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6_deprecated.R
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6_deprecated.R
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#' get_channels
#'
#' Get channel names for an array of markers, given a flowframe
#'
#' @param ff The flowFrame of interest
#' @param markers Vector with markers or channels of interest
#'
#' @return Corresponding channel names
#'
#' @seealso \code{\link{get_markers}}
#'
#' @examples
#'
#' # Read the flowFrame
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' ff <- flowCore::read.FCS(fileName)
#' GetChannels(ff, c("FSC-A", "CD3", "FITC-A"))
#' GetMarkers(ff, c("FSC-A", "CD3", "FITC-A"))
#'
#' @export
get_channels <- function(ff, markers) {
.Deprecated("GetChannels")
channelnames <- GetChannels(ff, markers)
return(channelnames)
}
#' get_markers
#'
#' Get marker names, given a flowframe. As available in "desc". If this is NA,
#' defaults to channel name.
#'
#' @param ff The flowFrame of interest
#' @param markers Vector with markers or channels of interest
#'
#' @return Corresponding marker names
#'
#' @seealso \code{\link{get_channels}}
#'
#' @examples
#'
#' # Read the flowFrame
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' ff <- flowCore::read.FCS(fileName)
#' GetChannels(ff, c("FSC-A", "CD3", "FITC-A"))
#' GetMarkers(ff, c("FSC-A", "CD3", "FITC-A"))
#'
#' @export
get_markers <- function(ff, markers) {
.Deprecated("GetMarkers")
markernames <- GetMarkers(ff, markers)
return(markernames)
}
#' Get MFI values for all clusters
#'
#' @param fsom FlowSOM object as generated by the FlowSOM function
#' or the BuildSOM function
#' @param colsUsed logical. Should report only the columns used to
#' build the SOM. Default = FALSE.
#' @param prettyColnames logical. Should report pretty column names instead
#' of standard column names. Default = FALSE.
#'
#' @return Matrix with median values for each marker
#'
#' @examples
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' flowSOM.res <- FlowSOM(fileName, compensate=TRUE,transform=TRUE,
#' scale=TRUE,colsToUse=c(9,12,14:18),nClus=10)
#' mfis <- GetClusterMFIs(flowSOM.res)
#' @export
GetMFIs <- function(fsom, colsUsed = FALSE, prettyColnames = FALSE){
.Deprecated("GetClusterMFIs")
MFIs <- GetClusterMFIs(fsom, colsUsed, prettyColnames)
return(MFIs)
}
#' Get CV values for all clusters
#'
#' @param fsom FlowSOM object as generated by the FlowSOM function
#' or the BuildSOM function
#'
#' @return Matrix with coefficient of variation values for each marker
#'
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' flowSOM.res <- FlowSOM(fileName, compensate=TRUE,transform=TRUE,
#' scale=TRUE,colsToUse=c(9,12,14:18),nClus=10)
#' cvs <- GetClusterCVs(flowSOM.res)
#'
#' @export
GetCVs <- function(fsom){
.Deprecated("GetClusterCVs")
results <- GetClusterCVs(fsom)
return(results)
}
#' UpdateNodeSize
#'
#' Update nodesize of FlowSOM object
#'
#' Add size property to the graph based on cellcount for each node
#'
#' @param fsom FlowSOM object, as generated by \code{\link{BuildMST}}
#' @param count Absolute cell count of the sample
#' @param reset Logical. If \code{TRUE}, all nodes get the same size
#' @param transform Transformation function. Use e.g. square root to let counts
#' correspond with area of node instead of radius
#' @param maxNodeSize Maximum node size after rescaling. Default: 15
#' @param shift Shift of the counts, defaults to 0
#' @param scale Scaling of the counts, defaults to the maximum of the value
#' minus the shift. With shift and scale set as default, the
#' largest node will be maxNodesize and an empty node will have
#' size 0
#'
#' @return Updated FlowSOM object
#' @seealso \code{\link{BuildMST}}
#'
#' @examples
#' # Read from file, build self-organizing map and minimal spanning tree
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' flowSOM.res <- ReadInput(fileName, compensate=TRUE,transform=TRUE,
#' scale=TRUE)
#' flowSOM.res <- BuildSOM(flowSOM.res,colsToUse=c(9,12,14:18))
#' flowSOM.res <- BuildMST(flowSOM.res)
#'
#' # Give all nodes same size
#' PlotStars(flowSOM.res, equalNodeSize = TRUE)
#'
#' # Node sizes relative to amount of cells assigned to the node
#' PlotStars(flowSOM.res)
#'
#' @export
UpdateNodeSize <- function(fsom, count = NULL, reset=FALSE, transform=sqrt,
maxNodeSize = 15,
shift = 0, scale = NULL){
.Deprecated("the equalNodeSize and maxNodeSize parameters in the plot functions")
if(reset){
fsom$MST$size <- rep(maxNodeSize, nrow(fsom$map$grid))
} else {
t <- rep(0, fsom$map$nNodes)
names(t) <- as.character(seq_len(fsom$map$nNodes))
t_tmp <- table(fsom$map$mapping[, 1]) / nrow(fsom$map$mapping)
t[names(t_tmp)] <- t_tmp
if(!is.null(count)){
t <- (t/sum(t)) * count
}
if(!is.null(transform)){
t <- transform(t)
}
if(is.null(shift)) shift <- min(t)
if(is.null(scale)) scale <- max(t - shift)
rescaled <- maxNodeSize * (t - shift)/scale
rescaled[rescaled == 0] <- 0.0001
fsom$MST$size <- rescaled
}
fsom
}
#' PlotClusters2D
#'
#' Plot nodes on scatter plot
#'
#' Plot a 2D scatter plot. All cells of fsom$data are plotted in
#' black, and those of the selected nodes are plotted in red.
#' The nodes in the grid are indexed starting from the left bottom,
#' first going right, then up. E.g. In a 10x10 grid, the node at
#' top left will have index 91.
#'
#' @param fsom FlowSOM object, as generated by \code{\link{BuildMST}}
#' @param marker1 Marker to plot on the x-axis
#' @param marker2 Marker to plot on the y-axis
#' @param nodes Nodes of which the cells should be plotted in red
#' @param main Title of the plot
#' @param col Colors for all the cells in the selected nodes (ordered array)
#' @param maxBgPoints Maximum number of background points to plot
#' @param pchBackground Character to use for background cells
#' @param pchCluster Character to use for cells in cluster
#' @param xlab Label for the x axis
#' @param ylab Label for the y axis
#' @param xlim Limits for the x axis
#' @param ylim Limits for the y axis
#' @param ... Other parameters to pass on to plot
#'
#' @return Nothing is returned. A plot is drawn in which all cells are plotted
#' in black and the cells of the selected nodes in red.
#' @seealso \code{\link{PlotNumbers}}, \code{\link{PlotCenters}},
#' \code{\link{BuildMST}}
#'
#' @examples
#'
#' ## Deprecated - use Plot2DScatters instead ##
#'
#' # Read from file, build self-organizing map and minimal spanning tree
#' fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM")
#' flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE,
#' scale = TRUE)
#' flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18))
#' flowSOM.res <- BuildMST(flowSOM.res)
#'
#' # Plot cells
#' \dontrun{
#' Plot2DScatters(flowSOM.res, c(1, 2), clusters = 91)
#' }
#'
#' @export
PlotClusters2D <- function(fsom, marker1, marker2, nodes,
col = "#FF0000", maxBgPoints = 10000,
pchBackground = ".",
pchCluster = ".",
main = "",
xlab = fsom$prettyColnames[marker1],
ylab = fsom$prettyColnames[marker2],
xlim = c(min(fsom$data[, marker1]),
max(fsom$data[, marker1])),
ylim = c(min(fsom$data[, marker2]),
max(fsom$data[, marker2])),
...){
.Deprecated("Plot2DScatters")
marker1 <- GetChannels(fsom, marker1)
marker2 <- GetChannels(fsom, marker2)
if(!is.null(maxBgPoints)){
background <- sample(seq_len(nrow(fsom$data)),
min(maxBgPoints, nrow(fsom$data)))
} else{
background <- seq_len(nrow(fsom$data))
}
graphics::plot(fsom$data[background, c(marker1, marker2)],
pch = pchBackground, col = "#000000AA",
main = main, xlab = xlab, ylab = ylab,
xlim = xlim, ylim = ylim,
...)
graphics::points(fsom$data[GetClusters(fsom) %in% nodes,
c(marker1, marker2)],
pch = pchCluster,
col = col)
graphics::points(fsom$map$medianValues[nodes, marker1],
fsom$map$medianValues[nodes, marker2],
pch = "x", col = "blue")
#cat(nodes, ": \n", table(m[GetClusters(fsom) %in% nodes]), "\n")
}
#' PlotOverview2D
#'
#' Plot metaclusters on scatter plots
#'
#' Write multiple 2D scatter plots to a png file.
#' All cells of fsom$data are plotted in black, and those of the selected
#' metaclusters are plotted in color.
#'
#' @param fsom FlowSOM object, as generated by \code{\link{FlowSOM}}.
#' If using a FlowSOM object as generated by
#' \code{\link{BuildMST}}, it needs to be wrapped in a list,
#' list(FlowSOM = fsom, metaclustering = metaclustering).
#' @param markerlist List in which each element is a pair of marker names
#' @param metaclusters Metaclusters of interest
#' @param colors Named vector with color value for each metacluster.
#' If NULL (default) colorbrewer "paired" is interpolated
#' @param ff flowFrame to use as reference for the marker names
#' @param ... Other parameters to pass on to PlotClusters2D
#'
#' @return Nothing is returned, but a plot is drawn for every markerpair and
#' every metacluster. The individual cells are colored, and the
#' center of each FlowSOM cluster is indicated with a blue cross.
#' @seealso \code{\link{PlotClusters2D}}
#'
#' @examples
#'
#' ## Deprecated - use Plot2DScatters instead ##
#'
#' # Read from file, build self-organizing map and minimal spanning tree
#' fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM")
#' flowSOM.res <- FlowSOM(fileName,
#' compensate = TRUE, transform = TRUE, scale = TRUE,
#' colsToUse = c(9, 12, 14:18),
#' nClus = 10,
#' seed = 1)
#'
#' # Plot cells
#' markers_of_interest = list(c("FSC-A", "SSC-A"),
#' c("CD3", "CD19"),
#' c("TCRb", "TCRyd"),
#' c("CD4", "CD8"))
#' metaclusters_of_interest = 1:10
#'
#' # Recommended to write to png
#'
#' \dontrun{
#' png("Markeroverview.png",
#' width = 500 * length(markers_of_interest),
#' height = 500 * length(metaclusters_of_interest))
#' Plot2DScatters(flowSOM.res,
#' channelpairs = markers_of_interest,
#' metaclusters = metaclusters_of_interest)
#' dev.off()
#' }
#'
#' @export
PlotOverview2D <- function(fsom,
markerlist,
metaclusters,
colors = NULL,
ff,
...){
.Deprecated("Plot2DScatters")
graphics::layout(matrix(seq_len(length(markerlist) * length(metaclusters)),
nrow = length(metaclusters)))
if(is.null(colors)){
colors <- RColorBrewer::brewer.pal(12,
"Paired")
colors <- rep(colors, length.out = length(metaclusters))
names(colors) <- as.character(metaclusters)
}
for(marker_pair in markerlist){
for(metacluster in metaclusters){
PlotClusters2D(fsom,
marker1 = marker_pair[1],
marker2 = marker_pair[2],
nodes = which(fsom$metaclustering == metacluster),
main = paste0("Metacluster ", metacluster),
col = colors[as.character(metacluster)],
...)
}
}
graphics::layout(1)
}
#' MetaclusterMFIs
#'
#' Compute the median fluorescence intensities for the metaclusters
#'
#' @param fsom Result of calling the FlowSOM function
#' @return Metacluster MFIs
#' @examples
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' ff <- flowCore::read.FCS(fileName)
#' ff <- flowCore::compensate(ff,ff@@description$SPILL)
#' ff <- flowCore::transform(ff,
#' flowCore::transformList(colnames(ff@@description$SPILL),
#' flowCore::logicleTransform()))
#' flowSOM.res <- FlowSOM(ff,scale=TRUE,colsToUse=c(9,12,14:18),maxMeta=10)
#' mfis <- GetMetaclusterMFIs(flowSOM.res)
#' @export
MetaclusterMFIs <- function(fsom){
.Deprecated("GetMetaclusterMFIs")
MFIs <- GetMetaclusterMFIs(fsom)
return(MFIs)
}
#' MetaclusterCVs
#'
#' Compute the coefficient of variation for the metaclusters
#'
#' @param fsom Result of calling the FlowSOM function
#' @return Metacluster CVs
#' @examples
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' ff <- flowCore::read.FCS(fileName)
#' ff <- flowCore::compensate(ff,ff@@description$SPILL)
#' ff <- flowCore::transform(ff,
#' flowCore::transformList(colnames(ff@@description$SPILL),
#' flowCore::logicleTransform()))
#' flowSOM.res <- FlowSOM(ff,scale=TRUE,colsToUse=c(9,12,14:18), nClus=10)
#' cvs <- GetMetaclusterCVs(flowSOM.res)
#' @export
MetaclusterCVs <- function(fsom){
.Deprecated("GetMetaclusterCVs")
CVs <- GetMetaclusterCVs(fsom)
return(CVs)
}
#' PlotGroups
#'
#' Plot differences between groups
#'
#' Plot FlowSOM trees, where each node is represented by
#' a star chart indicating mean marker values, the size of the node is relative
#' to the mean percentage of cells present in each
#'
#' @param fsom FlowSOM object, as generated by \code{\link{BuildMST}}
#' @param groups Groups result as generated by \code{\link{CountGroups}}
#' @param threshold Relative difference in groups before the node is
#' coloured
#' @param pThreshold Threshold on p-value from wilcox-test before the
#' node is coloured. If this is not NULL, threshold will
#' be ignored.
#' @param ... Additional arguments to pass to \code{\link{PlotFlowSOM}}
#'
#' @return A vector containing the labels assigned to the nodes for
#' all groups except the first
#'
#' @seealso \code{\link{PlotStars}},\code{\link{PlotVariable}},
#' \code{\link{PlotFlowSOM}},\code{\link{PlotLabels}},\code{\link{PlotNumbers}},
#' \code{\link{PlotMarker}},\code{\link{PlotPies}},\code{\link{QueryStarPlot}},
#' \code{\link{PlotSD}}
#'
#' @examples
#' #Run FlowSOM
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' fsom <- FlowSOM(fileName, compensate = TRUE, transform = TRUE,
#' scale = TRUE, colsToUse = c(9,12,14:18), nClus = 10)
#'
#' ff <- flowCore::read.FCS(fileName)
#' # Make an additional file without cluster 7 and double amount of cluster 5
#' selection <- c(which(GetClusters(fsom) %in% which(fsom$metaclustering != 7)),
#' which(GetClusters(fsom) %in% which(fsom$metaclustering == 5)))
#' ff_tmp <- ff[selection,]
#' flowCore::write.FCS(ff_tmp, file="ff_tmp.fcs")
#'
#' # Compare only the file with the double amount of cluster 10
#' features <- GetFeatures(fsom,
#' c(fileName, "ff_tmp.fcs"),
#' level = "clusters",
#' type = "percentages")
#' stats <- GroupStats(features$cluster_percentages,
#' groups = list("AllCells" = c(fileName),
#' "Without_ydTcells" = c("ff_tmp.fcs")))
#'
#' fold_changes <- stats["fold changes", ]
#' fold_changes_label <- factor(ifelse(fold_changes < -1.5,
#' "Underrepresented compared to Group 1",
#' ifelse(fold_changes > 1.5,
#' "Overrepresented compared to Group 1",
#' "--")),
#' levels = c("--",
#' "Underrepresented compared to Group 1",
#' "Overrepresented compared to Group 1"))
#' fold_changes_label[is.na(fold_changes_label)] <- "--"
#' gr_1 <- PlotStars(fsom,
#' title = "All Cells",
#' nodeSizes = stats["medians AllCells", ],
#' list_insteadof_ggarrange = TRUE)
#' gr_2 <- PlotStars(fsom, title = "Group 2",
#' nodeSizes = stats["medians Without_ydTcells", ],
#' backgroundValues = fold_changes_label,
#' backgroundColors = c("white", "red", "blue"),
#' list_insteadof_ggarrange = TRUE)
#' p <- ggpubr::ggarrange(plotlist = c(list(gr_1$tree), gr_2),
#' heights = c(3, 1))
#' p
#'
#' @importFrom grDevices colorRampPalette
#'
#' @export
PlotGroups <- function(fsom,
groups,
threshold = NULL,
pThreshold = 0.05,
...){
.Deprecated("GetFeatures and Groupstats")
fsom <- UpdateFlowSOM(fsom)
groupnames <- rownames(groups$means)
annotation <- list()
groupLevels <- c("--",
paste0("Underexpressed compared to ", groupnames[1]),
paste0("Overexpressed compared to ", groupnames[1]))
for (group in groupnames[-1]) {
if (!is.null(pThreshold) & is.null(threshold)) {
values <- groups$pctgs
pValues <- c()
for (i in seq_len(ncol(values))) {
test <- stats::wilcox.test(values[groups$groups %in% groupnames[1], i],
values[groups$groups %in% group, i],
exact = FALSE)
pValues <- c(pValues, test$p.value)
}
diff <- groups$means[group, ] - groups$means[groupnames[1], ]
adjustedP <- stats::p.adjust(pValues, "BH")
score <- 1 + (adjustedP < pThreshold) + (diff > 0 & adjustedP < pThreshold)
annotation[[group]] <- factor(groupLevels[score], levels = groupLevels)
} else if (is.null(pThreshold) & !is.null(threshold)) {
diff <- groups$means[group, ] - groups$means[1, ]
values <- (apply(groups$means[c(groupnames[1], group), ], 2, max) /
apply(groups$means[c(groupnames[1], group), ], 2, min))
annotation[[group]] <- factor(groupLevels
[1 + (values > threshold) +
(diff > 0 & values > threshold)],
levels = groupLevels)
} else {
stop("Please use only threshold or pThreshold, not both.")
}
}
resultList <- list("annotation" = annotation)
groupNames <- rownames(groups$means)
p <- PlotStars(fsom,
nodeSizes = groups$means_norm[[groupNames[1]]],
title = groupNames[1],
...)
resultList[[groupNames[1]]] <- p
backgroundColors <- grDevices::colorRampPalette(
c("#FFFFFF22","#00FFFF55","#FF000055"), alpha=TRUE)
for (group in groupNames[-1]){
#nodeSizes <- groups$means_norm[[group]]
p <- PlotStars(fsom,
backgroundValues = annotation[[group]],
title = group,
backgroundColors = backgroundColors,
nodeSizes = groups$means_norm[[group]],
...)
resultList[[group]] <- p
}
return(resultList)
}
#' Calculate differences in cell counts between groups
#'
#' @param fsom FlowSOM object as generated by BuildSOM
#' @param groups List containing an array with file names for each group
#' @param plot Logical. If TRUE, make a starplot of each individual file
#' @param silent Logical. If TRUE, print progress messages
#'
#' @return Distance matrix
#'
#' @seealso Groupstats
#'
#' @examples
#' set.seed(1)
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE,
#' scale = TRUE, colsToUse = c(9,12,14:18), nClus = 10)
#'
#' ff <- flowCore::read.FCS(fileName)
#' # Make an additional file without cluster 7 and double amount of cluster 5
#' selection <- c(which(GetClusters(flowSOM.res) %in%
#' which(flowSOM.res$metaclustering != 7)),
#' which(GetClusters(flowSOM.res) %in%
#' which(flowSOM.res$metaclustering == 5)))
#' ff_tmp <- ff[selection,]
#' flowCore::write.FCS(ff_tmp, file="ff_tmp.fcs")
#'
#' # Compare only the file with the double amount of cluster 10
#' features <- GetFeatures(flowSOM.res,
#' c(fileName, "ff_tmp.fcs"),
#' level = "clusters",
#' type = "percentages")
#' stats <- GroupStats(features$cluster_percentages,
#' groups = list("AllCells" = c(fileName),
#' "Without_ydTcells" = c("ff_tmp.fcs")))
#'
#' @export
CountGroups <- function (fsom, groups, plot = TRUE, silent = FALSE)
{
.Deprecated("GetFeatures and Groupstats")
if (class(groups[[1]]) == "character") {
files <- unlist(groups)
counts <- matrix(0, nrow = length(files), ncol = fsom$map$nNodes,
dimnames = list(files, as.character(1:fsom$map$nNodes)))
for (file in files) {
if (!silent) {
print(file)
}
ff <- flowCore::read.FCS(file)
fsom_f <- NewData(fsom, ff)
if (plot) {
PlotStars(fsom_f, title = file)
}
tmp <- table(fsom_f$map$mapping[, 1])
counts[file, names(tmp)] <- tmp
}
nGroups <- lapply(groups,
length)
}
else {
counts <- do.call(rbind, groups)
nGroups <- lapply(groups,
nrow)
}
pctgs <- t(sapply(seq_len(nrow(counts)), function(i) {
counts[i, ]/rowSums(counts)[i]
}))
means <- apply(pctgs, 2, function(x) {
tapply(x, INDEX = factor(rep(names(groups), nGroups),
levels = names(groups)), mean)
})
means <- means + 0.00000000000000000001
medians <- apply(pctgs, 2, function(x) {
tapply(x, INDEX = factor(rep(names(groups), nGroups),
levels = names(groups)), stats::median)
})
medians <- medians + 0.00000000000000000001
means_norm <- list()
for (group in names(groups)) {
means_norm[[group]] <- (means[group, ] - min(means))/(max(means) -
min(means))
}
list(groups = rep(names(groups), unlist(nGroups)),
counts = counts, pctgs = pctgs, means = means, medians = medians,
means_norm = means_norm)
}
#' query_multiple
#'
#' Function which takes a named list of multiple cell types, where every item is
#' a named vector with values "high"/"low" and the names correspond to the
#' markers or channels (e.g. as generated by parse_markertable).
#'
#' @param fsom FlowSOM object
#' @param cell_types Description of the cell types. Named list, with one named
#' vector per cell type containing "high"/"low" values
#' @param pdf_name Path to a pdf file to save figures
#' @param ... Additional arguments to pass to \code{\link{QueryStarPlot}}
#'
#' @return A label for every FlowSOM cluster (Unknown or one of the celltype
#' names of the list, if selected by QueryStarPlot)
#'
#' @seealso \code{\link{QueryStarPlot}}
#'
#' @examples
#' file <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' ff <- flowCore::read.FCS(file)
#' # Use the wrapper function to build a flowSOM object (saved in flowSOM.res)
#' # and a metaclustering (saved in flowSOM.res[["metaclustering"]])
#' flowSOM.res <- FlowSOM(ff,compensate = TRUE, transform = TRUE,scale = TRUE,
#' colsToUse = c(9,12,14:18), nClus = 10, silent = FALSE,
#' xdim=7, ydim=7)
#' cell_types <- list("CD8 T cells" = c("PE-Cy7-A" = "high",
#' "APC-Cy7-A" = "high",
#' "Pacific Blue-A" = "high"),
#' "B cells" = c("PE-Cy5-A" = "high"),
#' "NK cells" = c("PE-A" = "high",
#' "PE-Cy7-A" = "low",
#' "APC-Cy7-A" = "low"))
#' query_res <- QueryMultiple(flowSOM.res, cell_types, "query_multiple.pdf")
#'
#' @export
query_multiple <- function(fsom,
cell_types,
pdf_name = "query_multiple.pdf",
...){
.Deprecated("QueryMultiple")
labels <- QueryMultiple(fsom, cell_types, pdf_name)
return(labels)
}
#' PlotNode
#' Plot star chart
#'
#' Plot a star chart indicating median marker values of a single node
#'
#' @param fsom FlowSOM object, as generated by \code{\link{BuildMST}} or
#' the first element of the list returned by
#' \code{\link{FlowSOM}}
#' @param id Id of the node to plot (check PlotNumbers to get the ids)
#' @param markers Array of markers to use. Default: the markers used to build
#' the tree
#' @param colorPalette Colorpalette to be used for the markers
#' @param main Title of the plot
#'
#' @return Nothing is returned. A plot is drawn in which the node is
#' represented by a star chart indicating the median fluorescence intensities.
#'
#' @seealso \code{\link{PlotStars}},\code{\link{PlotNumbers}},
#' \code{\link{FlowSOM}}
#'
#' @examples
#' # Read from file, build self-organizing map and minimal spanning tree
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' flowSOM.res <- FlowSOM(fileName, compensate=TRUE,transform=TRUE,
#' scale=TRUE,colsToUse=c(9,12,14:18),nClus=10)
#'
#' # Deprecated, it is currently not possible anymore to plot an individual
#' # node alone. If necessary, zooming in on a node can be approximated by
#' # exagerating the size of the node.
#' PlotStars(flowSOM.res, nodeSizes = c(100, rep(0,99)), maxNodeSize = 10)
#'
#' @export
PlotNode <- function(fsom, id,
markers=fsom$map$colsUsed,
colorPalette=grDevices::colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F",
"yellow", "#FF7F00", "red", "#7F0000")),
main=paste0("Cluster ",id)){
.Deprecated("geom_arc_bar from ggforce")
graphics::plot(1, type="n", xlab="", ylab="",
xlim=c(-10, 10), ylim=c(-3, 3),asp=1,
bty="n",xaxt="n",yaxt="n",main=main)
labels <- fsom$prettyColnames[markers]
n <- length(markers)
if(is.function(colorPalette)){colorPalette <- colorPalette(n)}
data <- rbind(apply(fsom$map$medianValues[, markers,drop=FALSE],2,min,
na.rm = TRUE),
fsom$map$medianValues[id, markers,drop=FALSE],
apply(fsom$map$medianValues[, markers,drop=FALSE],2,max,
na.rm = TRUE))
coords <- matrix(c(100,0,100,100,0,100),nrow=3)
print(data)
graphics::stars(data,col.segments=colorPalette,
locations=coords,
draw.segments = TRUE,add=TRUE,
inches=FALSE)
graphics::symbols(coords[, 1], coords[, 2], circles = c(1,1,1),
inches = FALSE, bg = "transparent", bty='n', add=TRUE)
angle <- 2*pi / n
angles <- seq(angle/2,2*pi,by=angle)
left <- (angles > (pi/2) & angles < (3*pi/2))
x <- c(2,-2)[left+1]
y_tmp <- c(seq(-2,2,by= 4/(sum(!left)+1))[-c(1,sum(!left)+2)],
seq(2,-2,by=-4/(sum(left)+1))[-c(1,sum(left)+2)])
shiftFunction <- function(x,n){
c(x[(n+1):length(x)],x[1:n])
}
y <- shiftFunction(y_tmp,max((cummax(y_tmp)<0)*seq_along(y_tmp)))
for(i in seq_along(labels)){
graphics::text(x= x[i],
y= y[i],
labels=labels[i],
adj = c(as.numeric(left)[i],0.5),
cex = 0.5)
graphics::lines(x=c(x[i]+c(-0.2,0.2)[left[i]+1],
c(1.5,-1.5)[left[i]+1],
cos(angles[i])),
y=c(y[i],
y[i],
sin(angles[i])),
col=colorPalette[i],
lwd=2)
}
}
#' PlotCenters
#'
#' Plot cluster centers on a 2D plot
#'
#' Plot FlowSOM nodes on a 2D scatter plot of the data
#'
#' @param fsom FlowSOM object, as generated by \code{\link{BuildMST}}
#' @param marker1 Marker to show on the x-axis
#' @param marker2 Marker to show on the y-axis
#' @param MST Type of visualization, if 1 plot tree, else plot grid
#' @return Nothing is returned. A 2D scatter plot is drawn on which the nodes
#' of the grid are indicated
#'
#' @seealso \code{\link{PlotStars}},\code{\link{PlotPies}},
#' \code{\link{PlotMarker}},\code{\link{BuildMST}}
#'
#' @examples
#' # Read from file, build self-organizing map and minimal spanning tree
#' fileName <- system.file("extdata", "68983.fcs", package="FlowSOM")
#' flowSOM.res <- ReadInput(fileName, compensate=TRUE,transform=TRUE,
#' scale=TRUE)
#' flowSOM.res <- BuildSOM(flowSOM.res,colsToUse=c(9,12,14:18))
#' flowSOM.res <- BuildMST(flowSOM.res)
#'
#' # Plot centers
#' plot <- Plot2DScatters(flowSOM.res,
#' channelpairs = list(c("FSC-A","SSC-A")),
#' clusters = list(seq_len(NClusters(flowSOM.res))),
#' maxPoints = 0,
#' plotFile = NULL)
#'
#' @export
PlotCenters <- function(fsom, marker1, marker2, MST=TRUE){
.Deprecated("Plot2DScatters")
graphics::plot(fsom$data[, c(marker1, marker2)], pch=".", col="#000000AA")
d <- fsom$map$medianValues[, c(marker1, marker2)]
graphics::points(d,col="red")
if(MST==1){
g <- fsom$MST$graph
e <- get.edges(g, E(g))
} else {
e <- which(as.matrix(
stats::dist(fsom$map$grid,method = "manhattan"))==1,
arr.ind=TRUE)
}
for(i in seq_len(nrow(e))){
graphics::lines(d[c(e[i, 1], e[i, 2]), 1],
d[c(e[i, 1], e[i, 2]), 2],
col="red")
}
}