R/divisionFunction.R
In marionbetizeau/CerebellumAstrocyteSimulation: Simulate Astrocyte Lineages
#' Simulate an astrocyte lineage
#'
#' @param motherCell the information table of the mother cell
#' @param cloneOutput the initialized clone table with the 1st cell information
#' @param transitionMatrix a list specifying the transition matrix of the particular progenitor type:
#'
#' - \strong{transition.Progenitor} : list of 3 elements defining the probabilities of cell cycle exit vs amplification of the progenitor, as well as the potential types produced
#' \describe{
#' \item{probability}{Matrix with columns: probabilities of generating a MP (1st column), or an Astrocyte (2nd column),
#'
#' rows : for each generation in the simulated lineage (number of rows can vary)}
#' \item{type}{The corresponding names of different possible outcome, in the same order as the columns of the probability matrix (MP, Astro)
#' (number of types correponds to the number of columns of the above probability matrix)}
#' \item{generationInterval}{Vector of length corresponding to the number of generations, length equal to the number of rows of the matrix probability}
#' }
#'
#' - \strong{transition.Astro} : probabilities of generating the different astrocyte types after cell cycle exit
#' \describe{
#' \item{probability}{Matrix with columns: probabilities of generating a BG (1st column), a GLA (2nd column), or a WMA (3rd column)
#'
#' rows : for each generation in the simulated lineage (number of rows can vary)}
#' \item{type}{The corresponding names of different possible outcome, in the same order as the columns of the probability matrix (BG, GLA, WMA)
#' (number of types correponds to the number of columns of the above probability matrix)}
#' \item{generationInterval}{Vector of length corresponding to the number of generations, length equal to the number of rows of the matrix probability}
#' }
#'
#' - \strong{firstPostMitoticCell} : for the one-cell clones, proportions of the observed astrocyte types
#'
#' @param maxCount number of trials after which the function will stop and return an error message if the lineage never stops (stochasticity issue, the lineage stops when all
#' cells differentiate. If the probability of cell cycle exit is too low, some cell might keep on proliferating indefinitely.)
#'
#' @param parameterInterpolation boolean indicating whether interpolation was performed on the input data (always TRUE in the published simulations)
#'
#' @return cloneTable: dataframe containing the information about the simulated lineage
#' \describe{
#' \item{motherID}{ID of the mother of the current cell}
#' \item{cellID}{ID of the current cell}
#' \item{type}{Type of the current cell}
#' \item{timepoint}{Generation the current cell is born. Mother cell is initialized at generation 1}
#' }
#'
#' @seealso \code{\link{currentCell}} \code{\link{cloneTable}} \code{\link{transition.MP}} \code{\link{transition.Astro}} \code{\link{firstPostMitoticCell}}
#'
#' @examples
#' # Simulation of a lineage with a multipotent progenitor as initial cell.
#' cloneTable <- divisionMPGenerationDpd(motherCell = currentCell,
#' cloneOutput = cloneTable,
#' transitionMatrix = list(transition.MP, transition.Astro, firstPostMitoticCell),
#' maxCount = 100000)
#'
#' @export divisionMPGenerationDpd
divisionMPGenerationDpd <- function(motherCell = currentCell, cloneOutput = cloneTable,
transitionMatrix = list(transition.Progenitor, transition.Astro, firstPostMitoticCell),
maxCount = 100000,
parameterInterpolation = T){
# transition.MP : list of probability of becoming MP or Astrocyte, corresponding cell types, generationInterval : for generation dependent probabilities
# example : transition.Astro <- list(probability = matrix(c(pBG, pGLA,pWMA), nrow = length(pBG), ncol = 3), type = c("BG", "GLA","WMA"), generationInterval = generationInterval)
# checks the consistency of the input
if(parameterInterpolation == F){
if(dim(as.matrix(transitionMatrix[[2]]$probability))[1] != (length(transitionMatrix[[2]]$generationInterval)-1)
| dim(as.matrix(transitionMatrix[[2]]$probability))[2] != (length(transitionMatrix[[2]]$type))){
stop('The dimention of the Astocyte transitions are inconsistent, check the probability, type and generationInterval vectors')
}
if(dim(as.matrix(transitionMatrix[[1]]$probability))[1] != (length(transitionMatrix[[1]]$generationInterval)-1)
| dim(as.matrix(transitionMatrix[[1]]$probability))[2] != (length(transitionMatrix[[1]]$type))){
stop('The dimention of the MP transitions are inconsistent, check the probability, type and generationInterval vectors')
}
} else {
if(dim(as.matrix(transitionMatrix[[2]]$probability))[1] != (length(transitionMatrix[[2]]$generationInterval))
| dim(as.matrix(transitionMatrix[[2]]$probability))[2] != (length(transitionMatrix[[2]]$type))){
stop('The dimention of the Astocyte transitions are inconsistent, check the probability, type and generationInterval vectors')
}
if(dim(as.matrix(transitionMatrix[[1]]$probability))[1] != (length(transitionMatrix[[1]]$generationInterval))
| dim(as.matrix(transitionMatrix[[1]]$probability))[2] != (length(transitionMatrix[[1]]$type))){
stop('The dimention of the MP transitions are inconsistent, check the probability, type and generationInterval vectors')
}
if(length(transitionMatrix[[3]]$probabilities != transitionMatrix[[3]]$type)){
stop('The dimention of the firstPostMitoticCell probabilitites are inconsistent, check the type and probabilities vectors')
}
}
count <- 1
#numbermother <- dim(motherCell)[1]
while(dim(motherCell)[1]>0){
if(count > maxCount){
cloneTable <- rbind(data.frame(motherID = NA, cellID = NA, type = NA, timepoint = NA))
return(cloneTable)
stop('Did not converge')
}
motherType <- motherCell$type[1];
motherTimepoint <- motherCell$timepoint[1]; # print(paste("motherType", motherType,"mother cell:", motherCell, "timePoint:",motherTimepoint))
currentMotherID <- motherCell$cellID[1]
if(motherType %in% c("WMA", "BG", "Gla")){
motherCell <- motherCell[-1,] # remove the cell from the list of mother (will not divide)
}
if(motherType %in% "Astro"){ # can happen the intial cell in already differentiating
transition <- transitionMatrix[[3]]
cellDice <- runif(1)
cellType <- transition$type[min(which(cellDice < cumsum(transition$probability)))]
cloneTable$type[count] <- cellType
motherCell <- motherCell[-1,]
} else{
daughterDice <- runif(2)
transition <- transitionMatrix[[1]]
progType <- transition$type[1]
motherGenerationInterval <- .bincode(motherTimepoint, breaks = transition$generationInterval, right = F)
D1Type <- transition$type[min(which(daughterDice[1] < cumsum(transition$probability[motherGenerationInterval,])))]
D1ID <- max(cloneTable$cellID) + 1
D1Tp <- motherCell$timepoint[1]+1
D2Type <- transition$type[min(which(daughterDice[2] < cumsum(transition$probability[motherGenerationInterval,])))]
D2ID <- max(cloneTable$cellID) + 2
D2Tp <- motherCell$timepoint[1]+1
#currentMotherID <- motherCell$cellID[1]
motherCell <- motherCell[-1,]
if(D1Type == progType){
motherCell <- rbind(motherCell, data.frame(cellID = c(D1ID), type = c(D1Type), timepoint = D1Tp))
motherCell$type <- as.character(motherCell$type)
}else {
transition <- transitionMatrix[[2]]
motherGenerationInterval <- .bincode(motherTimepoint, breaks = transition$generationInterval, right = F)
D1Dice <- runif(1)
D1Type <- transition$type[min(which(D1Dice < cumsum(transition$probability[motherGenerationInterval,])))]
}
if(D2Type == progType){
motherCell <- rbind(motherCell, data.frame(cellID = c(D2ID), type = c(D2Type), timepoint = D2Tp))
motherCell$type <- as.character(motherCell$type)
} else{
transition <- transitionMatrix[[2]]
motherGenerationInterval <- .bincode(motherTimepoint, breaks = transition$generationInterval, right = F)
D2Dice <- runif(1)
D2Type <- transition$type[min(which(D2Dice < cumsum(transition$probability[motherGenerationInterval,])))]
}
cloneTable <- rbind(cloneTable, data.frame(motherID = currentMotherID,
cellID = c(D1ID, D2ID), type = c(D1Type, D2Type), timepoint = c(D1Tp, D2Tp)))
}
count <- count + 1; #print(count); print(cloneTable$timepoint)
}
cloneTable
}
#' Add transparency to a color
#'
#' @param col A color.
#' @param alpha The level of transparency.
#' @return The transparent color.
#' @examples
#' color1 <- add.alpha(col = "red", alpha = 0.6)
#'
#' color2 <- add.alpha(colors()[115], 0.5)
#' x <- rnorm(1000, mean = 1, sd = 0.5)
#' y <- rnorm(1000, mean = 1, sd = 0.5)
#' plot(x,y, col = c(color1, color2), pch = 16)
#'
#' @seealso \code{\link{rgb}}, \code{\link{colors}}
#'
#' @export add.alpha
add.alpha <- function(col, alpha=1){
if(missing(col))
stop("Please provide a vector of colours.")
apply(sapply(col, col2rgb)/255, 2,
function(x)
rgb(x[1], x[2], x[3], alpha=alpha))
}
getCloneSubtype <- function(cloneOutcome, cellTypes = c("BG", "GLA","WMA")){
if(sum(cloneOutcome, na.rm = T) >0){
cloneSubtype <- paste(cellTypes[cloneOutcome > 0 & is.na(cloneOutcome) == F], collapse = "_")
} else { cloneSubtype <- NA}
cloneSubtype
}
getCloneType <- function(cloneOutcome){
cellTypeNumber <- sum(cloneOutcome>0)
if(!is.na(cellTypeNumber) & cellTypeNumber < 2){
cloneType <- "HomC"
} else { cloneType <- "HetC"}
cloneType
}
#' Add error bars to a bar plot
#'
#' @param x the barplot x coordinates.
#' @param y the data (height of the bars).
#' @param upper matrix of the different error bar values.
#' @param lower matrix of the different error bar values. By default equals to upper
#' @param length length of the horizontal lines of the error bars
#'
#' @examples
#' v1 <- c(1:4)
#' my.x <- barplot(v1)
#' error <- c(0.2, 1, 0.5, 0.1)
#' error.bar(x = my.x, y = v1, upper = error)
#'
#' @export error.bar
error.bar <- function(x, y, upper, lower=upper, length=0.05,...){
if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))
stop("vectors must be same length")
arrows(x,y+upper, x, y-lower, angle=90, code=3, length=length, ...)
}
#' Add error bars to a bar plot
#'
#' @param x the barplot x coordinates.
#' @param y the data (height of the bars).
#' @param upper matrix of the different error bar values.
#' @param lower matrix of the different error bar values. By default equals to upper
#' @param length length of the horizontal lines of the error bars
#'
#' @examples
#' v1 <- c(1:4)
#' my.x <- barplot(v1)
#' error <- c(0.2, 1, 0.5, 0.1)
#' error.bar(x = my.x, y = v1, upper = error)
#'
#' @export meanSDReplicate
## Function to get the mean and SD of the different replates in the model
meanSDReplicate <- function(simulationResults, variable, replicateNumber = 5){
if(is.null(simulationResults$Replicate)){
simulationResults$Replicate <- 1
for ( i in c(1:replicateNumber)){
simulationResults$Replicate[(1 + (i-1)*numberClonesPerReplicate) : (numberClonesPerReplicate + (i-1) * numberClonesPerReplicate)] <- i
}
}
replicateNumber <- length(unique(simulationResults$Replicate))
variableCol <- which(names(simulationResults) %in% variable)
if(length(variableCol) == 0){stop("The variable chosen does not exist in the table, please check spelling and existence")}
Replicates <- t(matrix(data = unlist(with(simulationResults, tapply(simulationResults[, variableCol], INDEX = Replicate, FUN = table))), ncol = replicateNumber))
ReplicatesProp <- prop.table(Replicates, margin = 1)
meanReplicates<- apply(ReplicatesProp, MARGIN = 2, FUN = mean)
sdReplicates <- apply(ReplicatesProp, MARGIN = 2, FUN = sd)
list(mean = meanReplicates, sd = sdReplicates)
}
#' Data from observed clones after electroporation at E14 and sacrifice at P30
#'
#' A dataset containing the information of all clones measured
#'
#' @format A data frame with 102 rows and 14 variables:
#' \describe{
#' \item{animal_n}{Identifier of the animal the clone was observed in}
#' \item{clone_ID}{Identifier of the clone, combination of the different colors observed after recombination}
#' \item{time_of_electroporation_}{Development time when the electroporation was performed}
#' \item{time_of_analysis}{Time when the sacrifice and analysis were performed}
#' \item{clone_type}{Type of the clone, either homogeneous (HomC = clone contains cells of the same type), or heterogeneous (HetC = clone contains cells of more than one type)}
#' ...
#' }
#' @source Cerrato et al 2018
"Clones_E14_P30"
marionbetizeau/CerebellumAstrocyteSimulation documentation built on May 14, 2019, 7:20 p.m.
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#' Simulate an astrocyte lineage
#'
#' @param motherCell the information table of the mother cell
#' @param cloneOutput the initialized clone table with the 1st cell information
#' @param transitionMatrix a list specifying the transition matrix of the particular progenitor type:
#'
#' - \strong{transition.Progenitor} : list of 3 elements defining the probabilities of cell cycle exit vs amplification of the progenitor, as well as the potential types produced
#' \describe{
#' \item{probability}{Matrix with columns: probabilities of generating a MP (1st column), or an Astrocyte (2nd column),
#'
#' rows : for each generation in the simulated lineage (number of rows can vary)}
#' \item{type}{The corresponding names of different possible outcome, in the same order as the columns of the probability matrix (MP, Astro)
#' (number of types correponds to the number of columns of the above probability matrix)}
#' \item{generationInterval}{Vector of length corresponding to the number of generations, length equal to the number of rows of the matrix probability}
#' }
#'
#' - \strong{transition.Astro} : probabilities of generating the different astrocyte types after cell cycle exit
#' \describe{
#' \item{probability}{Matrix with columns: probabilities of generating a BG (1st column), a GLA (2nd column), or a WMA (3rd column)
#'
#' rows : for each generation in the simulated lineage (number of rows can vary)}
#' \item{type}{The corresponding names of different possible outcome, in the same order as the columns of the probability matrix (BG, GLA, WMA)
#' (number of types correponds to the number of columns of the above probability matrix)}
#' \item{generationInterval}{Vector of length corresponding to the number of generations, length equal to the number of rows of the matrix probability}
#' }
#'
#' - \strong{firstPostMitoticCell} : for the one-cell clones, proportions of the observed astrocyte types
#'
#' @param maxCount number of trials after which the function will stop and return an error message if the lineage never stops (stochasticity issue, the lineage stops when all
#' cells differentiate. If the probability of cell cycle exit is too low, some cell might keep on proliferating indefinitely.)
#'
#' @param parameterInterpolation boolean indicating whether interpolation was performed on the input data (always TRUE in the published simulations)
#'
#' @return cloneTable: dataframe containing the information about the simulated lineage
#' \describe{
#' \item{motherID}{ID of the mother of the current cell}
#' \item{cellID}{ID of the current cell}
#' \item{type}{Type of the current cell}
#' \item{timepoint}{Generation the current cell is born. Mother cell is initialized at generation 1}
#' }
#'
#' @seealso \code{\link{currentCell}} \code{\link{cloneTable}} \code{\link{transition.MP}} \code{\link{transition.Astro}} \code{\link{firstPostMitoticCell}}
#'
#' @examples
#' # Simulation of a lineage with a multipotent progenitor as initial cell.
#' cloneTable <- divisionMPGenerationDpd(motherCell = currentCell,
#' cloneOutput = cloneTable,
#' transitionMatrix = list(transition.MP, transition.Astro, firstPostMitoticCell),
#' maxCount = 100000)
#'
#' @export divisionMPGenerationDpd
divisionMPGenerationDpd <- function(motherCell = currentCell, cloneOutput = cloneTable,
transitionMatrix = list(transition.Progenitor, transition.Astro, firstPostMitoticCell),
maxCount = 100000,
parameterInterpolation = T){
# transition.MP : list of probability of becoming MP or Astrocyte, corresponding cell types, generationInterval : for generation dependent probabilities
# example : transition.Astro <- list(probability = matrix(c(pBG, pGLA,pWMA), nrow = length(pBG), ncol = 3), type = c("BG", "GLA","WMA"), generationInterval = generationInterval)
# checks the consistency of the input
if(parameterInterpolation == F){
if(dim(as.matrix(transitionMatrix[[2]]$probability))[1] != (length(transitionMatrix[[2]]$generationInterval)-1)
| dim(as.matrix(transitionMatrix[[2]]$probability))[2] != (length(transitionMatrix[[2]]$type))){
stop('The dimention of the Astocyte transitions are inconsistent, check the probability, type and generationInterval vectors')
}
if(dim(as.matrix(transitionMatrix[[1]]$probability))[1] != (length(transitionMatrix[[1]]$generationInterval)-1)
| dim(as.matrix(transitionMatrix[[1]]$probability))[2] != (length(transitionMatrix[[1]]$type))){
stop('The dimention of the MP transitions are inconsistent, check the probability, type and generationInterval vectors')
}
} else {
if(dim(as.matrix(transitionMatrix[[2]]$probability))[1] != (length(transitionMatrix[[2]]$generationInterval))
| dim(as.matrix(transitionMatrix[[2]]$probability))[2] != (length(transitionMatrix[[2]]$type))){
stop('The dimention of the Astocyte transitions are inconsistent, check the probability, type and generationInterval vectors')
}
if(dim(as.matrix(transitionMatrix[[1]]$probability))[1] != (length(transitionMatrix[[1]]$generationInterval))
| dim(as.matrix(transitionMatrix[[1]]$probability))[2] != (length(transitionMatrix[[1]]$type))){
stop('The dimention of the MP transitions are inconsistent, check the probability, type and generationInterval vectors')
}
if(length(transitionMatrix[[3]]$probabilities != transitionMatrix[[3]]$type)){
stop('The dimention of the firstPostMitoticCell probabilitites are inconsistent, check the type and probabilities vectors')
}
}
count <- 1
#numbermother <- dim(motherCell)[1]
while(dim(motherCell)[1]>0){
if(count > maxCount){
cloneTable <- rbind(data.frame(motherID = NA, cellID = NA, type = NA, timepoint = NA))
return(cloneTable)
stop('Did not converge')
}
motherType <- motherCell$type[1];
motherTimepoint <- motherCell$timepoint[1]; # print(paste("motherType", motherType,"mother cell:", motherCell, "timePoint:",motherTimepoint))
currentMotherID <- motherCell$cellID[1]
if(motherType %in% c("WMA", "BG", "Gla")){
motherCell <- motherCell[-1,] # remove the cell from the list of mother (will not divide)
}
if(motherType %in% "Astro"){ # can happen the intial cell in already differentiating
transition <- transitionMatrix[[3]]
cellDice <- runif(1)
cellType <- transition$type[min(which(cellDice < cumsum(transition$probability)))]
cloneTable$type[count] <- cellType
motherCell <- motherCell[-1,]
} else{
daughterDice <- runif(2)
transition <- transitionMatrix[[1]]
progType <- transition$type[1]
motherGenerationInterval <- .bincode(motherTimepoint, breaks = transition$generationInterval, right = F)
D1Type <- transition$type[min(which(daughterDice[1] < cumsum(transition$probability[motherGenerationInterval,])))]
D1ID <- max(cloneTable$cellID) + 1
D1Tp <- motherCell$timepoint[1]+1
D2Type <- transition$type[min(which(daughterDice[2] < cumsum(transition$probability[motherGenerationInterval,])))]
D2ID <- max(cloneTable$cellID) + 2
D2Tp <- motherCell$timepoint[1]+1
#currentMotherID <- motherCell$cellID[1]
motherCell <- motherCell[-1,]
if(D1Type == progType){
motherCell <- rbind(motherCell, data.frame(cellID = c(D1ID), type = c(D1Type), timepoint = D1Tp))
motherCell$type <- as.character(motherCell$type)
}else {
transition <- transitionMatrix[[2]]
motherGenerationInterval <- .bincode(motherTimepoint, breaks = transition$generationInterval, right = F)
D1Dice <- runif(1)
D1Type <- transition$type[min(which(D1Dice < cumsum(transition$probability[motherGenerationInterval,])))]
}
if(D2Type == progType){
motherCell <- rbind(motherCell, data.frame(cellID = c(D2ID), type = c(D2Type), timepoint = D2Tp))
motherCell$type <- as.character(motherCell$type)
} else{
transition <- transitionMatrix[[2]]
motherGenerationInterval <- .bincode(motherTimepoint, breaks = transition$generationInterval, right = F)
D2Dice <- runif(1)
D2Type <- transition$type[min(which(D2Dice < cumsum(transition$probability[motherGenerationInterval,])))]
}
cloneTable <- rbind(cloneTable, data.frame(motherID = currentMotherID,
cellID = c(D1ID, D2ID), type = c(D1Type, D2Type), timepoint = c(D1Tp, D2Tp)))
}
count <- count + 1; #print(count); print(cloneTable$timepoint)
}
cloneTable
}
#' Add transparency to a color
#'
#' @param col A color.
#' @param alpha The level of transparency.
#' @return The transparent color.
#' @examples
#' color1 <- add.alpha(col = "red", alpha = 0.6)
#'
#' color2 <- add.alpha(colors()[115], 0.5)
#' x <- rnorm(1000, mean = 1, sd = 0.5)
#' y <- rnorm(1000, mean = 1, sd = 0.5)
#' plot(x,y, col = c(color1, color2), pch = 16)
#'
#' @seealso \code{\link{rgb}}, \code{\link{colors}}
#'
#' @export add.alpha
add.alpha <- function(col, alpha=1){
if(missing(col))
stop("Please provide a vector of colours.")
apply(sapply(col, col2rgb)/255, 2,
function(x)
rgb(x[1], x[2], x[3], alpha=alpha))
}
getCloneSubtype <- function(cloneOutcome, cellTypes = c("BG", "GLA","WMA")){
if(sum(cloneOutcome, na.rm = T) >0){
cloneSubtype <- paste(cellTypes[cloneOutcome > 0 & is.na(cloneOutcome) == F], collapse = "_")
} else { cloneSubtype <- NA}
cloneSubtype
}
getCloneType <- function(cloneOutcome){
cellTypeNumber <- sum(cloneOutcome>0)
if(!is.na(cellTypeNumber) & cellTypeNumber < 2){
cloneType <- "HomC"
} else { cloneType <- "HetC"}
cloneType
}
#' Add error bars to a bar plot
#'
#' @param x the barplot x coordinates.
#' @param y the data (height of the bars).
#' @param upper matrix of the different error bar values.
#' @param lower matrix of the different error bar values. By default equals to upper
#' @param length length of the horizontal lines of the error bars
#'
#' @examples
#' v1 <- c(1:4)
#' my.x <- barplot(v1)
#' error <- c(0.2, 1, 0.5, 0.1)
#' error.bar(x = my.x, y = v1, upper = error)
#'
#' @export error.bar
error.bar <- function(x, y, upper, lower=upper, length=0.05,...){
if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))
stop("vectors must be same length")
arrows(x,y+upper, x, y-lower, angle=90, code=3, length=length, ...)
}
#' Add error bars to a bar plot
#'
#' @param x the barplot x coordinates.
#' @param y the data (height of the bars).
#' @param upper matrix of the different error bar values.
#' @param lower matrix of the different error bar values. By default equals to upper
#' @param length length of the horizontal lines of the error bars
#'
#' @examples
#' v1 <- c(1:4)
#' my.x <- barplot(v1)
#' error <- c(0.2, 1, 0.5, 0.1)
#' error.bar(x = my.x, y = v1, upper = error)
#'
#' @export meanSDReplicate
## Function to get the mean and SD of the different replates in the model
meanSDReplicate <- function(simulationResults, variable, replicateNumber = 5){
if(is.null(simulationResults$Replicate)){
simulationResults$Replicate <- 1
for ( i in c(1:replicateNumber)){
simulationResults$Replicate[(1 + (i-1)*numberClonesPerReplicate) : (numberClonesPerReplicate + (i-1) * numberClonesPerReplicate)] <- i
}
}
replicateNumber <- length(unique(simulationResults$Replicate))
variableCol <- which(names(simulationResults) %in% variable)
if(length(variableCol) == 0){stop("The variable chosen does not exist in the table, please check spelling and existence")}
Replicates <- t(matrix(data = unlist(with(simulationResults, tapply(simulationResults[, variableCol], INDEX = Replicate, FUN = table))), ncol = replicateNumber))
ReplicatesProp <- prop.table(Replicates, margin = 1)
meanReplicates<- apply(ReplicatesProp, MARGIN = 2, FUN = mean)
sdReplicates <- apply(ReplicatesProp, MARGIN = 2, FUN = sd)
list(mean = meanReplicates, sd = sdReplicates)
}
#' Data from observed clones after electroporation at E14 and sacrifice at P30
#'
#' A dataset containing the information of all clones measured
#'
#' @format A data frame with 102 rows and 14 variables:
#' \describe{
#' \item{animal_n}{Identifier of the animal the clone was observed in}
#' \item{clone_ID}{Identifier of the clone, combination of the different colors observed after recombination}
#' \item{time_of_electroporation_}{Development time when the electroporation was performed}
#' \item{time_of_analysis}{Time when the sacrifice and analysis were performed}
#' \item{clone_type}{Type of the clone, either homogeneous (HomC = clone contains cells of the same type), or heterogeneous (HetC = clone contains cells of more than one type)}
#' ...
#' }
#' @source Cerrato et al 2018
"Clones_E14_P30"
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