R/AbsoluteQuantification.R
In aLFQ/aLFQ: Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data
AbsoluteQuantification <- function(data, ...) UseMethod("AbsoluteQuantification")
AbsoluteQuantification.default <- function(data, total_protein_concentration=1, ...) {
if (length(setdiff(c("run_id","protein_id","concentration","response"),names(data))) > 0) stop("No suitable input for AbsoluteQuantification. Did you use ProteinInference on your data.frame?")
object = list()
data.selection<-data
data.selection$normalized_response <- data.selection$response / sum(data.selection$response)
data.selection$response <- log(data.selection$response)
data.selection$normalized_response <- log(data.selection$normalized_response)
object$is_calibrated <- FALSE
if (dim(subset(data.selection,data.selection$concentration != "?"))[1] > 2) {
object$is_calibrated <- TRUE
object$calibration <- subset(data.selection,data.selection$concentration != "?")
object$calibration$concentration <- log(as.numeric(object$calibration$concentration))
object$prediction <- data.selection
object$prediction$concentration<-"?"
object$model <- lm(concentration ~ response,object$calibration)
object$mfe <- mean(folderror.AbsoluteQuantification(exp(predict(object$model,object$calibration)),exp(object$calibration$concentration)))
object$r.squared <- summary(object$model)$r.squared
object$calibration_covar <- (100*sd(object$calibration$response)) / mean(object$calibration$response)
}
data.selection$normalized_concentration <- data.selection$normalized_response + log(total_protein_concentration)
object$estimation <- data.selection[,c("run_id","protein_id","normalized_response","normalized_concentration")]
object$normalized_concentration_covar <- (100*sd(data.selection$normalized_response)) / mean(data.selection$normalized_response)
class(object) <- "AbsoluteQuantification"
return(object)
}
predict.AbsoluteQuantification <- function(object, ...) {
if (!inherits(object, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!object$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
object$prediction$concentration <- predict(object$model,newdata=object$prediction)
return(object)
}
folderror.AbsoluteQuantification <- function(predicted,true, ...) {
fea = rep(0,0)
for(i in 1:length(predicted)) {
fe <- predicted[i] / true[i]
if (fe < 1) fe = 1 / fe
fea = c(fea, fe)
}
return(fea)
}
cval <- function(object, ...) UseMethod("cval")
cval.default <- function(object, ...)
stop("No method implemented for this class of object")
cval.AbsoluteQuantification <- function(object,cval_method="mc",mcx=1000, ...) {
if (!inherits(object, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!object$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
rs_sample <- 0
mfe_sample <- 0
object$cval$rs_array <- c()
object$cval$mfe_array <- c()
j <- 1
if (cval_method=="mc") {
while (j <= mcx) {
partition1 <- sample(c(1:dim(object$calibration)[1]),floor((1/3)*dim(object$calibration)[1]))
partition2 <- sample(c(1:dim(object$calibration)[1])[-partition1],floor((1/3)*dim(object$calibration)[1]))
partition3 <- c(1:dim(object$calibration)[1])[-c(partition1,partition2)]
regression1 <- lm(formula(concentration ~ response),object$calibration[c(partition1,partition2),])
rs1 <- 1 - sum((abs(object$calibration[partition3,]$concentration - predict(regression1,object$calibration[partition3,])))^2) / sum((abs(object$calibration[partition3,]$concentration - mean(object$calibration[partition3,]$concentration)))^2)
mfe1 <- mean(folderror.AbsoluteQuantification(as.vector(exp(predict(regression1,object$calibration[partition3,]))),exp(object$calibration[partition3,]$concentration)))
regression2 <- lm(formula(concentration ~ response),object$calibration[c(partition1,partition3),])
rs2 <- 1 - sum((abs(object$calibration[partition2,]$concentration - predict(regression2,object$calibration[partition2,])))^2) / sum((abs(object$calibration[partition2,]$concentration - mean(object$calibration[partition2,]$concentration)))^2)
mfe2 <- mean(folderror.AbsoluteQuantification(as.vector(exp(predict(regression2,object$calibration[partition2,]))),exp(object$calibration[partition2,]$concentration)))
regression3 <- lm(formula(concentration ~ response),object$calibration[c(partition2,partition3),])
rs3 <- 1 - sum((abs(object$calibration[partition1,]$concentration - predict(regression3,object$calibration[partition1,])))^2) / sum((abs(object$calibration[partition1,]$concentration - mean(object$calibration[partition1,]$concentration)))^2)
mfe3 <- mean(folderror.AbsoluteQuantification(as.vector(exp(predict(regression3,object$calibration[partition1,]))),exp(object$calibration[partition1,]$concentration)))
object$cval$mfe_array <- append(object$cval$mfe_array,c(mfe1,mfe2,mfe3))
object$cval$rs_array <- append(object$cval$rs_array,c(rs1,rs2,rs3))
j <- j + 1
}
object$cval$r.squared <- mean(object$cval$rs_array[which(is.finite(object$cval$rs_array))])
object$cval$mfe <- mean(object$cval$mfe_array[which(is.finite(object$cval$mfe_array))])
}
else if (cval_method=="boot") {
while (j <= mcx) {
training <- sample(dim(object$calibration)[1],replace=TRUE)
regression <- lm(formula(concentration ~ response),object$calibration[training,])
rs <- 1 - sum((abs(object$calibration[-unique(training),]$concentration - predict(regression,object$calibration[-unique(training),])))^2) / sum((abs(object$calibration[-unique(training),]$concentration - mean(object$calibration[-unique(training),]$concentration)))^2)
fe <- folderror.AbsoluteQuantification(as.vector(exp(predict(regression,object$calibration[-training,]))),exp(object$calibration[-training,]$concentration))
object$cval$mfe_array <- append(object$cval$mfe_array,mean(fe))
object$cval$rs_array <- append(object$cval$rs_array,rs)
j <- j + 1
}
object$cval$r.squared <- mean(object$cval$rs_array[which(is.finite(object$cval$rs_array))])
object$cval$mfe <- mean(object$cval$mfe_array[which(is.finite(object$cval$mfe_array))])
}
else if (cval_method=="loo") {
j <- 1
while (j <= dim(object$calibration)[1]) {
training <- c(1:dim(object$calibration)[1])[-j]
test <- j
regression1 <- lm(formula(concentration ~ response),object$calibration[training,])
mfe1 <- 1 + abs(1 - abs(exp(predict(regression1,object$calibration[test,])) - exp(object$calibration[test,]$concentration)) / exp(object$calibration[test,]$concentration))
mfe_sample <- mfe_sample + mfe1
j <- j + 1
}
object$cval$r.squared <- summary(object$model)$r.squared
object$cval$mfe <- mfe_sample / dim(object$calibration)[1]
}
return(object)
}
print.AbsoluteQuantification <- function(x, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
cat("AbsoluteQuantification\n")
cat("\n")
cat("Number of proteins: ")
cat(length(unique(x$estimation$protein_id)))
cat("\n")
cat("Calibration Trainingset size: ")
cat(dim(x$calibration)[[1]])
cat("\n")
cat("Calibration Testset size: ")
cat(dim(x$prediction)[[1]])
cat("\n")
cat("Calibration Regression mean-fold error: ")
cat(x$mfe)
cat("\n")
cat("Calibration Regression R-squared: ")
cat(x$r.squared)
cat("\n")
cat("Calibration cross-validation mean-fold error: ")
cat(x$cval$mfe)
cat("\n")
cat("Calibration cross-validation R-squared: ")
cat(x$cval$r.squared)
cat("\n")
cat("Calibration CV: ")
cat(x$calibration_covar)
cat("\n")
cat("Normalized Concentration CV: ")
cat(x$normalized_concentration_covar)
cat("\n")
}
plot.AbsoluteQuantification <- function(x, ...) {
concentration <- response <- NULL
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!x$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
if (dim(subset(x$prediction,concentration == "?"))[1] > 0) {
plot(data.frame(log10(exp(x$calibration$response)),log10(exp(as.numeric(x$calibration$concentration)))),col="red",xlab='log10(intensity)',ylab='log10(concentration)',xlim=c(min(log10(exp(as.numeric(x$calibration$response)))),max(log10(exp(as.numeric(x$calibration$response))))),ylim=c(min(log10(exp(as.numeric(x$calibration$concentration)))),max(log10(exp(as.numeric(x$calibration$concentration))))))
log10_model <-x$model
log10_model$coefficients[1] <- log10(exp(log10_model$coefficients[1]))
abline(log10_model, col="red")
if (is.null(x$cval)) {
title(paste('MFE:',format(x$mfe, digits=4),'RSQ:',format(x$r.squared, digits=4)))
}
else {
title(paste('CV-MFE:',format(x$cv$mfe, digits=4),'CV-RSQ:',format(x$cv$r.squared, digits=4)))
}
}
else if (dim(subset(x$prediction,concentration == "?"))[1] == 0) {
calibration.df <- data.frame(x$calibration$response,as.numeric(x$calibration$concentration))
names(calibration.df) <- c("response","concentration")
prediction.df <- data.frame(x$prediction$response,as.numeric(x$prediction$concentration))
names(prediction.df) <- c("response","concentration")
merged <- rbind(calibration.df,prediction.df)
merged <- subset(merged,is.finite(concentration) & is.finite(response))
plot(log10(exp(prediction.df)),xlab='log10(intensity)',ylab='log10(concentration)',xlim=c(min(log10(exp(as.numeric(merged$response)))),max(log10(exp(as.numeric(merged$response))))),ylim=c(min(log10(exp(as.numeric(merged$concentration)))),max(log10(exp(as.numeric(merged$concentration))))))
points(log10(exp(calibration.df)), col ="red")
log10_model <-x$model
log10_model$coefficients[1] <- log10(exp(log10_model$coefficients[1]))
abline(log10_model, col="red")
if (is.null(x$cval)) {
title(paste('MFE:',format(x$mfe, digits=4),'RSQ:',format(x$r.squared, digits=4)))
}
else {
title(paste('CV-MFE:',format(x$cv$mfe, digits=4),'CV-RSQ:',format(x$cv$r.squared, digits=4)))
}
}
}
hist.AbsoluteQuantification <- function(x, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!x$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
if (is.null(x$cval)) stop("Apply cval method to object before plotting the histogram.")
hist(x$cval$mfe_array, freq=FALSE,main=paste('Histogram of MFE\nMean = ',format(mean(x$cval$mfe_array), digits=4), ", 95% CI =", format(1.96*sd(x$cval$mfe_array), digits=4) ), breaks = 40, xlab="mean fold error")
xfit = seq(min(x$cval$mfe_array), max(x$cval$mfe_array),length=40)
yfit = dnorm(xfit, mean=mean(x$cval$mfe_array), sd=sd(x$cval$mfe_array))
lines(xfit, yfit, col="red")
}
export <- function(x, file, ...) UseMethod("export")
export.default <- function(x, file, ...)
stop("No method implemented for this class of object")
export.AbsoluteQuantification <- function(x, file, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (x$is_calibrated) {
data <- merge(x$prediction[,c("protein_id","response","concentration")],x$estimation, all=TRUE)
data$response <- exp(data$response)
data$concentration <- exp(as.numeric(data$concentration))
}
else {
data <- x$estimation
}
data$normalized_response <- exp(data$normalized_response)
data$normalized_concentration <- exp(data$normalized_concentration)
write.csv(data, file = file, ...)
}
pivot <- function(x, ...) UseMethod("pivot")
pivot.default <- function(x, ...) {
if (!inherits(x, "data.frame")) stop("Is not a data.frame")
if ("sec_id" %in% names(x)) {
data<-acast(x, protein_id ~ sec_id, value.var="response", fill=0)
}
else {
data<-acast(x, protein_id ~ run_id, value.var="response", fill=0)
}
return(data)
}
pivot.AbsoluteQuantification <- function(x, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if ("?" %in% x$prediction$concentration) stop("Apply predict before pivot to AbsoluteQuantification object")
if (x$is_calibrated) {
x$prediction$concentration<-exp(as.numeric(x$prediction$concentration))
tdata<-acast(x$prediction, protein_id ~ run_id, value.var="concentration", fill=0)
}
else {
x$prediction$normalized_concentration<-exp(x$prediction$normalized_concentration)
tdata<-acast(x$estimation, protein_id ~ run_id, value.var="normalized_concentration", fill=0)
}
return(tdata)
}
aLFQ/aLFQ documentation built on May 10, 2019, 1:59 a.m.
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AbsoluteQuantification <- function(data, ...) UseMethod("AbsoluteQuantification")
AbsoluteQuantification.default <- function(data, total_protein_concentration=1, ...) {
if (length(setdiff(c("run_id","protein_id","concentration","response"),names(data))) > 0) stop("No suitable input for AbsoluteQuantification. Did you use ProteinInference on your data.frame?")
object = list()
data.selection<-data
data.selection$normalized_response <- data.selection$response / sum(data.selection$response)
data.selection$response <- log(data.selection$response)
data.selection$normalized_response <- log(data.selection$normalized_response)
object$is_calibrated <- FALSE
if (dim(subset(data.selection,data.selection$concentration != "?"))[1] > 2) {
object$is_calibrated <- TRUE
object$calibration <- subset(data.selection,data.selection$concentration != "?")
object$calibration$concentration <- log(as.numeric(object$calibration$concentration))
object$prediction <- data.selection
object$prediction$concentration<-"?"
object$model <- lm(concentration ~ response,object$calibration)
object$mfe <- mean(folderror.AbsoluteQuantification(exp(predict(object$model,object$calibration)),exp(object$calibration$concentration)))
object$r.squared <- summary(object$model)$r.squared
object$calibration_covar <- (100*sd(object$calibration$response)) / mean(object$calibration$response)
}
data.selection$normalized_concentration <- data.selection$normalized_response + log(total_protein_concentration)
object$estimation <- data.selection[,c("run_id","protein_id","normalized_response","normalized_concentration")]
object$normalized_concentration_covar <- (100*sd(data.selection$normalized_response)) / mean(data.selection$normalized_response)
class(object) <- "AbsoluteQuantification"
return(object)
}
predict.AbsoluteQuantification <- function(object, ...) {
if (!inherits(object, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!object$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
object$prediction$concentration <- predict(object$model,newdata=object$prediction)
return(object)
}
folderror.AbsoluteQuantification <- function(predicted,true, ...) {
fea = rep(0,0)
for(i in 1:length(predicted)) {
fe <- predicted[i] / true[i]
if (fe < 1) fe = 1 / fe
fea = c(fea, fe)
}
return(fea)
}
cval <- function(object, ...) UseMethod("cval")
cval.default <- function(object, ...)
stop("No method implemented for this class of object")
cval.AbsoluteQuantification <- function(object,cval_method="mc",mcx=1000, ...) {
if (!inherits(object, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!object$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
rs_sample <- 0
mfe_sample <- 0
object$cval$rs_array <- c()
object$cval$mfe_array <- c()
j <- 1
if (cval_method=="mc") {
while (j <= mcx) {
partition1 <- sample(c(1:dim(object$calibration)[1]),floor((1/3)*dim(object$calibration)[1]))
partition2 <- sample(c(1:dim(object$calibration)[1])[-partition1],floor((1/3)*dim(object$calibration)[1]))
partition3 <- c(1:dim(object$calibration)[1])[-c(partition1,partition2)]
regression1 <- lm(formula(concentration ~ response),object$calibration[c(partition1,partition2),])
rs1 <- 1 - sum((abs(object$calibration[partition3,]$concentration - predict(regression1,object$calibration[partition3,])))^2) / sum((abs(object$calibration[partition3,]$concentration - mean(object$calibration[partition3,]$concentration)))^2)
mfe1 <- mean(folderror.AbsoluteQuantification(as.vector(exp(predict(regression1,object$calibration[partition3,]))),exp(object$calibration[partition3,]$concentration)))
regression2 <- lm(formula(concentration ~ response),object$calibration[c(partition1,partition3),])
rs2 <- 1 - sum((abs(object$calibration[partition2,]$concentration - predict(regression2,object$calibration[partition2,])))^2) / sum((abs(object$calibration[partition2,]$concentration - mean(object$calibration[partition2,]$concentration)))^2)
mfe2 <- mean(folderror.AbsoluteQuantification(as.vector(exp(predict(regression2,object$calibration[partition2,]))),exp(object$calibration[partition2,]$concentration)))
regression3 <- lm(formula(concentration ~ response),object$calibration[c(partition2,partition3),])
rs3 <- 1 - sum((abs(object$calibration[partition1,]$concentration - predict(regression3,object$calibration[partition1,])))^2) / sum((abs(object$calibration[partition1,]$concentration - mean(object$calibration[partition1,]$concentration)))^2)
mfe3 <- mean(folderror.AbsoluteQuantification(as.vector(exp(predict(regression3,object$calibration[partition1,]))),exp(object$calibration[partition1,]$concentration)))
object$cval$mfe_array <- append(object$cval$mfe_array,c(mfe1,mfe2,mfe3))
object$cval$rs_array <- append(object$cval$rs_array,c(rs1,rs2,rs3))
j <- j + 1
}
object$cval$r.squared <- mean(object$cval$rs_array[which(is.finite(object$cval$rs_array))])
object$cval$mfe <- mean(object$cval$mfe_array[which(is.finite(object$cval$mfe_array))])
}
else if (cval_method=="boot") {
while (j <= mcx) {
training <- sample(dim(object$calibration)[1],replace=TRUE)
regression <- lm(formula(concentration ~ response),object$calibration[training,])
rs <- 1 - sum((abs(object$calibration[-unique(training),]$concentration - predict(regression,object$calibration[-unique(training),])))^2) / sum((abs(object$calibration[-unique(training),]$concentration - mean(object$calibration[-unique(training),]$concentration)))^2)
fe <- folderror.AbsoluteQuantification(as.vector(exp(predict(regression,object$calibration[-training,]))),exp(object$calibration[-training,]$concentration))
object$cval$mfe_array <- append(object$cval$mfe_array,mean(fe))
object$cval$rs_array <- append(object$cval$rs_array,rs)
j <- j + 1
}
object$cval$r.squared <- mean(object$cval$rs_array[which(is.finite(object$cval$rs_array))])
object$cval$mfe <- mean(object$cval$mfe_array[which(is.finite(object$cval$mfe_array))])
}
else if (cval_method=="loo") {
j <- 1
while (j <= dim(object$calibration)[1]) {
training <- c(1:dim(object$calibration)[1])[-j]
test <- j
regression1 <- lm(formula(concentration ~ response),object$calibration[training,])
mfe1 <- 1 + abs(1 - abs(exp(predict(regression1,object$calibration[test,])) - exp(object$calibration[test,]$concentration)) / exp(object$calibration[test,]$concentration))
mfe_sample <- mfe_sample + mfe1
j <- j + 1
}
object$cval$r.squared <- summary(object$model)$r.squared
object$cval$mfe <- mfe_sample / dim(object$calibration)[1]
}
return(object)
}
print.AbsoluteQuantification <- function(x, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
cat("AbsoluteQuantification\n")
cat("\n")
cat("Number of proteins: ")
cat(length(unique(x$estimation$protein_id)))
cat("\n")
cat("Calibration Trainingset size: ")
cat(dim(x$calibration)[[1]])
cat("\n")
cat("Calibration Testset size: ")
cat(dim(x$prediction)[[1]])
cat("\n")
cat("Calibration Regression mean-fold error: ")
cat(x$mfe)
cat("\n")
cat("Calibration Regression R-squared: ")
cat(x$r.squared)
cat("\n")
cat("Calibration cross-validation mean-fold error: ")
cat(x$cval$mfe)
cat("\n")
cat("Calibration cross-validation R-squared: ")
cat(x$cval$r.squared)
cat("\n")
cat("Calibration CV: ")
cat(x$calibration_covar)
cat("\n")
cat("Normalized Concentration CV: ")
cat(x$normalized_concentration_covar)
cat("\n")
}
plot.AbsoluteQuantification <- function(x, ...) {
concentration <- response <- NULL
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!x$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
if (dim(subset(x$prediction,concentration == "?"))[1] > 0) {
plot(data.frame(log10(exp(x$calibration$response)),log10(exp(as.numeric(x$calibration$concentration)))),col="red",xlab='log10(intensity)',ylab='log10(concentration)',xlim=c(min(log10(exp(as.numeric(x$calibration$response)))),max(log10(exp(as.numeric(x$calibration$response))))),ylim=c(min(log10(exp(as.numeric(x$calibration$concentration)))),max(log10(exp(as.numeric(x$calibration$concentration))))))
log10_model <-x$model
log10_model$coefficients[1] <- log10(exp(log10_model$coefficients[1]))
abline(log10_model, col="red")
if (is.null(x$cval)) {
title(paste('MFE:',format(x$mfe, digits=4),'RSQ:',format(x$r.squared, digits=4)))
}
else {
title(paste('CV-MFE:',format(x$cv$mfe, digits=4),'CV-RSQ:',format(x$cv$r.squared, digits=4)))
}
}
else if (dim(subset(x$prediction,concentration == "?"))[1] == 0) {
calibration.df <- data.frame(x$calibration$response,as.numeric(x$calibration$concentration))
names(calibration.df) <- c("response","concentration")
prediction.df <- data.frame(x$prediction$response,as.numeric(x$prediction$concentration))
names(prediction.df) <- c("response","concentration")
merged <- rbind(calibration.df,prediction.df)
merged <- subset(merged,is.finite(concentration) & is.finite(response))
plot(log10(exp(prediction.df)),xlab='log10(intensity)',ylab='log10(concentration)',xlim=c(min(log10(exp(as.numeric(merged$response)))),max(log10(exp(as.numeric(merged$response))))),ylim=c(min(log10(exp(as.numeric(merged$concentration)))),max(log10(exp(as.numeric(merged$concentration))))))
points(log10(exp(calibration.df)), col ="red")
log10_model <-x$model
log10_model$coefficients[1] <- log10(exp(log10_model$coefficients[1]))
abline(log10_model, col="red")
if (is.null(x$cval)) {
title(paste('MFE:',format(x$mfe, digits=4),'RSQ:',format(x$r.squared, digits=4)))
}
else {
title(paste('CV-MFE:',format(x$cv$mfe, digits=4),'CV-RSQ:',format(x$cv$r.squared, digits=4)))
}
}
}
hist.AbsoluteQuantification <- function(x, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (!x$is_calibrated) stop("Method not supported for AbsoluteQuantification objects without calibration proteins.")
if (is.null(x$cval)) stop("Apply cval method to object before plotting the histogram.")
hist(x$cval$mfe_array, freq=FALSE,main=paste('Histogram of MFE\nMean = ',format(mean(x$cval$mfe_array), digits=4), ", 95% CI =", format(1.96*sd(x$cval$mfe_array), digits=4) ), breaks = 40, xlab="mean fold error")
xfit = seq(min(x$cval$mfe_array), max(x$cval$mfe_array),length=40)
yfit = dnorm(xfit, mean=mean(x$cval$mfe_array), sd=sd(x$cval$mfe_array))
lines(xfit, yfit, col="red")
}
export <- function(x, file, ...) UseMethod("export")
export.default <- function(x, file, ...)
stop("No method implemented for this class of object")
export.AbsoluteQuantification <- function(x, file, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if (x$is_calibrated) {
data <- merge(x$prediction[,c("protein_id","response","concentration")],x$estimation, all=TRUE)
data$response <- exp(data$response)
data$concentration <- exp(as.numeric(data$concentration))
}
else {
data <- x$estimation
}
data$normalized_response <- exp(data$normalized_response)
data$normalized_concentration <- exp(data$normalized_concentration)
write.csv(data, file = file, ...)
}
pivot <- function(x, ...) UseMethod("pivot")
pivot.default <- function(x, ...) {
if (!inherits(x, "data.frame")) stop("Is not a data.frame")
if ("sec_id" %in% names(x)) {
data<-acast(x, protein_id ~ sec_id, value.var="response", fill=0)
}
else {
data<-acast(x, protein_id ~ run_id, value.var="response", fill=0)
}
return(data)
}
pivot.AbsoluteQuantification <- function(x, ...) {
if (!inherits(x, "AbsoluteQuantification")) stop("Is not a AbsoluteQuantification object")
if ("?" %in% x$prediction$concentration) stop("Apply predict before pivot to AbsoluteQuantification object")
if (x$is_calibrated) {
x$prediction$concentration<-exp(as.numeric(x$prediction$concentration))
tdata<-acast(x$prediction, protein_id ~ run_id, value.var="concentration", fill=0)
}
else {
x$prediction$normalized_concentration<-exp(x$prediction$normalized_concentration)
tdata<-acast(x$estimation, protein_id ~ run_id, value.var="normalized_concentration", fill=0)
}
return(tdata)
}
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