreshold,
ch1PNThreshold=ch1PNThreshold,
ch2PNThreshold=ch2PNThreshold,
ch1PPThreshold=ch1PPThreshold,
ch2PPThreshold=ch2PPThreshold,
trainingData=FALSE,
fullTable=TRUE,
naLabel=naLabel)
wellClassification(droplets, classMethodLabel) <- df$class
droplets
}
)
#' @rdname gridClassify
#'
#' @return If \code{droplets} is a \code{ddpcrPlate} object, return
#' a \code{ddpcrPlate} object with the appropriate classification.
#'
#' @exportMethod gridClassify
setMethod(
"gridClassify", "ddpcrPlate",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
classMethodLabel="grid",
naLabel=ddpcr$rain) {
df <- gridClassify(do.call(rbind, amplitudes(droplets)),
ch1NNThreshold=ch1NNThreshold,
ch2NNThreshold=ch2NNThreshold,
ch1NPThreshold=ch1NPThreshold,
ch2NPThreshold=ch2NPThreshold,
ch1PNThreshold=ch1PNThreshold,
ch2PNThreshold=ch2PNThreshold,
ch1PPThreshold=ch1PPThreshold,
ch2PPThreshold=ch2PPThreshold,
trainingData=FALSE,
fullTable=TRUE,
naLabel=naLabel)
plateClassification(droplets, cMethod=classMethodLabel) <- df$class
droplets
}
)
#' @title Set thresholds to classify droplets.
#'
#' @description Classify droplets as "NN", "NP", "PN" or "PP" depending on
#' whether they lie above or below given thresholds. This is illustrated in the
#' details and parameters for more detail.
#'
#' @param droplets A \code{\link{ddpcrWell}} object or a data frame of droplet
#' amplitudes with columns \code{Ch1.Amplitude} and \code{Ch2.Amplitude}.
#' @param ch1Threshold The channel 1 upper bound for the NN and NP classes.
#' Defaults to 8000
#' @param ch2Threshold The channel 2 upper bound for the NN and PN classes.
#' Defaults to 3000.
#' @param ... Other options depending on the type of \code{droplets}.
#'
#' @details The \code{threshold} parameters correspond to those in the
#' following diagram:
#' \preformatted{Ch1 ^ |
#' | |
#' | |
#' | PN | PP
#' | |
#' | |
#' a|__________|__________
#' | |
#' | |
#' | NN | NP
#' | |
#' | |
#' --------------------->
#' b Ch2}
#' Specifically:
#' \describe{
#' \item{a:}{\code{ch1Threshold},}
#' \item{b:}{\code{ch2Threshold}.}
#' }
#'
#' @seealso This function is a special case of \code{\link{gridClassify}}.
#'
#' @name thresholdClassify
#'
#' @author Anthony Chiu, \email{anthony.chiu@cruk.manchester.ac.uk}
#'
#' @examples
#' ## Use thresholds to set a classification for a data frame.
#' gCl <- thresholdClassify(KRASdata[["E03"]],
#' ch1Threshold=6789, ch2Threshold=3000)
#' str(gCl)
#'
#' ## The same works for ddpcrWell objects.
#' aWell <- ddpcrWell(well=KRASdata[["E03"]])
#' aWell <- thresholdClassify(aWell, ch1Threshold=6789, ch2Threshold=3000)
#' str(aWell)
#'
#' ## ddpcrPlate objects work in exactly the same way.
#' krasPlate <- ddpcrPlate(wells=KRASdata)
#' krasPlate <- thresholdClassify(krasPlate,
#' ch1Threshold=6789, ch2Threshold=3000)
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' ## The default classification method (column name) is 'threshold'. It can be
#' ## changed by providing a label.
#' krasPlate <- ddpcrPlate(wells=KRASdata)
#' krasPlate <- thresholdClassify(krasPlate,
#' ch1Threshold=6789, ch2Threshold=3000,
#' classMethodLabel="manual")
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' @export
setGeneric(
"thresholdClassify",
function(droplets, ch1Threshold=6500, ch2Threshold=2900, ...) {
standardGeneric("thresholdClassify")
}
)
#' @rdname thresholdClassify
#'
#' @param fullTable Whether to return a data frame including amplitude figures.
#' If \code{TRUE}, a data frame with columns \code{Ch1.Amplitude},
#' \code{Ch1.Amplitude} and \code{class} is returned. If \code{FALSE}, a factor
#' with levels in \code{ddpcr$classesRain} is returned, where each entry
#' corresponds to each row in \code{droplets} (and \code{trainingData} is
#' automatically set to \code{FALSE}). Defaults to \code{TRUE}.
#'
#' @return If \code{droplets} is a data frame, return a data frame or factor
#' (depending on the \code{trainingData} and \code{fullTable} parameters) with
#' a classification for droplets in the chosen regions.
#'
#' @exportMethod thresholdClassify
setMethod(
"thresholdClassify", "data.frame",
function(droplets, ch1Threshold=6500, ch2Threshold=2900, fullTable=TRUE) {
gridClassify(droplets,
ch1NNThreshold=ch1Threshold,
ch2NNThreshold=ch2Threshold,
ch1NPThreshold=ch1Threshold,
ch2NPThreshold=ch2Threshold,
ch1PNThreshold=ch1Threshold,
ch2PNThreshold=ch2Threshold,
ch1PPThreshold=ch1Threshold,
ch2PPThreshold=ch2Threshold,
trainingData=TRUE,
fullTable=fullTable)
}
)
#' @rdname thresholdClassify
#'
#' @param classMethodLabel A name (as a character string) of the classification
#' method. Defaults to "thresholds".
#'
#' @return If \code{droplets} is a \code{ddpcrWell} object, return
#' a \code{ddpcrWell} object with the appropriate classification.
#'
#' @exportMethod thresholdClassify
setMethod("thresholdClassify", "ddpcrWell",
function(droplets, ch1Threshold=6500, ch2Threshold=2900,
classMethodLabel="thresholds") {
gridClassify(droplets,
ch1NNThreshold=ch1Threshold,
ch2NNThreshold=ch2Threshold,
ch1NPThreshold=ch1Threshold,
ch2NPThreshold=ch2Threshold,
ch1PNThreshold=ch1Threshold,
ch2PNThreshold=ch2Threshold,
ch1PPThreshold=ch1Threshold,
ch2PPThreshold=ch2Threshold,
classMethodLabel=classMethodLabel)
}
)
#' @rdname thresholdClassify
#'
#' @return If \code{droplets} is a \code{ddpcrPlate} object, return
#' a \code{ddpcrPlate} object with the appropriate classification.
#'
#' @exportMethod thresholdClassify
setMethod("thresholdClassify",
"ddpcrPlate",
function(droplets, ch1Threshold=6500, ch2Threshold=2900,
classMethodLabel="thresholds") {
gridClassify(droplets,
ch1NNThreshold=ch1Threshold,
ch2NNThreshold=ch2Threshold,
ch1NPThreshold=ch1Threshold,
ch2NPThreshold=ch2Threshold,
ch1PNThreshold=ch1Threshold,
ch2PNThreshold=ch2Threshold,
ch1PPThreshold=ch1Threshold,
ch2PPThreshold=ch2Threshold,
classMethodLabel=classMethodLabel)
}
)
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#' @import methods
NULL
#' @title Use a 'grid' to create training data for classification algorithms.
#'
#' @description Classify droplets as "NN", "NP", "PN" or "PP". The
#' classification is based on upper bounds for negative readings and lower
#' bounds for positive readings; see the details and parameters for more
#' detail. If required (see the \code{trainingData} parameter), droplets that
#' are not classified will be given the label "N/A".
#'
#' @param droplets A \code{\link{ddpcrWell}} object or a data frame of droplet
#' amplitudes with columns \code{Ch1.Amplitude} and \code{Ch2.Amplitude}.
#' @param ch1NNThreshold The channel 1 upper bound for the NN class. Defaults
#' to 6500.
#' @param ch2NNThreshold The channel 2 upper bound for the NN class. Defaults
#' to 1900.
#' @param ch1NPThreshold The channel 1 upper bound for the NP class. Defaults
#' to 6500.
#' @param ch2NPThreshold The channel 2 lower bound for the NP class. Defaults
#' to 5000.
#' @param ch1PNThreshold The channel 1 lower bound for the PN class. Defaults
#' to 10000.
#' @param ch2PNThreshold The channel 2 upper bound for the PN class. Defaults
#' to 2900.
#' @param ch1PPThreshold The channel 1 lower bound for the PP class. Defaults
#' to 7500.
#' @param ch2PPThreshold The channel 2 lower bound for the PP class. Defaults
#' to 5000.
#' @param ... Other options depending on the type of \code{droplets}.
#'
#' @details The \code{threshold} parameters correspond to those in the
#' following diagram:
#' \preformatted{Ch1 ^ | |
#' | | |
#' | PN | |
#' | | | PP
#' e|________| |
#' g|........:..|_________
#' | : :
#' c|.............._______
#' a|______ : : |
#' | | : : | NP
#' | NN | : : |
#' | | : : |
#' --------------------->
#' b f h d Ch2}
#' Specifically:
#' \describe{
#' \item{a:}{\code{ch1NNThreshold},}
#' \item{b:}{\code{ch2NNThreshold},}
#' \item{c:}{\code{ch1PNThreshold},}
#' \item{d:}{\code{ch2PNThreshold},}
#' \item{e:}{\code{ch1NPThreshold},}
#' \item{f:}{\code{ch2NPThreshold},}
#' \item{g:}{\code{ch1PPThreshold},}
#' \item{h:}{\code{ch2PPThreshold}.}
#' }
#'
#' @name gridClassify
#'
#' @seealso \code{\link{thresholdClassify}} is a special case of this
#' function.
#' @seealso \code{\link{removeDropletClasses}} retrieves a data frame with the
#' "N/A" (and "Rain") droplets removed. This can used for transforming
#' a grid-like classification into usable training data.
#'
#' @author Anthony Chiu, \email{anthony.chiu@cruk.manchester.ac.uk}
#'
#' @examples
#' ## Use a grid to set training data for a data frame.
#' sgCl <- gridClassify(KRASdata[["E03"]],
#' ch1NNThreshold=5700, ch2NNThreshold=1700,
#' ch1NPThreshold=5400, ch2NPThreshold=5700,
#' ch1PNThreshold=9700, ch2PNThreshold=2050,
#' ch1PPThreshold=7200, ch2PPThreshold=4800)
#' str(sgCl)
#'
#' ## For data frame only, we can set the trainingData flag to FALSE so that
#' ## the unclassified droplets are retained but labelled as "N/A"
#' sgCl <- gridClassify(KRASdata[["E03"]],
#' ch1NNThreshold=5700, ch2NNThreshold=1700,
#' ch1NPThreshold=5400, ch2NPThreshold=5700,
#' ch1PNThreshold=9700, ch2PNThreshold=2050,
#' ch1PPThreshold=7200, ch2PPThreshold=4800,
#' trainingData=FALSE)
#' dropletPlot(sgCl, cMethod="class")
#'
#' ## The same works for ddpcrWell objects.
#' aWell <- ddpcrWell(well=KRASdata[["E03"]])
#' aWell <- gridClassify(aWell,
#' ch1NNThreshold=5700, ch2NNThreshold=1700,
#' ch1NPThreshold=5400, ch2NPThreshold=5700,
#' ch1PNThreshold=9700, ch2PNThreshold=2050,
#' ch1PPThreshold=7200, ch2PPThreshold=4800)
#' str(aWell)
#'
#' ## ddpcrPlate objects work in exactly the same way.
#' krasPlate <- ddpcrPlate(wells=KRASdata)
#' krasPlate <- gridClassify(krasPlate)
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' ## The default classification method (column name) is 'gridClassify',
#' ## which may be a bit long. It can be changed.
#' krasPlate <- gridClassify(krasPlate, classMethodLabel="training")
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' @export
setGeneric(
"gridClassify",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
...) {
standardGeneric("gridClassify")
}
)
#' @rdname gridClassify
#'
#' @param trainingData Whether to use the output as training data. If
#' \code{TRUE}, returns the _full table_ with the "N/A" entries removed; if
#' \code{FALSE}, the "N/A" entries are retained. Taken to be \code{FALSE} if
#' \code{fullTable} is set to \code{FALSE}. Defaults to \code{TRUE}. Ignored
#' if \code{droplets} is not a data frame.
#' @param fullTable Whether to return a data frame including amplitude
#' figures. If \code{TRUE}, a data frame with columns \code{Ch1.Amplitude},
#' \code{Ch1.Amplitude} and \code{class} is returned. If \code{FALSE}, a factor
#' with levels in \code{ddpcr$classesRain} is returned, where each entry
#' corresponds to each row in \code{droplets} (and \code{trainingData} is
#' automatically set to \code{FALSE}). Defaults to \code{TRUE}. Ignored if
#' \code{droplets} is not a data frame.
#' @param naLabel The label to use for unclassified droplets. Should be either
#' ddpcr$na ("N/A") or ddpcr$rain ("Rain"). Defaults to ddpcr$rain.
#'
#' @return If \code{droplets} is a data frame, return a data frame or factor
#' (depending on the \code{trainingData} and \code{fullTable} parameters) with
#' a classification for droplets in the chosen regions.
#'
#' @exportMethod gridClassify
setMethod(
"gridClassify", "data.frame",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
trainingData=TRUE, fullTable=TRUE,
naLabel=ddpcr$rain) {
# Check that the thresholds are sensible.
thresholds <- c(ch1NNThreshold, ch2NNThreshold,
ch1NPThreshold, ch2NPThreshold,
ch1PNThreshold, ch2PNThreshold,
ch1PPThreshold, ch2PPThreshold)
if(!is.numeric(thresholds) || any(thresholds < 0)) {
stop("Thresholds should be nonnegative.")
}
if(ch1NNThreshold > ch1PNThreshold || ch2NNThreshold > ch2NPThreshold ||
(ch1NNThreshold > ch1PPThreshold && ch2NNThreshold > ch2PPThreshold) ||
(ch1NPThreshold > ch1PNThreshold && ch2NPThreshold < ch2PNThreshold) ||
ch1NPThreshold > ch1PPThreshold ||
ch2PNThreshold > ch2PPThreshold) {
stop("The thresholds overlap.")
}
# Set the classification of droplets that lie above or below the given
# thresholds.
df <- droplets[, c("Ch1.Amplitude", "Ch2.Amplitude")]
df$class <- ifelse(
df$Ch1.Amplitude < ch1NNThreshold & df$Ch2.Amplitude < ch2NNThreshold,
ddpcr$nn,
ifelse(
df$Ch1.Amplitude > ch1PNThreshold & df$Ch2.Amplitude < ch2PNThreshold,
ddpcr$pn,
ifelse(
df$Ch1.Amplitude < ch1NPThreshold & df$Ch2.Amplitude > ch2NPThreshold,
ddpcr$np,
ifelse(
df$Ch1.Amplitude > ch1PPThreshold & df$Ch2.Amplitude > ch2PPThreshold,
ddpcr$pp,
naLabel))))
# If we are setting training data, remove all rows not in the grid.
if(!fullTable) {
return(factor(df$class, levels=ddpcr$classesRain))
} else if(trainingData) {
df <- df[df$class != ddpcr$na, ]
}
df$class <- factor(df$class, levels=ddpcr$classesRain)
df
}
)
#' @rdname gridClassify
#'
#' @param classMethodLabel A name (as a character string) of the classification
#' method. Defaults to "grid".
#'
#' @return If \code{droplets} is a \code{ddpcrWell} object, return
#' a \code{ddpcrWell} object with the appropriate classification.
#'
#' @exportMethod gridClassify
setMethod("gridClassify", "ddpcrWell",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
classMethodLabel="grid",
naLabel=ddpcr$rain) {
df <- gridClassify(amplitudes(droplets),
ch1NNThreshold=ch1NNThreshold,
ch2NNThreshold=ch2NNThreshold,
ch1NPThreshold=ch1NPThreshold,
ch2NPThreshold=ch2NPThreshold,
ch1PNThreshold=ch1PNThreshold,
ch2PNThreshold=ch2PNThreshold,
ch1PPThreshold=ch1PPThreshold,
ch2PPThreshold=ch2PPThreshold,
trainingData=FALSE,
fullTable=TRUE,
naLabel=naLabel)
wellClassification(droplets, classMethodLabel) <- df$class
droplets
}
)
#' @rdname gridClassify
#'
#' @return If \code{droplets} is a \code{ddpcrPlate} object, return
#' a \code{ddpcrPlate} object with the appropriate classification.
#'
#' @exportMethod gridClassify
setMethod(
"gridClassify", "ddpcrPlate",
function(droplets,
ch1NNThreshold=6500, ch2NNThreshold=1900,
ch1NPThreshold=6500, ch2NPThreshold=5000,
ch1PNThreshold=10000, ch2PNThreshold=2900,
ch1PPThreshold=7500, ch2PPThreshold=5000,
classMethodLabel="grid",
naLabel=ddpcr$rain) {
df <- gridClassify(do.call(rbind, amplitudes(droplets)),
ch1NNThreshold=ch1NNThreshold,
ch2NNThreshold=ch2NNThreshold,
ch1NPThreshold=ch1NPThreshold,
ch2NPThreshold=ch2NPThreshold,
ch1PNThreshold=ch1PNThreshold,
ch2PNThreshold=ch2PNThreshold,
ch1PPThreshold=ch1PPThreshold,
ch2PPThreshold=ch2PPThreshold,
trainingData=FALSE,
fullTable=TRUE,
naLabel=naLabel)
plateClassification(droplets, cMethod=classMethodLabel) <- df$class
droplets
}
)
#' @title Set thresholds to classify droplets.
#'
#' @description Classify droplets as "NN", "NP", "PN" or "PP" depending on
#' whether they lie above or below given thresholds. This is illustrated in the
#' details and parameters for more detail.
#'
#' @param droplets A \code{\link{ddpcrWell}} object or a data frame of droplet
#' amplitudes with columns \code{Ch1.Amplitude} and \code{Ch2.Amplitude}.
#' @param ch1Threshold The channel 1 upper bound for the NN and NP classes.
#' Defaults to 8000
#' @param ch2Threshold The channel 2 upper bound for the NN and PN classes.
#' Defaults to 3000.
#' @param ... Other options depending on the type of \code{droplets}.
#'
#' @details The \code{threshold} parameters correspond to those in the
#' following diagram:
#' \preformatted{Ch1 ^ |
#' | |
#' | |
#' | PN | PP
#' | |
#' | |
#' a|__________|__________
#' | |
#' | |
#' | NN | NP
#' | |
#' | |
#' --------------------->
#' b Ch2}
#' Specifically:
#' \describe{
#' \item{a:}{\code{ch1Threshold},}
#' \item{b:}{\code{ch2Threshold}.}
#' }
#'
#' @seealso This function is a special case of \code{\link{gridClassify}}.
#'
#' @name thresholdClassify
#'
#' @author Anthony Chiu, \email{anthony.chiu@cruk.manchester.ac.uk}
#'
#' @examples
#' ## Use thresholds to set a classification for a data frame.
#' gCl <- thresholdClassify(KRASdata[["E03"]],
#' ch1Threshold=6789, ch2Threshold=3000)
#' str(gCl)
#'
#' ## The same works for ddpcrWell objects.
#' aWell <- ddpcrWell(well=KRASdata[["E03"]])
#' aWell <- thresholdClassify(aWell, ch1Threshold=6789, ch2Threshold=3000)
#' str(aWell)
#'
#' ## ddpcrPlate objects work in exactly the same way.
#' krasPlate <- ddpcrPlate(wells=KRASdata)
#' krasPlate <- thresholdClassify(krasPlate,
#' ch1Threshold=6789, ch2Threshold=3000)
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' ## The default classification method (column name) is 'threshold'. It can be
#' ## changed by providing a label.
#' krasPlate <- ddpcrPlate(wells=KRASdata)
#' krasPlate <- thresholdClassify(krasPlate,
#' ch1Threshold=6789, ch2Threshold=3000,
#' classMethodLabel="manual")
#' lapply(plateClassification(krasPlate, withAmplitudes=TRUE), head, n=1)
#'
#' @export
setGeneric(
"thresholdClassify",
function(droplets, ch1Threshold=6500, ch2Threshold=2900, ...) {
standardGeneric("thresholdClassify")
}
)
#' @rdname thresholdClassify
#'
#' @param fullTable Whether to return a data frame including amplitude figures.
#' If \code{TRUE}, a data frame with columns \code{Ch1.Amplitude},
#' \code{Ch1.Amplitude} and \code{class} is returned. If \code{FALSE}, a factor
#' with levels in \code{ddpcr$classesRain} is returned, where each entry
#' corresponds to each row in \code{droplets} (and \code{trainingData} is
#' automatically set to \code{FALSE}). Defaults to \code{TRUE}.
#'
#' @return If \code{droplets} is a data frame, return a data frame or factor
#' (depending on the \code{trainingData} and \code{fullTable} parameters) with
#' a classification for droplets in the chosen regions.
#'
#' @exportMethod thresholdClassify
setMethod(
"thresholdClassify", "data.frame",
function(droplets, ch1Threshold=6500, ch2Threshold=2900, fullTable=TRUE) {
gridClassify(droplets,
ch1NNThreshold=ch1Threshold,
ch2NNThreshold=ch2Threshold,
ch1NPThreshold=ch1Threshold,
ch2NPThreshold=ch2Threshold,
ch1PNThreshold=ch1Threshold,
ch2PNThreshold=ch2Threshold,
ch1PPThreshold=ch1Threshold,
ch2PPThreshold=ch2Threshold,
trainingData=TRUE,
fullTable=fullTable)
}
)
#' @rdname thresholdClassify
#'
#' @param classMethodLabel A name (as a character string) of the classification
#' method. Defaults to "thresholds".
#'
#' @return If \code{droplets} is a \code{ddpcrWell} object, return
#' a \code{ddpcrWell} object with the appropriate classification.
#'
#' @exportMethod thresholdClassify
setMethod("thresholdClassify", "ddpcrWell",
function(droplets, ch1Threshold=6500, ch2Threshold=2900,
classMethodLabel="thresholds") {
gridClassify(droplets,
ch1NNThreshold=ch1Threshold,
ch2NNThreshold=ch2Threshold,
ch1NPThreshold=ch1Threshold,
ch2NPThreshold=ch2Threshold,
ch1PNThreshold=ch1Threshold,
ch2PNThreshold=ch2Threshold,
ch1PPThreshold=ch1Threshold,
ch2PPThreshold=ch2Threshold,
classMethodLabel=classMethodLabel)
}
)
#' @rdname thresholdClassify
#'
#' @return If \code{droplets} is a \code{ddpcrPlate} object, return
#' a \code{ddpcrPlate} object with the appropriate classification.
#'
#' @exportMethod thresholdClassify
setMethod("thresholdClassify",
"ddpcrPlate",
function(droplets, ch1Threshold=6500, ch2Threshold=2900,
classMethodLabel="thresholds") {
gridClassify(droplets,
ch1NNThreshold=ch1Threshold,
ch2NNThreshold=ch2Threshold,
ch1NPThreshold=ch1Threshold,
ch2NPThreshold=ch2Threshold,
ch1PNThreshold=ch1Threshold,
ch2PNThreshold=ch2Threshold,
ch1PPThreshold=ch1Threshold,
ch2PPThreshold=ch2Threshold,
classMethodLabel=classMethodLabel)
}
)
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