R/mplnVisualize.R
In anjalisilva/MPLNClust: Mixtures of Multivariate Poisson-Log Normal Model for Clustering Count Data
Defines functions
heatmapFunctionTwo
mplnVisualizeHeatmap
barPlotFunction
mplnVisualizeBar
linePlotMonoCol
linePlotMultiCol
mplnVisualizeLine
mplnVisualizeAlluvial
Documented in
mplnVisualizeAlluvial
mplnVisualizeBar
mplnVisualizeHeatmap
mplnVisualizeLine
#' Alluvial Plot of Multiple Clustering Results
#'
#' A function to visualize clustering results via alluvial plots,
#' using the alluvial::alluvial() function. The function produces
#' an alluvial plot provided multiple clustering results for
#' the same group of observations. Up to four varying results
#' could be visualized. Minimum, one clustering result for
#' visualization is required. Maximum 10 colors (clusters) are
#' supported. Colors are assigned based on cluster membership
#' assigned for argument 'firstGrouping'.
#'
#' @param nObservations An integer specifying the total number of
#' observations, N, in the dataset. Default value is 50L.
#' @param firstGrouping An integer vector of length nObservations (N), specifying
#' the cluster membership of observations. This must be provided. Colors
#' will be assigned based on cluster membership provided in this
#' vector. Default value is a vector of length 50.
#' @param secondGrouping An integer vector of length nObservations (N), specifying the cluster
#' membership of N observations. This could be obtained via another
#' clustering run or from a different model selection criteria.
#' Default value is an empty vector.
#' @param thirdGrouping An integer vector of length nObservations (N), specifying the cluster
#' membership of N observations. This could be obtained via another
#' clustering run or from a different model selection criteria.
#' Default value is an empty vector.
#' @param fourthGrouping An integer vector of length nObservations (N), specifying the cluster
#' membership of N observations. This could be obtained via another
#' clustering run or from a different model selection criteria.
#' Default value is an empty vector.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return An alluvial plot is returned. The x-axis values are in
#' the order of vectors assigned (if any) to firstGrouping,
#' secondGrouping, thirdGrouping and fourthGrouping, respectively.
#' Colors will be assigned based on cluster membership provided
#' for argument, firstGrouping.
#'
#' @examples
#' # Example 1
#' # Assign values for parameters
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generate simulated data for 500 x 6 dataset
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 500,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data for G = 1:2
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 2,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize clustering results using alluvial plot
#' # Access results using models selected via model selection criteria
#' alluvialPlot <- MPLNClust::mplnVisualizeAlluvial(nObservations = nrow(simulatedCounts$dataset),
#' firstGrouping = MPLNClustResults$BICresults$BICmodelSelectedLabels,
#' secondGrouping = MPLNClustResults$ICLresults$ICLmodelSelectedLabels,
#' thirdGrouping = MPLNClustResults$AIC3results$AIC3modelSelectedLabels,
#' fourthGrouping = MPLNClustResults$AICresults$AICmodelSelectedLabels,
#' fileName = paste0('Plot_',date()),
#' printPlot = FALSE,
#' format = 'pdf')
#'
#' # Example 2
#' # Perform clustering via K-means with centers = 2
#' # Visualize clustering results using alluvial plot for
#' # K-means and above MPLNClust results for BIC, ICL and AIC3.
#' # Note, coloring is set with respect to argument
#' # firstGrouping, which is assinged MPLNClust results.
#'
#' set.seed(1234)
#' alluvialPlotMPLNClust <- MPLNClust::mplnVisualizeAlluvial(
#' nObservations = nrow(simulatedCounts$dataset),
#' firstGrouping = MPLNClustResults$BICresults$BICmodelSelectedLabels,
#' secondGrouping = MPLNClustResults$ICLresults$ICLmodelSelectedLabels,
#' thirdGrouping = MPLNClustResults$AIC3results$AIC3modelSelectedLabels,
#' fourthGrouping = kmeans(simulatedCounts$dataset, 2)$cluster,
#' fileName = paste0('Plot_',date()),
#' printPlot = FALSE,
#' format = 'pdf')
#'
#' # Note, coloring is now set with respect to argument firstGrouping,
#' # which is assinged K-means results.
#' set.seed(1234)
#' alluvialPlotKmeans <- MPLNClust::mplnVisualizeAlluvial(nObservations = nrow(simulatedCounts$dataset),
#' firstGrouping = kmeans(simulatedCounts$dataset, 2)$cluster,
#' secondGrouping = MPLNClustResults$BICresults$BICmodelSelectedLabels,
#' thirdGrouping = MPLNClustResults$ICLresults$ICLmodelSelectedLabels,
#' fourthGrouping = MPLNClustResults$AIC3results$AIC3modelSelectedLabels,
#' fileName = paste0('Plot_',date()),
#' printPlot = FALSE,
#' format = 'pdf')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @references
#' Bojanowski, M., R. Edwards (2016). alluvial: R Package for
#' Creating Alluvial Diagrams. R package version 0.1-2.
#' \href{https://github.com/mbojan/alluvial}{Link}
#'
#' Wickham, H., R. François, L. Henry and K. Müller (2021).
#' dplyr: A Grammar of Data Manipulation. R package version
#' 1.0.7. \href{https://CRAN.R-project.org/package=dplyr}{Link}
#'
#' @export
#' @import graphics
#' @import alluvial
#' @importFrom dplyr case_when
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
mplnVisualizeAlluvial <- function(nObservations = 50L,
firstGrouping = floor(runif(50, min = 1, max = 8)),
secondGrouping = vector(mode = "integer", length = 0),
thirdGrouping = vector(mode = "integer", length = 0),
fourthGrouping = vector(mode = "integer", length = 0),
fileName = paste0('Plot_',date()),
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(nObservations) != "integer") {
stop("\n nObservations should be an integer")
}
if (is.vector(firstGrouping) != TRUE) {
stop("\n firstGrouping should be a vector")
} else if (length(firstGrouping) != nObservations) {
stop("\n firstGrouping should be a vector of
length nObservations (N).")
}
if (is.vector(secondGrouping) != TRUE) {
stop("\n secondGrouping should be a vector")
} else if (length(secondGrouping) != nObservations) {
stop("\n secondGrouping should be a vector of
length nObservations (N).")
}
if (is.vector(thirdGrouping) != TRUE) {
stop("\n thirdGrouping should be a vector")
} else if (length(thirdGrouping) != nObservations) {
stop("\n thirdGrouping should be a vector of
length nObservations (N).")
}
if (is.vector(fourthGrouping) != TRUE) {
stop("\n fourthGrouping should be a vector")
} else if (length(fourthGrouping) != nObservations) {
stop("\n fourthGrouping should be a vector of
length nObservations (N).")
}
# Obtaining path to save images
pathNow <- getwd()
# Setting colors based on first grouping
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4',
'#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080',
'#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3',
'#808000', '#ffd8b1',
'#000075', '#808080')
setVectorColor <- firstGrouping
if(max(firstGrouping) == 1) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
TRUE ~ "orange")
} else if(max(firstGrouping) == 2) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
TRUE ~ "orange")
} else if(max(firstGrouping) == 3) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "3" ~ coloursBarPlot[3],
TRUE ~ "orange")
} else if(max(firstGrouping) == 4) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
TRUE ~ "orange")
} else if(max(firstGrouping) == 5) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
TRUE ~ "orange")
} else if(max(firstGrouping) == 6) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
TRUE ~ "orange")
} else if(max(firstGrouping) == 7) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
TRUE ~ "orange")
} else if(max(firstGrouping) == 8) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
setVectorColor == "8" ~ coloursBarPlot[8],
TRUE ~ "orange")
} else if(max(firstGrouping) == 9) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "3" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
setVectorColor == "8" ~ coloursBarPlot[8],
setVectorColor == "9" ~ coloursBarPlot[9],
TRUE ~ "orange")
} else if(max(firstGrouping) == 10) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "3" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
setVectorColor == "8" ~ coloursBarPlot[8],
setVectorColor == "9" ~ coloursBarPlot[9],
setVectorColor == "10" ~ coloursBarPlot[10],
TRUE ~ "orange")
}
# Saving cluster membership for each observation
if(length(secondGrouping) == 0) {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:2)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation", paste0("G=", max(firstGrouping))))
} else if(length(thirdGrouping) == 0) {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Method2 = secondGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:3)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation",
paste0("G=", max(firstGrouping)),
paste0("G=", max(secondGrouping))))
} else if(length(fourthGrouping) == 0) {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Method2 = secondGrouping,
Method3 = thirdGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:4)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation",
paste0("G=", max(firstGrouping)),
paste0("G=", max(secondGrouping)),
paste0("G=", max(thirdGrouping))))
} else {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Method2 = secondGrouping,
Method3 = thirdGrouping,
Method4 = fourthGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:5)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation",
paste0("G=", max(firstGrouping)),
paste0("G=", max(secondGrouping)),
paste0("G=", max(thirdGrouping)),
paste0("G=", max(fourthGrouping))))
}
# printing plots
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/AlluvialPlot_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/AlluvialPlot_", fileName, ".pdf"))
}
plotAlluvial
grDevices::dev.off()
}
class(plotAlluvial) <- "mplnAlluvialVisual"
return(plotAlluvial)
}
#' Visualize Clustered Results Via Line Plots
#'
#' A function to visualize clustering results via line plots.
#' Each cluster will have its own plot. Data is log-transformed
#' prior to visualizing. Values for each sample are connected
#' by dashed lines to illustrate the trends (log counts). The
#' yellow line shows the mean value (log counts) for each cluster.
#'
#' @param dataset A dataset of class matrix and type integer such that
#' rows correspond to observations and columns correspond to variables.
#' @param clusterMembershipVector A numeric vector of length nrow(dataset)
#' containing the cluster membership of each observation. If not provided,
#' all observations will be treated as belonging to one cluster. Default is NA.
#' @param LinePlotColours Character string indicating if the line plots
#' should be multicoloured or monotone, in black. Options are
#' 'multicolour' or 'black'. Default is 'black'.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return Plotting function provides the possibility for line plots.
#'
#' @examples
#' # Example 1
#' # Setting parameters
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generate simulated data
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 100,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 2,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize data using line plot
#' MPLNLineBlack <- MPLNClust::mplnVisualizeLine(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'TwoClusterModel',
#' printPlot = FALSE,
#' format = 'png')
#'
#' # Visualize data using line plot with multicolours
#' # Use navigation buttons to see previous plots
#' MPLNLineColor <- MPLNClust::mplnVisualizeLine(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'TwoClusterModel',
#' LinePlotColours = "multicolour",
#' printPlot = FALSE,
#' format = 'png')
#'
#' # Example 2
#' # Carry out K-means clustering for same dataset as above
#' # Use navigation buttons to see previous plots
#' set.seed(1234)
#' KmeansLineColor <- MPLNClust::mplnVisualizeLine(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' kmeans(simulatedCounts$dataset, 3)$cluster,
#' fileName = 'ThreeClusterKmeansModel',
#' LinePlotColours = "multicolour",
#' printPlot = FALSE,
#' format = 'png')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @export
#' @import graphics
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom RColorBrewer brewer.pal
mplnVisualizeLine <- function(dataset,
clusterMembershipVector = NA,
fileName = paste0('Plot_',date()),
LinePlotColours = "black",
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(dataset) != "double" & typeof(dataset) != "integer") {
stop("\n Dataset type needs to be integer")
}
if (is.matrix(dataset) != TRUE) {
stop("\n Dataset needs to be a matrix")
}
if (is.logical(clusterMembershipVector) == TRUE) {
cat("\n clusterMembershipVector is not provided.")
clusterMembershipVector <- rep(1, nrow(dataset))
} else if (is.numeric(clusterMembershipVector) == TRUE) {
if (nrow(dataset) != length(clusterMembershipVector)) {
stop("\n length(clusterMembershipVector) should match
nrow(dataset)")
}
}
# Obtaining path to save images
pathNow <- getwd()
# Saving cluster membership for each observation
DataPlusLabs <- cbind(dataset, clusterMembershipVector)
ordervector <- anothervector <- list()
# Divide observations into each cluster based on membership
for (i in 1:max(clusterMembershipVector)) {
ordervector[[i]] <- which(DataPlusLabs[,
ncol(dataset) + 1] == i)
# divide observations as an integer based on cluster membership
anothervector[[i]] <- rep(i,
length(which(DataPlusLabs[,
ncol(dataset) + 1] == i)))
}
# Setting the colours
if(max(clusterMembershipVector) > 17) {
qualColPals <- RColorBrewer::brewer.pal.info[brewer.pal.info$category == 'qual', ]
coloursBarPlot <- unlist(mapply(RColorBrewer::brewer.pal,
qualColPals$maxcolors,
rownames(qualColPals)))
} else {
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',
'#000075', '#808080')
}
# empty plots
linePlots <- NULL
# Line Plots
if (LinePlotColours == "multicolour") {
linePlots <- list()
for(cluster in unique(clusterMembershipVector)) {
# Save how many observations below to each cluster size,
# given by 'cluster'
if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) == 1) {
toPlot2 <- as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset))
rownames(toPlot2) <- names(which(DataPlusLabs[, ncol(dataset) + 1] == cluster))
} else if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) > 1) {
toPlot2 <- as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset))
}
# Save column mean in last row
toplot1 <- rbind(log(toPlot2 + 1), colMeans(log(toPlot2 + 1)))
# If discontinunity is needed between samples (e.g. for 6 samples)
# toplot1_space=cbind(toplot1[,c(1:3)],rep(NA,nrow(toPlot2)+1),
# toplot1[,c(4:6)])
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".pdf"))
}
linePlotMultiCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
coloursBarPlot = coloursBarPlot,
cluster = cluster)
grDevices::dev.off()
}
linePlots[[cluster]] <- linePlotMultiCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
coloursBarPlot = coloursBarPlot,
cluster = cluster)
}
} else if (LinePlotColours == "black") {
linePlots <- list()
for(cluster in unique(clusterMembershipVector)) {
# Save how many observations below to each cluster size,
# given by 'cluster'
if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) == 1) {
toPlot2 <- t(as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset)))
rownames(toPlot2) <- names(which(DataPlusLabs[, ncol(dataset) + 1] == cluster))
} else if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) > 1) {
toPlot2 <- as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset))
}
# Save column mean in last row
toplot1 <- rbind(log(toPlot2 + 1), colMeans(log(toPlot2 + 1)))
# If discontinunity is needed between samples (e.g. for 6 samples)
# toplot1_space=cbind(toplot1[,c(1:3)],rep(NA,nrow(toPlot2)+1),
# toplot1[,c(4:6)])
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".pdf"))
}
linePlotMonoCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
cluster = cluster)
grDevices::dev.off()
}
linePlots[[cluster]] <- linePlotMonoCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
cluster = cluster)
}
}
return(linePlots)
}
# Helper function for line plot
linePlotMultiCol <- function(dataset,
toplot1,
toPlot2,
coloursBarPlot,
cluster) {
linePlotMultiCol <- graphics::matplot(t(toplot1), type = "l", pch = 1,
col = c(rep(coloursBarPlot[cluster], nrow(toPlot2)), 7),
xlab = "Samples", ylab = "Expression (log counts)", cex = 1,
lty = c(rep(2, nrow(toPlot2)), 1),
lwd = c(rep(3, nrow(toPlot2)), 4),
xaxt = "n", xlim = c(1, ncol(toplot1)),
main = paste("Cluster ", cluster))
linePlotMultiCol <- linePlotMultiCol + axis(1, at = c(1:ncol(dataset)), labels = colnames(dataset))
return(linePlotMultiCol)
}
# Helper function for line plot
linePlotMonoCol <- function(dataset,
toplot1,
toPlot2,
cluster) {
linePlotMonoCol <- graphics::matplot(t(toplot1), type = "l", pch = 1,
col = c(rep(1, nrow(toPlot2)), 7),
xlab = "Samples", ylab = "Expression (log counts)", cex = 1,
lty = c(rep(2, nrow(toPlot2)), 1),
lwd = c(rep(3, nrow(toPlot2)), 4),
xaxt = "n", xlim = c(1, ncol(toplot1)),
main = paste("Cluster ", cluster))
linePlotMonoCol <- linePlotMonoCol + axis(1, at = c(1:ncol(dataset)), labels = colnames(dataset))
return(linePlotMonoCol)
}
#' Visualize Posterior Probabilities via Bar Plot
#'
#' A function to produce a barplot of posterior probabilities
#' for each observation, after clustering via mixtures of
#' multivariate Poisson-log normal (MPLN) model.
#'
#' @param vectorObservations A vector of length observations (N), that
#' contains either integers or characters, specifying the observations.
#' E.g., c(1:100) for 100 different observations or c("a", "b", ...).
#' @param probabilities A matrix of numeric probabilities containing N
#' (observation) rows and G (number of maximum clusters) columns.
#' Row sums should be 1. Default value is NA.
#' @param clusterMembershipVector A numeric vector of length nrow(dataset)
#' containing the cluster membership of each observation. Default is NA.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return A bar plot of posterior probabilities for each observation
#' in the dataset.
#'
#' @examples
#' # Example 1
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generating simulated data
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 100,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 2,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize posterior probabilities via a bar plot
#' MPLNVisuals <- MPLNClust::mplnVisualizeBar(vectorObservations = 1:nrow(simulatedCounts$dataset),
#' probabilities =
#' MPLNClustResults$allResults$`G=2`$probaPost,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'PosteriorProbMPLN',
#' printPlot = FALSE,
#' format = 'png')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @export
#' @import graphics
#' @import ggplot2
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom RColorBrewer brewer.pal
#' @importFrom randomcoloR distinctColorPalette
#' @importFrom reshape melt
mplnVisualizeBar <- function(vectorObservations,
probabilities = NA,
clusterMembershipVector = NA,
fileName = paste0('Plot_',date()),
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(vectorObservations) != "double" & typeof(vectorObservations) != "integer" & typeof(vectorObservations) != "character") {
stop("\n vectorObservations type needs to be double, integer, or character only")
}
if (is.logical(clusterMembershipVector) == TRUE) {
cat("\n clusterMembershipVector is not provided.")
clusterMembershipVector <- rep(1, length(vectorObservations))
} else if (is.numeric(clusterMembershipVector) == TRUE) {
if (length(vectorObservations) != length(clusterMembershipVector)) {
stop("\n length(clusterMembershipVector) should match
length(vectorObservations)")
}
}
if (is.logical(probabilities) == TRUE) {
cat("\n Probabilities are not provided. Barplot of probabilities will not be produced.")
} else if (is.matrix(probabilities) == TRUE) {
if (nrow(probabilities) != length(clusterMembershipVector)) {
stop("\n length(probabilities) should match nrow(vectorObservations)")
}
if (any(rowSums(probabilities) >= 1.01)) {
stop("\n rowSums(probabilities) reveals at least
one observation has probability != 1.")
}
if (any(rowSums(probabilities) <= 0.99)) {
stop("\n rowSums(probabilities) reveals at least
one observation has probability != 1.")
}
}
# Obtaining path to save images
pathNow <- getwd()
# Saving cluster membership for each observation
DataPlusLabs <- cbind(vectorObservations, clusterMembershipVector)
ordervector <- anothervector <- list()
# Divide observations into each cluster based on membership
for (i in 1:max(clusterMembershipVector)) {
ordervector[[i]] <- which(DataPlusLabs[, 2] == i)
# divide observations as an integer based on cluster membership
anothervector[[i]] <- rep(i,
length(which(DataPlusLabs[, 2] == i)))
}
vec <- unlist(ordervector) # put observations in order of cluster membership
colorsvector <- unlist(anothervector) # put all details together as integers
# Setting the colours
if(max(clusterMembershipVector) > 17) {
qualColPals <- RColorBrewer::brewer.pal.info[brewer.pal.info$category == 'qual', ]
coloursBarPlot <- unlist(mapply(RColorBrewer::brewer.pal,
qualColPals$maxcolors,
rownames(qualColPals)))
} else {
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',
'#000075', '#808080')
}
# empty plot
barPlot <- NULL
# Bar plot
tableProbabilities <- as.data.frame(cbind(Sample = c(1:nrow(probabilities)),
Cluster = mclust::map(probabilities),
probabilities))
names(tableProbabilities) <- c("Sample", "Cluster",
paste0("P", rep(1:(ncol(tableProbabilities) - 2))))
tableProbabilitiesMelt <- reshape::melt(tableProbabilities,
id.vars = c("Sample", "Cluster"))
if (printPlot == TRUE) {
barPlot <- barPlotFunction(tableProbabilitiesMelt = tableProbabilitiesMelt,
coloursBarPlot = coloursBarPlot,
probabilities = probabilities)
ggplot2::ggsave(paste0(pathNow, "/barplot_", fileName, ".", format))
}
barPlot <- barPlotFunction(tableProbabilitiesMelt = tableProbabilitiesMelt,
coloursBarPlot = coloursBarPlot,
probabilities = probabilities)
return(barPlot)
}
# Helper function for bar plot
barPlotFunction <- function(tableProbabilitiesMelt,
coloursBarPlot,
probabilities) {
variable <- value <- Sample <- NULL
if(is.data.frame(tableProbabilitiesMelt) != TRUE) {
stop("tableProbabilitiesMelt should be a data frame")
}
if(is.character(coloursBarPlot) != TRUE) {
stop("coloursBarPlot should be character")
}
if(is.matrix(probabilities) != TRUE) {
stop("probabilities should be a matrix")
}
barPlot <- ggplot2::ggplot(data = tableProbabilitiesMelt,
ggplot2::aes(fill = variable, y = value, x = Sample))
barPlot <-
barPlot +
ggplot2::geom_bar(position = "fill", stat = "identity") +
ggplot2::scale_fill_manual(values = coloursBarPlot,
name = "Cluster") +
ggplot2::theme_bw() +
ggplot2::theme(text = ggplot2::element_text(size = 10),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(face = "bold"),
axis.text.y = element_text(face = "bold")) +
ggplot2::coord_cartesian(ylim = c(0, 1), xlim = c(1, nrow(probabilities))) +
ggplot2::labs(x = "Observation") +
ggplot2::scale_y_continuous(name = "Posterior probability", limits = c(0: 1))
return(barPlot)
}
#' Visualize Clustered Results Via Heatmaps
#'
#' A function to produce heatmaps of data with clustering results.
#'
#' @param dataset A dataset of class matrix and type integer such that
#' rows correspond to observations (N) and columns (C) correspond to variables.
#' @param clusterMembershipVector A numeric vector of length nrow(dataset)
#' containing the cluster membership of each observation as generated by
#' mpln(). Default is NA.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return A heatmap of data with cluster memberships.
#'
#' @examples
#' # Example 1
#' # Setting the parameters
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generating simulated data
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 100,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 3,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize data via a Heatmap for G = 2
#' MPLNHeatmap2 <- MPLNClust::mplnVisualizeHeatmap(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'TwoClusterModel',
#' printPlot = FALSE,
#' format = 'png')
#'
#' # Visualize data via a Heatmap for G = 3
#' MPLNHeatmap3 <- MPLNClust::mplnVisualizeHeatmap(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=3`$clusterlabels,
#' fileName = 'ThreeClusterModel',
#' printPlot = FALSE,
#' format = 'png')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @export
#' @import graphics
#' @import ggplot2
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom RColorBrewer brewer.pal
#' @importFrom randomcoloR distinctColorPalette
#' @importFrom pheatmap pheatmap
#' @importFrom gplots heatmap.2
#' @importFrom gplots redgreen
#' @importFrom reshape melt
mplnVisualizeHeatmap <- function(dataset,
clusterMembershipVector = NA,
fileName = paste0('Plot_',date()),
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(dataset) != "double" & typeof(dataset) != "integer") {
stop("\n Dataset type needs to be integer")
}
if (is.matrix(dataset) != TRUE) {
stop("\n Dataset needs to be a matrix")
}
if (is.logical(clusterMembershipVector) == TRUE) {
cat("\n clusterMembershipVector is not provided.")
clusterMembershipVector <- rep(1, nrow(dataset))
} else if (is.numeric(clusterMembershipVector) == TRUE) {
if (nrow(dataset) != length(clusterMembershipVector)) {
stop("\n length(clusterMembershipVector) should match
nrow(dataset)")
}
}
# Obtaining path to save images
pathNow <- getwd()
# Saving cluster membership for each observation
DataPlusLabs <- cbind(dataset, clusterMembershipVector)
ordervector <- anothervector <- list()
# Divide observations into each cluster based on membership
for (i in 1:max(clusterMembershipVector)) {
ordervector[[i]] <- which(DataPlusLabs[,
ncol(dataset) + 1] == i)
# divide observations as an integer based on cluster membership
anothervector[[i]] <- rep(i,
length(which(DataPlusLabs[,
ncol(dataset) + 1] == i)))
}
vec <- unlist(ordervector) # put observations in order of cluster membership
colorsvector <- unlist(anothervector) # put all details together as integers
# Setting the colours
if(max(clusterMembershipVector) > 17) {
qualColPals <- RColorBrewer::brewer.pal.info[brewer.pal.info$category == 'qual', ]
coloursBarPlot <- unlist(mapply(RColorBrewer::brewer.pal,
qualColPals$maxcolors,
rownames(qualColPals)))
} else {
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',
'#000075', '#808080')
}
# empty plots
heatmapOne <- heatmapTwo <- NULL
# Heatmap 1
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/heatmap1_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/heatmap1_", fileName, ".pdf"))
}
gplots::heatmap.2(as.matrix(dataset[vec, ]),
dendrogram = "column",
trace = "none",
scale = "row",
Rowv = FALSE, col = rev(gplots::redgreen(75)),
RowSideColor = coloursBarPlot[colorsvector],
labRow = FALSE,
main = paste("Clustering results, G =",
max(clusterMembershipVector)))
graphics::par(xpd = TRUE)
graphics::legend(xpd = TRUE, x = -0.1, y = 0,
legend = paste0("Cluster ", unique(colorsvector)),
col = unique(coloursBarPlot[colorsvector]),
lty = 1,
lwd = 5,
cex =.8, horiz = FALSE)
grDevices::dev.off()
}
# Heatmap 2
# Only produced if less than 17 clusters
if(max(clusterMembershipVector) < 18) {
# Defining annotation row
annotation_row = data.frame(Cluster = factor(clusterMembershipVector[vec]))
if(is.null(rownames(dataset)) == TRUE) {
rownames(dataset) = paste("Gene", c(1:nrow(dataset[vec, ])))
rownames(annotation_row) = rownames(dataset[vec, ])
} else {
rownames(annotation_row) = rownames(dataset[vec, ])
}
# Define row annotation colours
heatMap2RowAnnotation <- c("1" = coloursBarPlot[1], "2" = coloursBarPlot[2],
"3" = coloursBarPlot[3], "4" = coloursBarPlot[4],
"5" = coloursBarPlot[5], "6" = coloursBarPlot[6],
"7" = coloursBarPlot[7], "8" = coloursBarPlot[8],
"9" = coloursBarPlot[9], "10" = coloursBarPlot[10],
"11" = coloursBarPlot[11], "12" = coloursBarPlot[12],
"13" = coloursBarPlot[13], "14" = coloursBarPlot[14],
"15" = coloursBarPlot[15], "16" = coloursBarPlot[16],
"17" = coloursBarPlot[17])
# Show row names or not based on dataset size
if(nrow(dataset) < 50){
showLabels = TRUE
} else {
showLabels = FALSE
}
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/heatmap2_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/heatmap2_", fileName, ".pdf"))
}
heatmapFunctionTwo(dataset = dataset,
vec = vec,
showLabels = showLabels,
heatMap2RowAnnotation = heatMap2RowAnnotation,
annotation_row = annotation_row,
clusterMembershipVector = clusterMembershipVector)
grDevices::dev.off()
}
heatmapTwo <- heatmapFunctionTwo(dataset = dataset,
vec = vec,
showLabels = showLabels,
heatMap2RowAnnotation = heatMap2RowAnnotation,
annotation_row = annotation_row,
clusterMembershipVector = clusterMembershipVector)
}
return(list(heatmapOne,
heatmapTwo))
}
# Helper function for heatmap
heatmapFunctionTwo <- function(dataset,
vec,
showLabels,
heatMap2RowAnnotation,
annotation_row,
clusterMembershipVector) {
pheatmapPlot <- pheatmap::pheatmap(as.matrix(dataset[vec, ]), show_colnames = TRUE,
show_rownames = showLabels,
labels_col = colnames(dataset),
annotation_row = annotation_row,
annotation_colors = list(Cluster = heatMap2RowAnnotation[
sort(unique(clusterMembershipVector))]),
legend = TRUE, scale ="row",
border_color = "black", cluster_row = FALSE,
cluster_col = FALSE,
color = rev(gplots::redgreen(1000)) )
return(pheatmapPlot)
}
# [END]
anjalisilva/MPLNClust documentation built on Jan. 28, 2024, 11:02 a.m.
R Package Documentation
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Defines functions heatmapFunctionTwo mplnVisualizeHeatmap barPlotFunction mplnVisualizeBar linePlotMonoCol linePlotMultiCol mplnVisualizeLine mplnVisualizeAlluvial
Documented in mplnVisualizeAlluvial mplnVisualizeBar mplnVisualizeHeatmap mplnVisualizeLine
#' Alluvial Plot of Multiple Clustering Results
#'
#' A function to visualize clustering results via alluvial plots,
#' using the alluvial::alluvial() function. The function produces
#' an alluvial plot provided multiple clustering results for
#' the same group of observations. Up to four varying results
#' could be visualized. Minimum, one clustering result for
#' visualization is required. Maximum 10 colors (clusters) are
#' supported. Colors are assigned based on cluster membership
#' assigned for argument 'firstGrouping'.
#'
#' @param nObservations An integer specifying the total number of
#' observations, N, in the dataset. Default value is 50L.
#' @param firstGrouping An integer vector of length nObservations (N), specifying
#' the cluster membership of observations. This must be provided. Colors
#' will be assigned based on cluster membership provided in this
#' vector. Default value is a vector of length 50.
#' @param secondGrouping An integer vector of length nObservations (N), specifying the cluster
#' membership of N observations. This could be obtained via another
#' clustering run or from a different model selection criteria.
#' Default value is an empty vector.
#' @param thirdGrouping An integer vector of length nObservations (N), specifying the cluster
#' membership of N observations. This could be obtained via another
#' clustering run or from a different model selection criteria.
#' Default value is an empty vector.
#' @param fourthGrouping An integer vector of length nObservations (N), specifying the cluster
#' membership of N observations. This could be obtained via another
#' clustering run or from a different model selection criteria.
#' Default value is an empty vector.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return An alluvial plot is returned. The x-axis values are in
#' the order of vectors assigned (if any) to firstGrouping,
#' secondGrouping, thirdGrouping and fourthGrouping, respectively.
#' Colors will be assigned based on cluster membership provided
#' for argument, firstGrouping.
#'
#' @examples
#' # Example 1
#' # Assign values for parameters
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generate simulated data for 500 x 6 dataset
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 500,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data for G = 1:2
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 2,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize clustering results using alluvial plot
#' # Access results using models selected via model selection criteria
#' alluvialPlot <- MPLNClust::mplnVisualizeAlluvial(nObservations = nrow(simulatedCounts$dataset),
#' firstGrouping = MPLNClustResults$BICresults$BICmodelSelectedLabels,
#' secondGrouping = MPLNClustResults$ICLresults$ICLmodelSelectedLabels,
#' thirdGrouping = MPLNClustResults$AIC3results$AIC3modelSelectedLabels,
#' fourthGrouping = MPLNClustResults$AICresults$AICmodelSelectedLabels,
#' fileName = paste0('Plot_',date()),
#' printPlot = FALSE,
#' format = 'pdf')
#'
#' # Example 2
#' # Perform clustering via K-means with centers = 2
#' # Visualize clustering results using alluvial plot for
#' # K-means and above MPLNClust results for BIC, ICL and AIC3.
#' # Note, coloring is set with respect to argument
#' # firstGrouping, which is assinged MPLNClust results.
#'
#' set.seed(1234)
#' alluvialPlotMPLNClust <- MPLNClust::mplnVisualizeAlluvial(
#' nObservations = nrow(simulatedCounts$dataset),
#' firstGrouping = MPLNClustResults$BICresults$BICmodelSelectedLabels,
#' secondGrouping = MPLNClustResults$ICLresults$ICLmodelSelectedLabels,
#' thirdGrouping = MPLNClustResults$AIC3results$AIC3modelSelectedLabels,
#' fourthGrouping = kmeans(simulatedCounts$dataset, 2)$cluster,
#' fileName = paste0('Plot_',date()),
#' printPlot = FALSE,
#' format = 'pdf')
#'
#' # Note, coloring is now set with respect to argument firstGrouping,
#' # which is assinged K-means results.
#' set.seed(1234)
#' alluvialPlotKmeans <- MPLNClust::mplnVisualizeAlluvial(nObservations = nrow(simulatedCounts$dataset),
#' firstGrouping = kmeans(simulatedCounts$dataset, 2)$cluster,
#' secondGrouping = MPLNClustResults$BICresults$BICmodelSelectedLabels,
#' thirdGrouping = MPLNClustResults$ICLresults$ICLmodelSelectedLabels,
#' fourthGrouping = MPLNClustResults$AIC3results$AIC3modelSelectedLabels,
#' fileName = paste0('Plot_',date()),
#' printPlot = FALSE,
#' format = 'pdf')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @references
#' Bojanowski, M., R. Edwards (2016). alluvial: R Package for
#' Creating Alluvial Diagrams. R package version 0.1-2.
#' \href{https://github.com/mbojan/alluvial}{Link}
#'
#' Wickham, H., R. François, L. Henry and K. Müller (2021).
#' dplyr: A Grammar of Data Manipulation. R package version
#' 1.0.7. \href{https://CRAN.R-project.org/package=dplyr}{Link}
#'
#' @export
#' @import graphics
#' @import alluvial
#' @importFrom dplyr case_when
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
mplnVisualizeAlluvial <- function(nObservations = 50L,
firstGrouping = floor(runif(50, min = 1, max = 8)),
secondGrouping = vector(mode = "integer", length = 0),
thirdGrouping = vector(mode = "integer", length = 0),
fourthGrouping = vector(mode = "integer", length = 0),
fileName = paste0('Plot_',date()),
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(nObservations) != "integer") {
stop("\n nObservations should be an integer")
}
if (is.vector(firstGrouping) != TRUE) {
stop("\n firstGrouping should be a vector")
} else if (length(firstGrouping) != nObservations) {
stop("\n firstGrouping should be a vector of
length nObservations (N).")
}
if (is.vector(secondGrouping) != TRUE) {
stop("\n secondGrouping should be a vector")
} else if (length(secondGrouping) != nObservations) {
stop("\n secondGrouping should be a vector of
length nObservations (N).")
}
if (is.vector(thirdGrouping) != TRUE) {
stop("\n thirdGrouping should be a vector")
} else if (length(thirdGrouping) != nObservations) {
stop("\n thirdGrouping should be a vector of
length nObservations (N).")
}
if (is.vector(fourthGrouping) != TRUE) {
stop("\n fourthGrouping should be a vector")
} else if (length(fourthGrouping) != nObservations) {
stop("\n fourthGrouping should be a vector of
length nObservations (N).")
}
# Obtaining path to save images
pathNow <- getwd()
# Setting colors based on first grouping
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4',
'#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080',
'#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3',
'#808000', '#ffd8b1',
'#000075', '#808080')
setVectorColor <- firstGrouping
if(max(firstGrouping) == 1) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
TRUE ~ "orange")
} else if(max(firstGrouping) == 2) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
TRUE ~ "orange")
} else if(max(firstGrouping) == 3) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "3" ~ coloursBarPlot[3],
TRUE ~ "orange")
} else if(max(firstGrouping) == 4) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
TRUE ~ "orange")
} else if(max(firstGrouping) == 5) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
TRUE ~ "orange")
} else if(max(firstGrouping) == 6) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
TRUE ~ "orange")
} else if(max(firstGrouping) == 7) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
TRUE ~ "orange")
} else if(max(firstGrouping) == 8) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "2" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
setVectorColor == "8" ~ coloursBarPlot[8],
TRUE ~ "orange")
} else if(max(firstGrouping) == 9) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "3" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
setVectorColor == "8" ~ coloursBarPlot[8],
setVectorColor == "9" ~ coloursBarPlot[9],
TRUE ~ "orange")
} else if(max(firstGrouping) == 10) {
colSetting <- dplyr::case_when(
setVectorColor == "1" ~ coloursBarPlot[1],
setVectorColor == "2" ~ coloursBarPlot[2],
setVectorColor == "3" ~ coloursBarPlot[3],
setVectorColor == "4" ~ coloursBarPlot[4],
setVectorColor == "5" ~ coloursBarPlot[5],
setVectorColor == "6" ~ coloursBarPlot[6],
setVectorColor == "7" ~ coloursBarPlot[7],
setVectorColor == "8" ~ coloursBarPlot[8],
setVectorColor == "9" ~ coloursBarPlot[9],
setVectorColor == "10" ~ coloursBarPlot[10],
TRUE ~ "orange")
}
# Saving cluster membership for each observation
if(length(secondGrouping) == 0) {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:2)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation", paste0("G=", max(firstGrouping))))
} else if(length(thirdGrouping) == 0) {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Method2 = secondGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:3)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation",
paste0("G=", max(firstGrouping)),
paste0("G=", max(secondGrouping))))
} else if(length(fourthGrouping) == 0) {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Method2 = secondGrouping,
Method3 = thirdGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:4)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation",
paste0("G=", max(firstGrouping)),
paste0("G=", max(secondGrouping)),
paste0("G=", max(thirdGrouping))))
} else {
toVisualize <- data.frame(Observation = c(1:nObservations),
Method1 = firstGrouping,
Method2 = secondGrouping,
Method3 = thirdGrouping,
Method4 = fourthGrouping,
Freq = rep(1, nObservations))
plotAlluvial <- alluvial::alluvial(toVisualize[, c(1:5)],
freq = toVisualize$Freq,
border = NA, alpha = 0.5,
col = colSetting,
cex = 0.75,
axis_labels = c("Observation",
paste0("G=", max(firstGrouping)),
paste0("G=", max(secondGrouping)),
paste0("G=", max(thirdGrouping)),
paste0("G=", max(fourthGrouping))))
}
# printing plots
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/AlluvialPlot_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/AlluvialPlot_", fileName, ".pdf"))
}
plotAlluvial
grDevices::dev.off()
}
class(plotAlluvial) <- "mplnAlluvialVisual"
return(plotAlluvial)
}
#' Visualize Clustered Results Via Line Plots
#'
#' A function to visualize clustering results via line plots.
#' Each cluster will have its own plot. Data is log-transformed
#' prior to visualizing. Values for each sample are connected
#' by dashed lines to illustrate the trends (log counts). The
#' yellow line shows the mean value (log counts) for each cluster.
#'
#' @param dataset A dataset of class matrix and type integer such that
#' rows correspond to observations and columns correspond to variables.
#' @param clusterMembershipVector A numeric vector of length nrow(dataset)
#' containing the cluster membership of each observation. If not provided,
#' all observations will be treated as belonging to one cluster. Default is NA.
#' @param LinePlotColours Character string indicating if the line plots
#' should be multicoloured or monotone, in black. Options are
#' 'multicolour' or 'black'. Default is 'black'.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return Plotting function provides the possibility for line plots.
#'
#' @examples
#' # Example 1
#' # Setting parameters
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generate simulated data
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 100,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 2,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize data using line plot
#' MPLNLineBlack <- MPLNClust::mplnVisualizeLine(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'TwoClusterModel',
#' printPlot = FALSE,
#' format = 'png')
#'
#' # Visualize data using line plot with multicolours
#' # Use navigation buttons to see previous plots
#' MPLNLineColor <- MPLNClust::mplnVisualizeLine(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'TwoClusterModel',
#' LinePlotColours = "multicolour",
#' printPlot = FALSE,
#' format = 'png')
#'
#' # Example 2
#' # Carry out K-means clustering for same dataset as above
#' # Use navigation buttons to see previous plots
#' set.seed(1234)
#' KmeansLineColor <- MPLNClust::mplnVisualizeLine(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' kmeans(simulatedCounts$dataset, 3)$cluster,
#' fileName = 'ThreeClusterKmeansModel',
#' LinePlotColours = "multicolour",
#' printPlot = FALSE,
#' format = 'png')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @export
#' @import graphics
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom RColorBrewer brewer.pal
mplnVisualizeLine <- function(dataset,
clusterMembershipVector = NA,
fileName = paste0('Plot_',date()),
LinePlotColours = "black",
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(dataset) != "double" & typeof(dataset) != "integer") {
stop("\n Dataset type needs to be integer")
}
if (is.matrix(dataset) != TRUE) {
stop("\n Dataset needs to be a matrix")
}
if (is.logical(clusterMembershipVector) == TRUE) {
cat("\n clusterMembershipVector is not provided.")
clusterMembershipVector <- rep(1, nrow(dataset))
} else if (is.numeric(clusterMembershipVector) == TRUE) {
if (nrow(dataset) != length(clusterMembershipVector)) {
stop("\n length(clusterMembershipVector) should match
nrow(dataset)")
}
}
# Obtaining path to save images
pathNow <- getwd()
# Saving cluster membership for each observation
DataPlusLabs <- cbind(dataset, clusterMembershipVector)
ordervector <- anothervector <- list()
# Divide observations into each cluster based on membership
for (i in 1:max(clusterMembershipVector)) {
ordervector[[i]] <- which(DataPlusLabs[,
ncol(dataset) + 1] == i)
# divide observations as an integer based on cluster membership
anothervector[[i]] <- rep(i,
length(which(DataPlusLabs[,
ncol(dataset) + 1] == i)))
}
# Setting the colours
if(max(clusterMembershipVector) > 17) {
qualColPals <- RColorBrewer::brewer.pal.info[brewer.pal.info$category == 'qual', ]
coloursBarPlot <- unlist(mapply(RColorBrewer::brewer.pal,
qualColPals$maxcolors,
rownames(qualColPals)))
} else {
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',
'#000075', '#808080')
}
# empty plots
linePlots <- NULL
# Line Plots
if (LinePlotColours == "multicolour") {
linePlots <- list()
for(cluster in unique(clusterMembershipVector)) {
# Save how many observations below to each cluster size,
# given by 'cluster'
if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) == 1) {
toPlot2 <- as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset))
rownames(toPlot2) <- names(which(DataPlusLabs[, ncol(dataset) + 1] == cluster))
} else if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) > 1) {
toPlot2 <- as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset))
}
# Save column mean in last row
toplot1 <- rbind(log(toPlot2 + 1), colMeans(log(toPlot2 + 1)))
# If discontinunity is needed between samples (e.g. for 6 samples)
# toplot1_space=cbind(toplot1[,c(1:3)],rep(NA,nrow(toPlot2)+1),
# toplot1[,c(4:6)])
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".pdf"))
}
linePlotMultiCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
coloursBarPlot = coloursBarPlot,
cluster = cluster)
grDevices::dev.off()
}
linePlots[[cluster]] <- linePlotMultiCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
coloursBarPlot = coloursBarPlot,
cluster = cluster)
}
} else if (LinePlotColours == "black") {
linePlots <- list()
for(cluster in unique(clusterMembershipVector)) {
# Save how many observations below to each cluster size,
# given by 'cluster'
if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) == 1) {
toPlot2 <- t(as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset)))
rownames(toPlot2) <- names(which(DataPlusLabs[, ncol(dataset) + 1] == cluster))
} else if (length(which(DataPlusLabs[, ncol(dataset) + 1] == cluster)) > 1) {
toPlot2 <- as.matrix(DataPlusLabs[which(DataPlusLabs[,
ncol(dataset) + 1] == cluster), c(1:ncol(dataset))],
ncol = ncol(dataset))
}
# Save column mean in last row
toplot1 <- rbind(log(toPlot2 + 1), colMeans(log(toPlot2 + 1)))
# If discontinunity is needed between samples (e.g. for 6 samples)
# toplot1_space=cbind(toplot1[,c(1:3)],rep(NA,nrow(toPlot2)+1),
# toplot1[,c(4:6)])
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/LinePlot_Cluster", cluster,
"_", fileName, ".pdf"))
}
linePlotMonoCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
cluster = cluster)
grDevices::dev.off()
}
linePlots[[cluster]] <- linePlotMonoCol(dataset = dataset,
toplot1 = toplot1,
toPlot2 = toPlot2,
cluster = cluster)
}
}
return(linePlots)
}
# Helper function for line plot
linePlotMultiCol <- function(dataset,
toplot1,
toPlot2,
coloursBarPlot,
cluster) {
linePlotMultiCol <- graphics::matplot(t(toplot1), type = "l", pch = 1,
col = c(rep(coloursBarPlot[cluster], nrow(toPlot2)), 7),
xlab = "Samples", ylab = "Expression (log counts)", cex = 1,
lty = c(rep(2, nrow(toPlot2)), 1),
lwd = c(rep(3, nrow(toPlot2)), 4),
xaxt = "n", xlim = c(1, ncol(toplot1)),
main = paste("Cluster ", cluster))
linePlotMultiCol <- linePlotMultiCol + axis(1, at = c(1:ncol(dataset)), labels = colnames(dataset))
return(linePlotMultiCol)
}
# Helper function for line plot
linePlotMonoCol <- function(dataset,
toplot1,
toPlot2,
cluster) {
linePlotMonoCol <- graphics::matplot(t(toplot1), type = "l", pch = 1,
col = c(rep(1, nrow(toPlot2)), 7),
xlab = "Samples", ylab = "Expression (log counts)", cex = 1,
lty = c(rep(2, nrow(toPlot2)), 1),
lwd = c(rep(3, nrow(toPlot2)), 4),
xaxt = "n", xlim = c(1, ncol(toplot1)),
main = paste("Cluster ", cluster))
linePlotMonoCol <- linePlotMonoCol + axis(1, at = c(1:ncol(dataset)), labels = colnames(dataset))
return(linePlotMonoCol)
}
#' Visualize Posterior Probabilities via Bar Plot
#'
#' A function to produce a barplot of posterior probabilities
#' for each observation, after clustering via mixtures of
#' multivariate Poisson-log normal (MPLN) model.
#'
#' @param vectorObservations A vector of length observations (N), that
#' contains either integers or characters, specifying the observations.
#' E.g., c(1:100) for 100 different observations or c("a", "b", ...).
#' @param probabilities A matrix of numeric probabilities containing N
#' (observation) rows and G (number of maximum clusters) columns.
#' Row sums should be 1. Default value is NA.
#' @param clusterMembershipVector A numeric vector of length nrow(dataset)
#' containing the cluster membership of each observation. Default is NA.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return A bar plot of posterior probabilities for each observation
#' in the dataset.
#'
#' @examples
#' # Example 1
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generating simulated data
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 100,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 2,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize posterior probabilities via a bar plot
#' MPLNVisuals <- MPLNClust::mplnVisualizeBar(vectorObservations = 1:nrow(simulatedCounts$dataset),
#' probabilities =
#' MPLNClustResults$allResults$`G=2`$probaPost,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'PosteriorProbMPLN',
#' printPlot = FALSE,
#' format = 'png')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @export
#' @import graphics
#' @import ggplot2
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom RColorBrewer brewer.pal
#' @importFrom randomcoloR distinctColorPalette
#' @importFrom reshape melt
mplnVisualizeBar <- function(vectorObservations,
probabilities = NA,
clusterMembershipVector = NA,
fileName = paste0('Plot_',date()),
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(vectorObservations) != "double" & typeof(vectorObservations) != "integer" & typeof(vectorObservations) != "character") {
stop("\n vectorObservations type needs to be double, integer, or character only")
}
if (is.logical(clusterMembershipVector) == TRUE) {
cat("\n clusterMembershipVector is not provided.")
clusterMembershipVector <- rep(1, length(vectorObservations))
} else if (is.numeric(clusterMembershipVector) == TRUE) {
if (length(vectorObservations) != length(clusterMembershipVector)) {
stop("\n length(clusterMembershipVector) should match
length(vectorObservations)")
}
}
if (is.logical(probabilities) == TRUE) {
cat("\n Probabilities are not provided. Barplot of probabilities will not be produced.")
} else if (is.matrix(probabilities) == TRUE) {
if (nrow(probabilities) != length(clusterMembershipVector)) {
stop("\n length(probabilities) should match nrow(vectorObservations)")
}
if (any(rowSums(probabilities) >= 1.01)) {
stop("\n rowSums(probabilities) reveals at least
one observation has probability != 1.")
}
if (any(rowSums(probabilities) <= 0.99)) {
stop("\n rowSums(probabilities) reveals at least
one observation has probability != 1.")
}
}
# Obtaining path to save images
pathNow <- getwd()
# Saving cluster membership for each observation
DataPlusLabs <- cbind(vectorObservations, clusterMembershipVector)
ordervector <- anothervector <- list()
# Divide observations into each cluster based on membership
for (i in 1:max(clusterMembershipVector)) {
ordervector[[i]] <- which(DataPlusLabs[, 2] == i)
# divide observations as an integer based on cluster membership
anothervector[[i]] <- rep(i,
length(which(DataPlusLabs[, 2] == i)))
}
vec <- unlist(ordervector) # put observations in order of cluster membership
colorsvector <- unlist(anothervector) # put all details together as integers
# Setting the colours
if(max(clusterMembershipVector) > 17) {
qualColPals <- RColorBrewer::brewer.pal.info[brewer.pal.info$category == 'qual', ]
coloursBarPlot <- unlist(mapply(RColorBrewer::brewer.pal,
qualColPals$maxcolors,
rownames(qualColPals)))
} else {
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',
'#000075', '#808080')
}
# empty plot
barPlot <- NULL
# Bar plot
tableProbabilities <- as.data.frame(cbind(Sample = c(1:nrow(probabilities)),
Cluster = mclust::map(probabilities),
probabilities))
names(tableProbabilities) <- c("Sample", "Cluster",
paste0("P", rep(1:(ncol(tableProbabilities) - 2))))
tableProbabilitiesMelt <- reshape::melt(tableProbabilities,
id.vars = c("Sample", "Cluster"))
if (printPlot == TRUE) {
barPlot <- barPlotFunction(tableProbabilitiesMelt = tableProbabilitiesMelt,
coloursBarPlot = coloursBarPlot,
probabilities = probabilities)
ggplot2::ggsave(paste0(pathNow, "/barplot_", fileName, ".", format))
}
barPlot <- barPlotFunction(tableProbabilitiesMelt = tableProbabilitiesMelt,
coloursBarPlot = coloursBarPlot,
probabilities = probabilities)
return(barPlot)
}
# Helper function for bar plot
barPlotFunction <- function(tableProbabilitiesMelt,
coloursBarPlot,
probabilities) {
variable <- value <- Sample <- NULL
if(is.data.frame(tableProbabilitiesMelt) != TRUE) {
stop("tableProbabilitiesMelt should be a data frame")
}
if(is.character(coloursBarPlot) != TRUE) {
stop("coloursBarPlot should be character")
}
if(is.matrix(probabilities) != TRUE) {
stop("probabilities should be a matrix")
}
barPlot <- ggplot2::ggplot(data = tableProbabilitiesMelt,
ggplot2::aes(fill = variable, y = value, x = Sample))
barPlot <-
barPlot +
ggplot2::geom_bar(position = "fill", stat = "identity") +
ggplot2::scale_fill_manual(values = coloursBarPlot,
name = "Cluster") +
ggplot2::theme_bw() +
ggplot2::theme(text = ggplot2::element_text(size = 10),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(face = "bold"),
axis.text.y = element_text(face = "bold")) +
ggplot2::coord_cartesian(ylim = c(0, 1), xlim = c(1, nrow(probabilities))) +
ggplot2::labs(x = "Observation") +
ggplot2::scale_y_continuous(name = "Posterior probability", limits = c(0: 1))
return(barPlot)
}
#' Visualize Clustered Results Via Heatmaps
#'
#' A function to produce heatmaps of data with clustering results.
#'
#' @param dataset A dataset of class matrix and type integer such that
#' rows correspond to observations (N) and columns (C) correspond to variables.
#' @param clusterMembershipVector A numeric vector of length nrow(dataset)
#' containing the cluster membership of each observation as generated by
#' mpln(). Default is NA.
#' @param printPlot Logical indicating if plot(s) should be saved in local
#' directory. Default TRUE. Options TRUE or FALSE.
#' @param fileName Unique character string indicating the name for the plot
#' being generated. Default is Plot_date, where date is obtained from
#' date().
#' @param format Character string indicating the format of the image to
#' be produced. Default 'pdf'. Options 'pdf' or 'png'.
#'
#' @return A heatmap of data with cluster memberships.
#'
#' @examples
#' # Example 1
#' # Setting the parameters
#' trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
#' trueMu2 <- c(2, 2.5, 2, 2, 2, 2)
#'
#' trueSigma1 <- diag(6) * 2
#' trueSigma2 <- diag(6)
#'
#' # Generating simulated data
#' simulatedCounts <- MPLNClust::mplnDataGenerator(nObservations = 100,
#' dimensionality = 6,
#' mixingProportions = c(0.79, 0.21),
#' mu = rbind(trueMu1, trueMu2),
#' sigma = rbind(trueSigma1, trueSigma2),
#' produceImage = "No")
#'
#' # Clustering data
#' MPLNClustResults <- MPLNClust::mplnVariational(
#' dataset = as.matrix(simulatedCounts$dataset),
#' membership = "none",
#' gmin = 1,
#' gmax = 3,
#' initMethod = "kmeans",
#' nInitIterations = 1,
#' normalize = "Yes")
#'
#' # Visualize data via a Heatmap for G = 2
#' MPLNHeatmap2 <- MPLNClust::mplnVisualizeHeatmap(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=2`$clusterlabels,
#' fileName = 'TwoClusterModel',
#' printPlot = FALSE,
#' format = 'png')
#'
#' # Visualize data via a Heatmap for G = 3
#' MPLNHeatmap3 <- MPLNClust::mplnVisualizeHeatmap(dataset = simulatedCounts$dataset,
#' clusterMembershipVector =
#' MPLNClustResults$allResults$`G=3`$clusterlabels,
#' fileName = 'ThreeClusterModel',
#' printPlot = FALSE,
#' format = 'png')
#'
#' @author Anjali Silva, \email{anjali@alumni.uoguelph.ca}
#'
#' @export
#' @import graphics
#' @import ggplot2
#' @importFrom grDevices png
#' @importFrom grDevices pdf
#' @importFrom grDevices dev.off
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom RColorBrewer brewer.pal
#' @importFrom randomcoloR distinctColorPalette
#' @importFrom pheatmap pheatmap
#' @importFrom gplots heatmap.2
#' @importFrom gplots redgreen
#' @importFrom reshape melt
mplnVisualizeHeatmap <- function(dataset,
clusterMembershipVector = NA,
fileName = paste0('Plot_',date()),
printPlot = TRUE,
format = 'pdf') {
# Checking user input
if (typeof(dataset) != "double" & typeof(dataset) != "integer") {
stop("\n Dataset type needs to be integer")
}
if (is.matrix(dataset) != TRUE) {
stop("\n Dataset needs to be a matrix")
}
if (is.logical(clusterMembershipVector) == TRUE) {
cat("\n clusterMembershipVector is not provided.")
clusterMembershipVector <- rep(1, nrow(dataset))
} else if (is.numeric(clusterMembershipVector) == TRUE) {
if (nrow(dataset) != length(clusterMembershipVector)) {
stop("\n length(clusterMembershipVector) should match
nrow(dataset)")
}
}
# Obtaining path to save images
pathNow <- getwd()
# Saving cluster membership for each observation
DataPlusLabs <- cbind(dataset, clusterMembershipVector)
ordervector <- anothervector <- list()
# Divide observations into each cluster based on membership
for (i in 1:max(clusterMembershipVector)) {
ordervector[[i]] <- which(DataPlusLabs[,
ncol(dataset) + 1] == i)
# divide observations as an integer based on cluster membership
anothervector[[i]] <- rep(i,
length(which(DataPlusLabs[,
ncol(dataset) + 1] == i)))
}
vec <- unlist(ordervector) # put observations in order of cluster membership
colorsvector <- unlist(anothervector) # put all details together as integers
# Setting the colours
if(max(clusterMembershipVector) > 17) {
qualColPals <- RColorBrewer::brewer.pal.info[brewer.pal.info$category == 'qual', ]
coloursBarPlot <- unlist(mapply(RColorBrewer::brewer.pal,
qualColPals$maxcolors,
rownames(qualColPals)))
} else {
coloursBarPlot <- c('#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6',
'#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324',
'#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',
'#000075', '#808080')
}
# empty plots
heatmapOne <- heatmapTwo <- NULL
# Heatmap 1
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/heatmap1_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/heatmap1_", fileName, ".pdf"))
}
gplots::heatmap.2(as.matrix(dataset[vec, ]),
dendrogram = "column",
trace = "none",
scale = "row",
Rowv = FALSE, col = rev(gplots::redgreen(75)),
RowSideColor = coloursBarPlot[colorsvector],
labRow = FALSE,
main = paste("Clustering results, G =",
max(clusterMembershipVector)))
graphics::par(xpd = TRUE)
graphics::legend(xpd = TRUE, x = -0.1, y = 0,
legend = paste0("Cluster ", unique(colorsvector)),
col = unique(coloursBarPlot[colorsvector]),
lty = 1,
lwd = 5,
cex =.8, horiz = FALSE)
grDevices::dev.off()
}
# Heatmap 2
# Only produced if less than 17 clusters
if(max(clusterMembershipVector) < 18) {
# Defining annotation row
annotation_row = data.frame(Cluster = factor(clusterMembershipVector[vec]))
if(is.null(rownames(dataset)) == TRUE) {
rownames(dataset) = paste("Gene", c(1:nrow(dataset[vec, ])))
rownames(annotation_row) = rownames(dataset[vec, ])
} else {
rownames(annotation_row) = rownames(dataset[vec, ])
}
# Define row annotation colours
heatMap2RowAnnotation <- c("1" = coloursBarPlot[1], "2" = coloursBarPlot[2],
"3" = coloursBarPlot[3], "4" = coloursBarPlot[4],
"5" = coloursBarPlot[5], "6" = coloursBarPlot[6],
"7" = coloursBarPlot[7], "8" = coloursBarPlot[8],
"9" = coloursBarPlot[9], "10" = coloursBarPlot[10],
"11" = coloursBarPlot[11], "12" = coloursBarPlot[12],
"13" = coloursBarPlot[13], "14" = coloursBarPlot[14],
"15" = coloursBarPlot[15], "16" = coloursBarPlot[16],
"17" = coloursBarPlot[17])
# Show row names or not based on dataset size
if(nrow(dataset) < 50){
showLabels = TRUE
} else {
showLabels = FALSE
}
if (printPlot == TRUE) {
if (format == 'png') {
grDevices::png(paste0(pathNow, "/heatmap2_", fileName, ".png"))
} else {
grDevices::pdf(paste0(pathNow, "/heatmap2_", fileName, ".pdf"))
}
heatmapFunctionTwo(dataset = dataset,
vec = vec,
showLabels = showLabels,
heatMap2RowAnnotation = heatMap2RowAnnotation,
annotation_row = annotation_row,
clusterMembershipVector = clusterMembershipVector)
grDevices::dev.off()
}
heatmapTwo <- heatmapFunctionTwo(dataset = dataset,
vec = vec,
showLabels = showLabels,
heatMap2RowAnnotation = heatMap2RowAnnotation,
annotation_row = annotation_row,
clusterMembershipVector = clusterMembershipVector)
}
return(list(heatmapOne,
heatmapTwo))
}
# Helper function for heatmap
heatmapFunctionTwo <- function(dataset,
vec,
showLabels,
heatMap2RowAnnotation,
annotation_row,
clusterMembershipVector) {
pheatmapPlot <- pheatmap::pheatmap(as.matrix(dataset[vec, ]), show_colnames = TRUE,
show_rownames = showLabels,
labels_col = colnames(dataset),
annotation_row = annotation_row,
annotation_colors = list(Cluster = heatMap2RowAnnotation[
sort(unique(clusterMembershipVector))]),
legend = TRUE, scale ="row",
border_color = "black", cluster_row = FALSE,
cluster_col = FALSE,
color = rev(gplots::redgreen(1000)) )
return(pheatmapPlot)
}
# [END]
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