| Title: | Using self-organizing maps for visualization and interpretation of cytometry data |
|---|---|
| Description: | FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. |
| Authors: | Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien Quintelier [aut], Annelies Emmaneel [aut], Britt Callebaut [aut], Yvan Saeys [aut] |
| Maintainer: | Sofie Van Gassen <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.15.0 |
| Built: | 2024-10-30 07:20:07 UTC |
| Source: | https://github.com/bioc/FlowSOM |
Add annotation to a FlowSOM plot
AddAnnotation( p, fsom, toAnnotate = NULL, prefix = list(metaclusters = "MCL ", clusters = "CL "), ... )AddAnnotation( p, fsom, toAnnotate = NULL, prefix = list(metaclusters = "MCL ", clusters = "CL "), ... )
p |
Plot to add annotation to. When using |
fsom |
FlowSOM object that goes with the plot. |
toAnnotate |
A named list with "metaclusters" and/or "clusters" as names and a vector with the (meta)clusters that need to be annotated. Names can be abbreviated. Use a named vector with the old names as values and new labels as names for custom labeling. |
prefix |
Prefix to be added to labels. Default is "MCL " and "CL " for metaclusters and clusters respectively. |
... |
Arguments passed to geom_text_repel. |
The updated plot
# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) p <- PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering, list_insteadof_ggarrange = TRUE) annotationList <- list("metaclusters" = c("CD8 T cells" = "1", "B cells" = "8"), "clusters" = c(97)) AddAnnotation(p, flowSOM.res, toAnnotate = annotationList, prefix = list("metaclusters" = "", clusters = "CL "))# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) p <- PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering, list_insteadof_ggarrange = TRUE) annotationList <- list("metaclusters" = c("CD8 T cells" = "1", "B cells" = "8"), "clusters" = c(97)) AddAnnotation(p, flowSOM.res, toAnnotate = annotationList, prefix = list("metaclusters" = "", clusters = "CL "))
Function plots the background
AddBackground( p, backgroundValues, backgroundColors = NULL, backgroundLim = NULL )AddBackground( p, backgroundValues, backgroundColors = NULL, backgroundLim = NULL )
p |
ggplot object |
backgroundValues |
Vector of values to be plotted as background for the nodes |
backgroundColors |
Color palette to be used for the background coloring. Can be either a function or an array specifying colors. |
backgroundLim |
Background limits (can be used to ensure consistent Color palette between plots). If NULL (default), will be automatically adapted to the data. |
Returns nothing, but plots the background
PlotFlowSOM, AddLabels,
AddNodes, AddPies, AddStars
Add a flowFrame to the data variable of the FlowSOM object
AddFlowFrame(fsom, flowFrame)AddFlowFrame(fsom, flowFrame)
fsom |
FlowSOM object, as constructed by the ReadInput function |
flowFrame |
flowFrame to add to the FlowSOM object |
FlowSOM object with data added
AddLabels
AddLabels( p, labels, hjust = 0.5, layout = NULL, textSize = 3.88, textColor = "black", ... )AddLabels( p, labels, hjust = 0.5, layout = NULL, textSize = 3.88, textColor = "black", ... )
p |
ggplot object |
labels |
Labels to be added to each node |
hjust |
Horizontal adjust for labels. Default is centered. |
layout |
Dataframe with x and y columns. If null, the dataframe from the ggplot object will be reused. |
textSize |
Size for geom_text. Default (=3.88) is from geom_text. |
textColor |
Color for geom_text. Default = black. |
... |
Additional parameters to pass to geom_text |
Returns the ggplot object with labels added
Function plots the MST
AddMST(p, fsom)AddMST(p, fsom)
p |
ggplot object |
fsom |
FlowSOM object, as generated by |
Returns nothing, but plots the MST for FlowSOM MST view
PlotFlowSOM, ParseEdges,
AddStarsPies, AddLabels, AddNodes,
AddBackground, AddPies, AddStars
Function plots the nodes
AddNodes( p, nodeInfo = NULL, values = NULL, lim = NULL, colorPalette = NULL, fillColor = "white", showLegend = TRUE, label = "", ... )AddNodes( p, nodeInfo = NULL, values = NULL, lim = NULL, colorPalette = NULL, fillColor = "white", showLegend = TRUE, label = "", ... )
p |
ggplot object |
nodeInfo |
Dataframe with for every node an x, y and size value, if null the dataframe from the ggplot object will be reused. |
values |
Values used for coloring the nodes. Default = NULL, in which case all nodes are filled in fillColor. |
lim |
The limits of the color scale, not used if values = NULL. |
colorPalette |
Color palette for color in nodes, not used if values = NULL. A vector of colors or a color function. |
fillColor |
Fixed fill for node colors, default = white. |
showLegend |
Boolean, default = TRUE. |
label |
Title for the legend. |
... |
Additional arguments to pass to geom_circle |
Returns nothing, but plots the nodes
PlotFlowSOM, PlotMarker,
PlotVariable, AddLabels,
AddBackground, AddPies,
AddStars, AddStarsPies
Function plots the pies
AddPies(p, fsom, cellLabels, layout = NULL, colorPalette = NULL)AddPies(p, fsom, cellLabels, layout = NULL, colorPalette = NULL)
p |
ggplot object |
fsom |
FlowSOM object, as generated by |
cellLabels |
Array of factors indicating the cell labels |
layout |
Coordinates of nodes. Uses dataframe of the ggplot object if NULL. |
colorPalette |
Color palette to be used for colors. Can be either a function or an array specifying colors. |
ggplot object with the pies added
PlotFlowSOM, AddLabels,
AddNodes, AddBackground, PlotPies,
AddStars, ParseArcs
AddScale
AddScale( p, values = NULL, colors = NULL, limits = NULL, showLegend = TRUE, labelLegend = "", type = "fill" )AddScale( p, values = NULL, colors = NULL, limits = NULL, showLegend = TRUE, labelLegend = "", type = "fill" )
p |
ggplot object |
values |
Values used for the fill |
colors |
Colors to use (can be a vector or a function) |
limits |
Limits to use in the scale |
showLegend |
Boolean on whether to show the legend |
labelLegend |
Label to show as title of the legend |
type |
fill (default) or color |
ggplot object with scale added
Function plots the stars
AddStars(p, fsom, markers = fsom$map$colsUsed, colorPalette = NULL)AddStars(p, fsom, markers = fsom$map$colsUsed, colorPalette = NULL)
p |
ggplot object |
fsom |
FlowSOM object, as generated by |
markers |
Determines which markers to plot. Default = "fsom$map$colsUsed" |
colorPalette |
Color palette to be used for colors. Can be either a function or an array specifying colors. |
ggplot object with the stars added
PlotFlowSOM, AddLabels,
AddNodes, AddBackground, PlotStars,
AddPies, ParseArcs
Function plots stars or pies
AddStarsPies(p, arcs, colorPalette, showLegend = TRUE)AddStarsPies(p, arcs, colorPalette, showLegend = TRUE)
p |
ggplot object |
arcs |
Dataframe that contains all the data for the plotting the pies or stars |
colorPalette |
A vector of colors or a color function |
showLegend |
Boolean on whether to show the legend |
Returns nothing, but plots the stars or pies
PlotFlowSOM, AddLabels,
AddNodes, AddBackground, AddPies,
AddStars, ParseArcs, PlotStars
PlotPies
Aggregate multiple FCS files to analyze them simultaneously.
A new FCS file is written, which contains about cTotal cells,
with ceiling(cTotal/nFiles) cells from each file. Two new columns
are added: a column indicating the original file by index, and a noisy
version of this for better plotting opportunities (index plus or minus a
value between 0 and 0.1).
AggregateFlowFrames( fileNames, cTotal, channels = NULL, writeOutput = FALSE, outputFile = "aggregate.fcs", keepOrder = FALSE, silent = FALSE, sampleWithReplacement = FALSE, ... )AggregateFlowFrames( fileNames, cTotal, channels = NULL, writeOutput = FALSE, outputFile = "aggregate.fcs", keepOrder = FALSE, silent = FALSE, sampleWithReplacement = FALSE, ... )
fileNames |
Character vector containing full paths to the FCS files or a flowSet to aggregate |
cTotal |
Total number of cells to write to the output file |
channels |
Channels/markers to keep in the aggregate. Default NULL takes all channels of the first file. |
writeOutput |
Whether to write the resulting flowFrame to a file. Default FALSE |
outputFile |
Full path to output file. Default "aggregate.fcs" |
keepOrder |
If TRUE, the random subsample will be ordered in the same way as they were originally ordered in the file. Default = FALSE. |
silent |
If FALSE, prints an update every time it starts processing a new file. Default = FALSE. |
sampleWithReplacement |
If TRUE and more cells per file are requested than actually present, all cells will be included plus additional resampling. Otherwise, at most all cells will be included once. Default = FALSE. |
... |
Additional arguments to pass to read.FCS |
This function does not return anything, but will write a file with
about cTotal cells to outputFile
# Define filename fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") # This example will sample 2 times 500 cells. ff_new <- AggregateFlowFrames(c(fileName, fileName), 1000)# Define filename fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") # This example will sample 2 times 500 cells. ff_new <- AggregateFlowFrames(c(fileName, fileName), 1000)
Calculate node size
AutoMaxNodeSize(layout, overlap)AutoMaxNodeSize(layout, overlap)
layout |
Coordinates of nodes |
overlap |
Parameter that determines how much overlap there will be. If negative the nodes will be smaller |
Function that calculates the minimum distance between the nodes to use this to adapt the maxNodeSize for better plotting
Returns the maxNodeSize with some overlap
PlotFlowSOM, ScaleStarHeights,
ParseNodeSize
Build Minimal Spanning Tree
BuildMST(fsom, silent = FALSE, tSNE = FALSE)BuildMST(fsom, silent = FALSE, tSNE = FALSE)
fsom |
FlowSOM object, as generated by |
silent |
If |
tSNE |
If |
Add minimal spanning tree description to the FlowSOM object
FlowSOM object containing MST description
# Read from file, build self-organizing map 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)) # Build the Minimal Spanning Tree flowSOM.res <- BuildMST(flowSOM.res)# Read from file, build self-organizing map 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)) # Build the Minimal Spanning Tree flowSOM.res <- BuildMST(flowSOM.res)
Build a SOM based on the data contained in the FlowSOM object
BuildSOM(fsom, colsToUse = NULL, silent = FALSE, outlierMAD = 4, ...)BuildSOM(fsom, colsToUse = NULL, silent = FALSE, outlierMAD = 4, ...)
fsom |
FlowSOM object containing the data, as constructed by
the |
colsToUse |
Markers, channels or indices to use for building the SOM |
silent |
if |
outlierMAD |
Number of MAD when a cell is considered an outlier.
See also |
... |
options to pass on to the SOM function (xdim, ydim, rlen, mst, alpha, radius, init, distf, importance) |
FlowSOM object containing the SOM result, which can be used as input
for the BuildMST function
This code is strongly based on the kohonen package.
R. Wehrens and L.M.C. Buydens, Self- and Super-organising Maps
in R: the kohonen package J. Stat. Softw., 21(5), 2007
# Read from file fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE, scale = TRUE) # Build the Self-Organizing Map # E.g. with gridsize 5x5, presenting the dataset 20 times, # no use of MST in neighborhood calculations in between flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18), xdim = 5, ydim = 5, rlen = 20) # Build the minimal spanning tree and apply metaclustering flowSOM.res <- BuildMST(flowSOM.res) metacl <- MetaClustering(flowSOM.res$map$codes, "metaClustering_consensus", max = 10)# Read from file fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE, scale = TRUE) # Build the Self-Organizing Map # E.g. with gridsize 5x5, presenting the dataset 20 times, # no use of MST in neighborhood calculations in between flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18), xdim = 5, ydim = 5, rlen = 20) # Build the minimal spanning tree and apply metaclustering flowSOM.res <- BuildMST(flowSOM.res) metacl <- MetaClustering(flowSOM.res$map$codes, "metaClustering_consensus", max = 10)
Calculate differences in cell counts between groups
CountGroups(fsom, groups, plot = TRUE, silent = FALSE)CountGroups(fsom, groups, plot = TRUE, silent = FALSE)
fsom |
FlowSOM object as generated by BuildSOM |
groups |
List containing an array with file names for each group |
plot |
Logical. If TRUE, make a starplot of each individual file |
silent |
Logical. If TRUE, print progress messages |
Distance matrix
GroupStats
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$cluster_percentages, groups = list("AllCells" = c(fileName), "Without_ydTcells" = c("ff_tmp.fcs")))set.seed(1) fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9,12,14:18), nClus = 10) ff <- flowCore::read.FCS(fileName) # Make an additional file without cluster 7 and double amount of cluster 5 selection <- c(which(GetClusters(flowSOM.res) %in% which(flowSOM.res$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")))
Calculate distance matrix using a minimal spanning tree neighborhood
Dist.MST(X)Dist.MST(X)
X |
matrix in which each row represents a point |
Distance matrix
Method to run general FlowSOM workflow. Will scale the data and uses consensus meta-clustering by default.
FlowSOM( input, pattern = ".fcs", compensate = FALSE, spillover = NULL, transform = FALSE, toTransform = NULL, transformFunction = flowCore::logicleTransform(), transformList = NULL, scale = FALSE, scaled.center = TRUE, scaled.scale = TRUE, silent = TRUE, colsToUse = NULL, nClus = 10, maxMeta = NULL, importance = NULL, seed = NULL, ... )FlowSOM( input, pattern = ".fcs", compensate = FALSE, spillover = NULL, transform = FALSE, toTransform = NULL, transformFunction = flowCore::logicleTransform(), transformList = NULL, scale = FALSE, scaled.center = TRUE, scaled.scale = TRUE, silent = TRUE, colsToUse = NULL, nClus = 10, maxMeta = NULL, importance = NULL, seed = NULL, ... )
input |
a flowFrame, a flowSet, a matrix with column names or an array of paths to files or directories |
pattern |
if input is an array of file- or directorynames, select only files containing pattern |
compensate |
logical, does the data need to be compensated |
spillover |
spillover matrix to compensate with If NULL and compensate = TRUE, we will look for $SPILL description in FCS file. |
transform |
logical, does the data need to be transformed with the
transformation given in |
toTransform |
column names or indices that need to be transformed.
Will be ignored if |
transformFunction |
Defaults to logicleTransform() |
transformList |
transformList to apply on the samples. |
scale |
logical, does the data needs to be rescaled. Default = FALSE |
scaled.center |
see |
scaled.scale |
see |
silent |
if |
colsToUse |
Markers, channels or indices to use for building the SOM. Default (NULL) is all the columns used to build the FlowSOM object. |
nClus |
Exact number of clusters for meta-clustering. Ignored if maxMeta is specified. Default = 10. |
maxMeta |
Maximum number of clusters to try out for
meta-clustering. If |
importance |
array with numeric values. Parameters will be scaled according to importance |
seed |
Set a seed for reproducible results |
... |
options to pass on to the SOM function (xdim, ydim, rlen, mst, alpha, radius, init, distf) |
A list with two items: the first is the flowSOM object
containing all information (see the vignette for more detailed
information about this object), the second is the metaclustering of
the nodes of the grid. This is a wrapper function for
ReadInput, BuildSOM,
BuildMST and MetaClustering.
Executing them separately may provide more options.
scale,
ReadInput,
BuildSOM,
BuildMST,
MetaClustering
# Read from file 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) # Or read from flowFrame object ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Plot results PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) # Get metaclustering per cell flowSOM.clustering <- GetMetaclusters(flowSOM.res)# Read from file 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) # Or read from flowFrame object ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Plot results PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) # Get metaclustering per cell flowSOM.clustering <- GetMetaclusters(flowSOM.res)
FlowSOM default colors
FlowSOM_colors(n)FlowSOM_colors(n)
n |
Number of colors to generate |
array of n colors
This functions plots a summary of a flowSOM object. It includes a table of (meta)cluster data, the flowSOM trees and grid view, the (meta)cluster labels, the markers expression, the file distribution if present, the cluster per metacluster percentage, a t-SNE plot, and the MFI per metacluster.
FlowSOMmary(fsom, plotFile = "FlowSOMmary.pdf")FlowSOMmary(fsom, plotFile = "FlowSOMmary.pdf")
fsom |
FlowSOM object, as generated by |
plotFile |
Name of the pdf file that will be generated (default is
FlowSOMmary.pdf). If |
Returns a summary of the FlowSOM object
# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) FlowSOMmary(flowSOM.res)# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) FlowSOMmary(flowSOM.res)
FlowSOM subset
FlowSOMSubset(fsom, ids)FlowSOMSubset(fsom, ids)
fsom |
FlowSOM object, as generated by |
ids |
Array containing the ids to keep |
Take a subset from a FlowSOM object
FlowSOM object containing updated data and median values, but with the same grid
# Read two files (Artificially, as we just split 1 file in 2 subsets) fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff1 <- flowCore::read.FCS(fileName)[1:1000, ] flowCore::keyword(ff1)[["FIL"]] <- "File1" ff2 <- flowCore::read.FCS(fileName)[1001:2000, ] flowCore::keyword(ff2)[["FIL"]] <- "File2" flowSOM.res <- FlowSOM(flowCore::flowSet(c(ff1, ff2)), compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), maxMeta = 10) # see $metadata for subsets: flowSOM.res$metaData # Use only the second file, without changing the map fSOM2 <- FlowSOMSubset(flowSOM.res, (flowSOM.res$metaData[[2]][1]): (flowSOM.res$metaData[[2]][2]))# Read two files (Artificially, as we just split 1 file in 2 subsets) fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff1 <- flowCore::read.FCS(fileName)[1:1000, ] flowCore::keyword(ff1)[["FIL"]] <- "File1" ff2 <- flowCore::read.FCS(fileName)[1001:2000, ] flowCore::keyword(ff2)[["FIL"]] <- "File2" flowSOM.res <- FlowSOM(flowCore::flowSet(c(ff1, ff2)), compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), maxMeta = 10) # see $metadata for subsets: flowSOM.res$metaData # Use only the second file, without changing the map fSOM2 <- FlowSOMSubset(flowSOM.res, (flowSOM.res$metaData[[2]][1]): (flowSOM.res$metaData[[2]][2]))
Compute the F measure between two clustering results
FMeasure(realClusters, predictedClusters, silent = FALSE)FMeasure(realClusters, predictedClusters, silent = FALSE)
realClusters |
Array containing real cluster labels for each sample |
predictedClusters |
Array containing predicted cluster labels for each sample |
silent |
Logical, if FALSE (default), print some information about precision and recall |
F measure score
# Generate some random data as an example realClusters <- sample(1:5,100,replace = TRUE) predictedClusters <- sample(1:6, 100, replace = TRUE) # Calculate the FMeasure FMeasure(realClusters,predictedClusters)# Generate some random data as an example realClusters <- sample(1:5,100,replace = TRUE) predictedClusters <- sample(1:6, 100, replace = TRUE) # Calculate the FMeasure FMeasure(realClusters,predictedClusters)
Get channel names for an array of markers, given a flowFrame
get_channels(ff, markers)get_channels(ff, markers)
ff |
The flowFrame of interest |
markers |
Vector with markers or channels of interest |
Corresponding channel names
# 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"))# 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"))
Get marker names, given a flowFrame. As available in "desc". If this is NA, defaults to channel name.
get_markers(ff, markers)get_markers(ff, markers)
ff |
The flowFrame of interest |
markers |
Vector with markers or channels of interest |
Corresponding marker names
# 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"))# 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"))
Get channel names for an array of markers, given a flowFrame or a FlowSOM
object. As available in "name". grep is used to look for the
markers. Other regex can be added.
GetChannels(object, markers, exact = TRUE)GetChannels(object, markers, exact = TRUE)
object |
The flowFrame or the FlowSOM object of interest |
markers |
Vector with markers or channels of interest. Also accepts the index of the marker found in the object. |
exact |
If TRUE (default), the grep pattern will be extended to start with ^\\Q and end with \\E$, so only exact matches are possible. |
Corresponding channel names
# 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"))# 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"))
Get CV values for all clusters
GetClusterCVs(fsom)GetClusterCVs(fsom)
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
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)
Get MFI values for all clusters
GetClusterMFIs(fsom, colsUsed = FALSE, prettyColnames = FALSE)GetClusterMFIs(fsom, colsUsed = FALSE, prettyColnames = FALSE)
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
colsUsed |
logical. Should report only the columns used to build the SOM. Default = FALSE. |
prettyColnames |
logical. Should report pretty column names instead of standard column names. Default = FALSE. |
Matrix with median 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) mfis <- GetClusterMFIs(flowSOM.res)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)
Get percentage-positive values for all clusters
GetClusterPercentagesPositive( fsom, cutoffs, colsUsed = FALSE, prettyColnames = FALSE )GetClusterPercentagesPositive( fsom, cutoffs, colsUsed = FALSE, prettyColnames = FALSE )
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
cutoffs |
named numeric vector. Upper bounds of negative population fluorescence-intensity values for each marker / channel. |
colsUsed |
logical. Should report only the columns used to build the SOM. Default = FALSE. |
prettyColnames |
logical. Should report pretty column names instead of standard column names. Default = FALSE. |
Matrix with percentages of cells that are positive in selected markers per each cluster
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) perc_pos <- GetClusterPercentagesPositive(flowSOM.res, cutoffs = c('CD4' = 5000))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) perc_pos <- GetClusterPercentagesPositive(flowSOM.res, cutoffs = c('CD4' = 5000))
Get cluster label for all individual cells
GetClusters(fsom)GetClusters(fsom)
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
vector label for every cell
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) cluster_labels <- GetClusters(flowSOM.res)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) cluster_labels <- GetClusters(flowSOM.res)
Get counts of number of cells in clusters or metaclusters
GetCounts(fsom, level = "metaclusters")GetCounts(fsom, level = "metaclusters")
fsom |
FlowSOM object |
level |
Character string, should be either "clusters" or "metaclusters" (default) or abbreviations. |
A named vector with the counts
# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::estimateLogicle(ff, flowCore::colnames(ff)[8:18])) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) GetCounts(flowSOM.res) GetCounts(flowSOM.res, level = "clusters")# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::estimateLogicle(ff, flowCore::colnames(ff)[8:18])) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) GetCounts(flowSOM.res) GetCounts(flowSOM.res, level = "clusters")
Get CV values for all clusters
GetCVs(fsom)GetCVs(fsom)
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
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)
Map FCS files on an existing FlowSOM object
GetFeatures( fsom, files, level = c("clusters", "metaclusters"), type = "counts", MFI = NULL, positive_cutoffs = NULL, filenames = NULL, silent = FALSE )GetFeatures( fsom, files, level = c("clusters", "metaclusters"), type = "counts", MFI = NULL, positive_cutoffs = NULL, filenames = NULL, silent = FALSE )
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
files |
Either a vector of FCS files or paths to FCS files |
level |
Level(s) of interest. Default is c("clusters", "metaclusters"), but can also be only one of them. Can be abbreviated. |
type |
Type of features to extract. Default is "counts", can be a vector of "counts", "percentages", "MFIs" and/or "percentages_positive" or abbreviations. |
MFI |
Vector with channels / markers for which the MFI
values must be returned when "MFIs" is in |
positive_cutoffs |
Named vector with fluorescence-intensity values
per channel / marker that are the upper bounds for
a negative population when "percentages_positive" is
in |
filenames |
An optional vector with filenames that will be used as rownames in the count matrices. If NULL (default) either the paths will be used or a numerical vector. |
silent |
Logical. If |
matrix with features per population - type combination
# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff[1:1000, ], scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Map new data counts <- GetFeatures(fsom = flowSOM.res, level = "clusters", files = c(ff[1001:2000, ], ff[2001:3000, ])) features <- GetFeatures(fsom = flowSOM.res, files = c(ff[1001:2000, ], ff[2001:3000, ]), type = c("counts", "percentages", "MFIs"), MFI = "APC-A", filenames = c("ff_1001-2000", "ff_2001-3000")) # Get percentages of positive cells positive_cutoffs <- c('CD8' = 1.5, 'CD4' = 0.3, 'CD19' = 1.3, 'CD3' = -0.3) perc_pos <- GetFeatures(fsom = flowSOM.res, files = c(ff[1001:2000, ], ff[2001:3000, ]), type = c("percentages_positive"), positive_cutoffs = positive_cutoffs, filenames = c("ff_1001-2000", "ff_2001-3000"))# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff[1:1000, ], scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Map new data counts <- GetFeatures(fsom = flowSOM.res, level = "clusters", files = c(ff[1001:2000, ], ff[2001:3000, ])) features <- GetFeatures(fsom = flowSOM.res, files = c(ff[1001:2000, ], ff[2001:3000, ]), type = c("counts", "percentages", "MFIs"), MFI = "APC-A", filenames = c("ff_1001-2000", "ff_2001-3000")) # Get percentages of positive cells positive_cutoffs <- c('CD8' = 1.5, 'CD4' = 0.3, 'CD19' = 1.3, 'CD3' = -0.3) perc_pos <- GetFeatures(fsom = flowSOM.res, files = c(ff[1001:2000, ], ff[2001:3000, ]), type = c("percentages_positive"), positive_cutoffs = positive_cutoffs, filenames = c("ff_1001-2000", "ff_2001-3000"))
Reads a FlowJo workspace file using the flowWorkspace library and returns a list with a matrix containing gating results and a vector with a label for each cell from a set of specified gates
GetFlowJoLabels( files, wspFile, group = "All Samples", cellTypes = NULL, getData = FALSE, ... )GetFlowJoLabels( files, wspFile, group = "All Samples", cellTypes = NULL, getData = FALSE, ... )
files |
The FCS files of interest |
wspFile |
The FlowJo wsp file to read |
group |
The FlowJo group to parse. Default "All Samples". |
cellTypes |
Cell types to use for final labeling the cells. Should correspond with a subset of the gate names in FlowJo. |
getData |
If true, flowFrames are returned as well. |
... |
Extra arguments to pass to CytoML::flowjo_to_gatingset |
This function returns a list, which for every file contains a list in which the first element ("matrix") is a matrix containing filtering results for each specified gate and the second element ("manual") is a vector which assigns one label to each cell. If only one file is given, only one list is returned instead of a list of lists.
# Identify the files fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") wspFile <- system.file("extdata", "gating.wsp", package = "FlowSOM") # Specify the cell types of interest for assigning one label per cell cellTypes <- c("B cells", "gd T cells", "CD4 T cells", "CD8 T cells", "NK cells", "NK T cells") # Parse the FlowJo workspace gatingResult <- GetFlowJoLabels(fcs_file, wspFile, cellTypes = cellTypes, getData = TRUE) # Check the number of cells assigned to each gate colSums(gatingResult$matrix) # Build a FlowSOM tree flowSOM.res <- FlowSOM(gatingResult$flowFrame, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot pies indicating the percentage of cell types present in the nodes PlotPies(flowSOM.res, gatingResult$manual, backgroundValues = flowSOM.res$metaclustering)# Identify the files fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") wspFile <- system.file("extdata", "gating.wsp", package = "FlowSOM") # Specify the cell types of interest for assigning one label per cell cellTypes <- c("B cells", "gd T cells", "CD4 T cells", "CD8 T cells", "NK cells", "NK T cells") # Parse the FlowJo workspace gatingResult <- GetFlowJoLabels(fcs_file, wspFile, cellTypes = cellTypes, getData = TRUE) # Check the number of cells assigned to each gate colSums(gatingResult$matrix) # Build a FlowSOM tree flowSOM.res <- FlowSOM(gatingResult$flowFrame, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot pies indicating the percentage of cell types present in the nodes PlotPies(flowSOM.res, gatingResult$manual, backgroundValues = flowSOM.res$metaclustering)
Get marker names for an array of channels, given a flowFrame or a FlowSOM
object. As available in "desc". If this is NA, defaults to channel name.
grep is used to look for the markers. Other regex can be added.
GetMarkers(object, channels, exact = TRUE)GetMarkers(object, channels, exact = TRUE)
object |
The flowFrame or the FlowSOM object of interest |
channels |
Vector with markers or channels of interest. Also accepts the index of the channel in the object. |
exact |
If TRUE (default), the grep pattern will be extended to start with ^\\Q and end with \\E$, so only exact matches are possible. |
Corresponding marker names
# 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"))# 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"))
Compute the coefficient of variation for the metaclusters
GetMetaclusterCVs(fsom, colsUsed = FALSE, prettyColnames = FALSE)GetMetaclusterCVs(fsom, colsUsed = FALSE, prettyColnames = FALSE)
fsom |
Result of calling the FlowSOM function |
colsUsed |
Logical. Should report only the columns used to build the SOM. Default = FALSE. |
prettyColnames |
Logical. Should report pretty column names instead of standard column names. Default = FALSE. |
Metacluster CVs
fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) cvs <- GetMetaclusterCVs(flowSOM.res)fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) cvs <- GetMetaclusterCVs(flowSOM.res)
Compute the median fluorescence intensities for the metaclusters
GetMetaclusterMFIs(fsom, colsUsed = FALSE, prettyColnames = FALSE)GetMetaclusterMFIs(fsom, colsUsed = FALSE, prettyColnames = FALSE)
fsom |
Result of calling the FlowSOM function |
colsUsed |
Logical. Should report only the columns used to build the SOM. Default = FALSE. |
prettyColnames |
Logical. Should report pretty column names instead of standard column names. Default = FALSE. |
Metacluster MFIs
fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) mfis <- GetMetaclusterMFIs(flowSOM.res)fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) mfis <- GetMetaclusterMFIs(flowSOM.res)
Get percentage-positive values for all metaclusters
GetMetaclusterPercentagesPositive( fsom, cutoffs, colsUsed = FALSE, prettyColnames = FALSE )GetMetaclusterPercentagesPositive( fsom, cutoffs, colsUsed = FALSE, prettyColnames = FALSE )
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
cutoffs |
named numeric vector. Upper bounds of negative population fluorescence-intensity values for each marker / channel. |
colsUsed |
logical. Should report only the columns used to build the SOM. Default = FALSE. |
prettyColnames |
logical. Should report pretty column names instead of standard column names. Default = FALSE. |
Matrix with percentages of cells that are positive in selected markers per each metacluster
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) perc_pos <- GetMetaclusterPercentagesPositive(flowSOM.res, cutoffs = c('CD4' = 5000))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) perc_pos <- GetMetaclusterPercentagesPositive(flowSOM.res, cutoffs = c('CD4' = 5000))
Get metacluster label for all individual cells
GetMetaclusters(fsom, meta = NULL)GetMetaclusters(fsom, meta = NULL)
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
meta |
Metacluster label for each FlowSOM cluster. If this is NULL, the fsom argument should be as generated by the FlowSOM function, and fsom$metaclustering will be used. |
vector label for every cell
fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) metacluster_labels <- GetMetaclusters(flowSOM.res) metacluster_labels <- GetMetaclusters(flowSOM.res, meta = flowSOM.res$metaclustering)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) metacluster_labels <- GetMetaclusters(flowSOM.res) metacluster_labels <- GetMetaclusters(flowSOM.res, meta = flowSOM.res$metaclustering)
Get MFI values for all clusters
GetMFIs(fsom, colsUsed = FALSE, prettyColnames = FALSE)GetMFIs(fsom, colsUsed = FALSE, prettyColnames = FALSE)
fsom |
FlowSOM object as generated by the FlowSOM function or the BuildSOM function |
colsUsed |
logical. Should report only the columns used to build the SOM. Default = FALSE. |
prettyColnames |
logical. Should report pretty column names instead of standard column names. Default = FALSE. |
Matrix with median 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) mfis <- GetClusterMFIs(flowSOM.res)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)
Get percentages of number of cells in clusters or metaclusters
GetPercentages(fsom, level = "metaclusters")GetPercentages(fsom, level = "metaclusters")
fsom |
FlowSOM object |
level |
Character string, should be either "clusters" or "metaclusters" (default) or abbreviations. |
A named vector with the percentages
# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::estimateLogicle(ff, flowCore::colnames(ff)[8:18])) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) GetPercentages(flowSOM.res) GetPercentages(flowSOM.res, level = "clusters")# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::estimateLogicle(ff, flowCore::colnames(ff)[8:18])) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) GetPercentages(flowSOM.res) GetPercentages(flowSOM.res, level = "clusters")
Helper function to get the ggplot colors
gg_color_hue(n)gg_color_hue(n)
n |
Number of colors |
array with hexadecimal color values
Calculate statistics between 2 groups based on the GetFeatures
output
GroupStats(features, groups)GroupStats(features, groups)
features |
Feature matrix as generated by |
groups |
Named list with file or patient IDs per group (should match
with the rownames of the |
Matrix with the medians per group, the p-values (the raw, Benjamini Hochberg corrected one and the -log10) that resulted from a Wilcox test and the fold and log10 fold changes between the medians of the 2 groups
# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Create new data # To illustrate the output, we here generate new FCS files (with more # cells in metaclusters 1 and 9). # In practice you would not generate any new file but use your different # files from your different groups flowCore::write.FCS(ff[sample(1:nrow(ff), 1000), ], file = "ff_tmp1.fcs") flowCore::write.FCS(ff[sample(1:nrow(ff), 1000), ], file = "ff_tmp2.fcs") flowCore::write.FCS(ff[sample(1:nrow(ff), 1000), ], file = "ff_tmp3.fcs") ff_tmp <- ff[c(1:1000, which(flowSOM.res$map$mapping[, 1] %in% which(flowSOM.res$metaclustering == 9)), which(flowSOM.res$map$mapping[, 1] %in% which(flowSOM.res$metaclustering == 1))), ] flowCore::write.FCS(ff_tmp[sample(1:nrow(ff_tmp), 1000), ], file = "ff_tmp4.fcs") flowCore::write.FCS(ff_tmp[sample(1:nrow(ff_tmp), 1000), ], file = "ff_tmp5.fcs") # Get the count matrix percentages <- GetFeatures(fsom = flowSOM.res, files = c("ff_tmp1.fcs", "ff_tmp2.fcs", "ff_tmp3.fcs", "ff_tmp4.fcs", "ff_tmp5.fcs"), type = "percentages") # Perform the statistics groups <- list("Group 1" = c("ff_tmp1.fcs", "ff_tmp2.fcs", "ff_tmp3.fcs"), "Group 2" = c("ff_tmp4.fcs", "ff_tmp5.fcs")) MC_stats <- GroupStats(percentages[["metacluster_percentages"]], groups) C_stats <- GroupStats(percentages[["cluster_percentages"]], groups) # Process the fold changes vector fold_changes <- C_stats["fold changes", ] fold_changes <- factor(ifelse(fold_changes < -3, "Underrepresented compared to Group 1", ifelse(fold_changes > 3, "Overrepresented compared to Group 1", "--")), levels = c("--", "Underrepresented compared to Group 1", "Overrepresented compared to Group 1")) fold_changes[is.na(fold_changes)] <- "--" # Show in figure ## Fold change gr_1 <- PlotStars(flowSOM.res, title = "Group 1", nodeSizes = C_stats["medians Group 1", ], list_insteadof_ggarrange = TRUE) gr_2 <- PlotStars(flowSOM.res, title = "Group 2", nodeSizes = C_stats["medians Group 2", ], backgroundValues = fold_changes, backgroundColors = c("white", "red", "blue"), list_insteadof_ggarrange = TRUE) p <- ggpubr::ggarrange(plotlist = c(list(gr_1$tree), gr_2), heights = c(3, 1)) ggplot2::ggsave("Groups_foldchanges.pdf", p, width = 10) ## p values p <- PlotVariable(flowSOM.res, title = "Wilcox test group 1 vs. group 2", variable = C_stats["p values", ]) ggplot2::ggsave("Groups_pvalues.pdf", p) ## volcano plot p <- ggplot2::ggplot(data.frame("-log10 p values" = c(C_stats[4, ], MC_stats[4, ]), "log10 fold changes" = c(C_stats[7, ], MC_stats[7, ]), check.names = FALSE), ggplot2::aes(x = `log10 fold changes`, y = `-log10 p values`)) + ggplot2::xlim(-3, 3) + ggplot2::ylim(0, 3) + ggplot2::geom_point()# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Create new data # To illustrate the output, we here generate new FCS files (with more # cells in metaclusters 1 and 9). # In practice you would not generate any new file but use your different # files from your different groups flowCore::write.FCS(ff[sample(1:nrow(ff), 1000), ], file = "ff_tmp1.fcs") flowCore::write.FCS(ff[sample(1:nrow(ff), 1000), ], file = "ff_tmp2.fcs") flowCore::write.FCS(ff[sample(1:nrow(ff), 1000), ], file = "ff_tmp3.fcs") ff_tmp <- ff[c(1:1000, which(flowSOM.res$map$mapping[, 1] %in% which(flowSOM.res$metaclustering == 9)), which(flowSOM.res$map$mapping[, 1] %in% which(flowSOM.res$metaclustering == 1))), ] flowCore::write.FCS(ff_tmp[sample(1:nrow(ff_tmp), 1000), ], file = "ff_tmp4.fcs") flowCore::write.FCS(ff_tmp[sample(1:nrow(ff_tmp), 1000), ], file = "ff_tmp5.fcs") # Get the count matrix percentages <- GetFeatures(fsom = flowSOM.res, files = c("ff_tmp1.fcs", "ff_tmp2.fcs", "ff_tmp3.fcs", "ff_tmp4.fcs", "ff_tmp5.fcs"), type = "percentages") # Perform the statistics groups <- list("Group 1" = c("ff_tmp1.fcs", "ff_tmp2.fcs", "ff_tmp3.fcs"), "Group 2" = c("ff_tmp4.fcs", "ff_tmp5.fcs")) MC_stats <- GroupStats(percentages[["metacluster_percentages"]], groups) C_stats <- GroupStats(percentages[["cluster_percentages"]], groups) # Process the fold changes vector fold_changes <- C_stats["fold changes", ] fold_changes <- factor(ifelse(fold_changes < -3, "Underrepresented compared to Group 1", ifelse(fold_changes > 3, "Overrepresented compared to Group 1", "--")), levels = c("--", "Underrepresented compared to Group 1", "Overrepresented compared to Group 1")) fold_changes[is.na(fold_changes)] <- "--" # Show in figure ## Fold change gr_1 <- PlotStars(flowSOM.res, title = "Group 1", nodeSizes = C_stats["medians Group 1", ], list_insteadof_ggarrange = TRUE) gr_2 <- PlotStars(flowSOM.res, title = "Group 2", nodeSizes = C_stats["medians Group 2", ], backgroundValues = fold_changes, backgroundColors = c("white", "red", "blue"), list_insteadof_ggarrange = TRUE) p <- ggpubr::ggarrange(plotlist = c(list(gr_1$tree), gr_2), heights = c(3, 1)) ggplot2::ggsave("Groups_foldchanges.pdf", p, width = 10) ## p values p <- PlotVariable(flowSOM.res, title = "Wilcox test group 1 vs. group 2", variable = C_stats["p values", ]) ggplot2::ggsave("Groups_pvalues.pdf", p) ## volcano plot p <- ggplot2::ggplot(data.frame("-log10 p values" = c(C_stats[4, ], MC_stats[4, ]), "log10 fold changes" = c(C_stats[7, ], MC_stats[7, ]), check.names = FALSE), ggplot2::aes(x = `log10 fold changes`, y = `-log10 p values`)) + ggplot2::xlim(-3, 3) + ggplot2::ylim(0, 3) + ggplot2::geom_point()
Select k well spread points from X
Initialize_KWSP(X, xdim, ydim)Initialize_KWSP(X, xdim, ydim)
X |
matrix in which each row represents a point |
xdim |
x dimension of the grid |
ydim |
y dimension of the grid |
array containing the selected selected rows
points <- matrix(1:1000, ncol = 10) selection <- Initialize_KWSP(points, 3, 3)points <- matrix(1:1000, ncol = 10) selection <- Initialize_KWSP(points, 3, 3)
Create a grid from first 2 PCA components
Initialize_PCA(data, xdim, ydim)Initialize_PCA(data, xdim, ydim)
data |
matrix in which each row represents a point |
xdim |
x dimension of the grid |
ydim |
y dimension of the grid |
array containing the selected selected rows
points <- matrix(1:1000, ncol = 10) selection <- Initialize_PCA(points, 3, 3)points <- matrix(1:1000, ncol = 10) selection <- Initialize_PCA(points, 3, 3)
Extract the compensated and transformed data and all gate labels.
ManualVector(manualMatrix, cellTypes)ManualVector(manualMatrix, cellTypes)
manualMatrix |
Matrix containing boolean values, indicating for every gate (column) whether the cell (row) is part of it or not. |
cellTypes |
Cell types to use in the summary vector. All others will be ignored and cells which do not fall in one of these gates will get the label "Unknown". Order is important! |
A factor with one label for every cell
Assign nearest node to each datapoint
MapDataToCodes(codes, newdata, distf = 2)MapDataToCodes(codes, newdata, distf = 2)
codes |
matrix with nodes of the SOM |
newdata |
datapoints to assign |
distf |
Distance function (1 = manhattan, 2 = euclidean, 3 = chebyshev, 4 = cosine) |
Array with nearest node id for each datapoint
Compute the coefficient of variation for the metaclusters
MetaclusterCVs(fsom)MetaclusterCVs(fsom)
fsom |
Result of calling the FlowSOM function |
Metacluster CVs
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$SPILL), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff,scale=TRUE,colsToUse=c(9,12,14:18), nClus=10) cvs <- GetMetaclusterCVs(flowSOM.res)fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff,ff@description$SPILL) ff <- flowCore::transform(ff, flowCore::transformList(colnames(ff@description$SPILL), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff,scale=TRUE,colsToUse=c(9,12,14:18), nClus=10) cvs <- GetMetaclusterCVs(flowSOM.res)
Cluster data with automatic number of cluster determination for several algorithms
MetaClustering(data, method, max = 20, seed = NULL, ...)MetaClustering(data, method, max = 20, seed = NULL, ...)
data |
Matrix containing the data to cluster |
method |
Clustering method to use |
max |
Maximum number of clusters to try out |
seed |
Seed to pass on to given clustering method |
... |
Extra parameters to pass along |
Numeric array indicating cluster for each datapoint
# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE,transform = TRUE, scale = TRUE) flowSOM.res <- BuildSOM(flowSOM.res,colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Apply metaclustering metacl <- MetaClustering(flowSOM.res$map$codes, "metaClustering_consensus", max = 10) # Get metaclustering per cell flowSOM.clustering <- metacl[flowSOM.res$map$mapping[, 1]]# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE,transform = TRUE, scale = TRUE) flowSOM.res <- BuildSOM(flowSOM.res,colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Apply metaclustering metacl <- MetaClustering(flowSOM.res$map$codes, "metaClustering_consensus", max = 10) # Get metaclustering per cell flowSOM.clustering <- metacl[flowSOM.res$map$mapping[, 1]]
Cluster data using hierarchical consensus clustering with k clusters
metaClustering_consensus(data, k = 7, seed = NULL)metaClustering_consensus(data, k = 7, seed = NULL)
data |
Matrix containing the data to cluster |
k |
Number of clusters |
seed |
Seed to pass to consensusClusterPlus |
Numeric array indicating cluster for each datapoint
# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE,transform = TRUE, scale = TRUE) flowSOM.res <- BuildSOM(flowSOM.res,colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Apply consensus metaclustering metacl <- metaClustering_consensus(flowSOM.res$map$codes, k = 10)# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE,transform = TRUE, scale = TRUE) flowSOM.res <- BuildSOM(flowSOM.res,colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Apply consensus metaclustering metacl <- metaClustering_consensus(flowSOM.res$map$codes, k = 10)
Compute the median fluorescence intensities for the metaclusters
MetaclusterMFIs(fsom)MetaclusterMFIs(fsom)
fsom |
Result of calling the FlowSOM function |
Metacluster MFIs
fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff,ff@description$SPILL) ff <- flowCore::transform(ff, flowCore::transformList(colnames(ff@description$SPILL), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff,scale=TRUE,colsToUse=c(9,12,14:18),maxMeta=10) mfis <- GetMetaclusterMFIs(flowSOM.res)fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff,ff@description$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)
Extracts the number of clusters from a FlowSOM object
NClusters(fsom)NClusters(fsom)
fsom |
FlowSOM object |
The number of clusters
# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) flowSOM.res <- FlowSOM(ff, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), maxMeta = 10) NClusters(flowSOM.res)# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) flowSOM.res <- FlowSOM(ff, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), maxMeta = 10) NClusters(flowSOM.res)
Map new data to a FlowSOM grid
NewData( fsom, input, madAllowed = 4, compensate = NULL, spillover = NULL, transform = NULL, toTransform = NULL, transformFunction = NULL, transformList = NULL, scale = NULL, scaled.center = NULL, scaled.scale = NULL, silent = FALSE )NewData( fsom, input, madAllowed = 4, compensate = NULL, spillover = NULL, transform = NULL, toTransform = NULL, transformFunction = NULL, transformList = NULL, scale = NULL, scaled.center = NULL, scaled.scale = NULL, silent = FALSE )
fsom |
FlowSOM object |
input |
A flowFrame, a flowSet or an array of paths to files or directories |
madAllowed |
A warning is generated if the distance of the new data points to their closest cluster center is too big. This is computed based on the typical distance of the points from the original dataset assigned to that cluster, the threshold being set to median + madAllowed * MAD. Default is 4. |
compensate |
logical, does the data need to be compensated. If NULL, the same value as in the original FlowSOM call will be used. |
spillover |
spillover matrix to compensate with. If NULL, the same value as in the original FlowSOM call will be used. |
transform |
logical, does the data need to be transformed. If NULL, the same value as in the original FlowSOM call will be used. |
toTransform |
column names or indices that need to be transformed. If NULL, the same value as in the original FlowSOM call will be used. |
transformFunction |
If NULL, the same value as in the original FlowSOM call will be used. |
transformList |
If NULL, the same value as in the original FlowSOM call will be used. |
scale |
Logical, does the data needs to be rescaled. If NULL, the same value as in the original FlowSOM call will be used. |
scaled.center |
See |
scaled.scale |
See |
silent |
Logical. If |
New data is mapped to an existing FlowSOM object. The input is similar to the
ReadInput function.
A new FlowSOM object is created, with the same grid, but a new
mapping, node sizes and mean values. The same preprocessing steps
(compensation, transformation and scaling) will happen to this file as was
specified in the original FlowSOM call. The scaling parameters from the
original grid will be used.
A new FlowSOM object
FlowSOMSubset if you want to get a subset of the
current data instead of a new dataset
# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff[1:1000, ], scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Map new data fSOM2 <- NewData(flowSOM.res, ff[1001:2000, ])# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff[1:1000, ], scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Map new data fSOM2 <- NewData(flowSOM.res, ff[1001:2000, ])
Extracts the number of metaclusters from a FlowSOM object
NMetaclusters(fsom)NMetaclusters(fsom)
fsom |
FlowSOM object |
The number of metaclusters
# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) flowSOM.res <- FlowSOM(ff, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), maxMeta = 10) NMetaclusters(flowSOM.res)# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) flowSOM.res <- FlowSOM(ff, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), maxMeta = 10) NMetaclusters(flowSOM.res)
Parses stars
ParseArcs(x, y, arcValues, arcHeights)ParseArcs(x, y, arcValues, arcHeights)
x |
x coordinate of node |
y |
y coordinate of node |
arcValues |
A named vector with the frequency of how the node should be divided |
arcHeights |
The heights of the arcs |
Function that parses the FlowSOM object into a dataframe for the star values for ggplot
A dataframe ready to use with ggplot, consisting of the coordinates of centers, the radius and angles of the star values
PlotFlowSOM, ParseEdges,
ParseNodeSize,
ParseQuery, ParseSD
Parses edges
ParseEdges(fsom)ParseEdges(fsom)
fsom |
FlowSOM object, as generated by |
Function that parses the graph edges of the FlowSOM object into a dataframe
A dataframe consisting of start and end coordinates of edges
PlotFlowSOM, ParseNodeSize,
ParseArcs, ParseQuery,
ParseSD, AddMST
ParseLayout
ParseLayout(fsom, layout)ParseLayout(fsom, layout)
fsom |
FlowSOM object |
layout |
"MST", "grid" or a matrix/dataframe with 2 columns and 1 row per cluster |
dataframe with 2 columns and 1 row per cluster
Parses node size
ParseNodeSize(nodeSizes, maxNodeSize, refNodeSize)ParseNodeSize(nodeSizes, maxNodeSize, refNodeSize)
nodeSizes |
A vector with node sizes |
maxNodeSize |
Determines the maximum node size. |
refNodeSize |
Reference for node size against which the nodeSizes will be scaled. Default = max(nodeSizes) |
Function that parses the mapping of the FlowSOM object into node sizes relative to the abundances of cells per cluster
Scales node size relative to the abundances of cells per cluster
A vector is returned consisting of node sizes
PlotFlowSOM, ParseEdges,
AutoMaxNodeSize, ParseArcs,
ParseQuery, ParseSD
Parses query
ParseQuery(fsom, query)ParseQuery(fsom, query)
fsom |
FlowSOM object, as generated by |
query |
Array containing "high" or "low" for the specified column names of the FlowSOM data |
Identify nodes in the tree which resemble a certain profile of "high" or "low" marker expressions.
A list, containing the ids of the selected nodes, the individual scores for all nodes and the scores for each marker for each node
PlotFlowSOM, ParseEdges,
ParseNodeSize, ParseArcs,
QueryStarPlot, ParseSD
Calculates the standard deviation of a FlowSOM object
ParseSD(fsom, marker = NULL)ParseSD(fsom, marker = NULL)
fsom |
FlowSOM object, as generated by |
marker |
If a marker is given, the standard deviation for this marker is shown. Otherwise, the maximum ratio is used. |
A vector containing the SDs
PlotFlowSOM, ParseEdges,
ParseNodeSize, ParseArcs,
ParseQuery, PlotSD
Function to draw 2D scatter plots of FlowSOM (meta)clusters
Plot2DScatters( fsom, channelpairs, clusters = NULL, metaclusters = NULL, maxBgPoints = 3000, sizeBgPoints = 0.5, maxPoints = 1000, sizePoints = 0.5, xLim = NULL, yLim = NULL, xyLabels = c("marker"), density = TRUE, centers = TRUE, colors = NULL, plotFile = "2DScatterPlots.png" )Plot2DScatters( fsom, channelpairs, clusters = NULL, metaclusters = NULL, maxBgPoints = 3000, sizeBgPoints = 0.5, maxPoints = 1000, sizePoints = 0.5, xLim = NULL, yLim = NULL, xyLabels = c("marker"), density = TRUE, centers = TRUE, colors = NULL, plotFile = "2DScatterPlots.png" )
fsom |
FlowSOM object, as created by |
channelpairs |
List in which each element is a pair of channel or marker names |
clusters |
Vector or list (to combine multiple clusters in one plot) with indices of clusters of interest |
metaclusters |
Vector or list (to combine multiple metaclusters in one plot) with indices of metaclusters of interest |
maxBgPoints |
Maximum number of background cells to plot |
sizeBgPoints |
Size of the background cells |
maxPoints |
Maximum number of (meta)cluster cells to plot |
sizePoints |
Size of the (meta)cluster cells |
xLim |
Optional vector of a lower and upper limit of the x-axis |
yLim |
Optional vector of a lower and upper limit of the y-axis |
xyLabels |
Determines the label of the x- and y-axis. Can be "marker" and\or "channel" or abbrevations. Default = "marker". |
density |
Default is |
centers |
Default is |
colors |
Colors for all the cells in the selected nodes
(ordered list). First the clusters are colored,
then the metaclusters. If |
plotFile |
If a filepath for a png is given (default =
2DScatterPlots.png), the plots will be plotted in
the corresponding png file. If |
Plot multiple 2D scatter plots in a png file. A subset of fsom$data is plotted in gray, and those of the selected clusters and metaclusters are plotted in color.
If plot is TRUE, nothing is returned and a plot is
drawn in which background cells are plotted in gray and the cells of
the selected nodes in color. If plot is FALSE, a ggplot
objects list is returned.
# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Make the 2D scatter plots of the clusters and metaclusters of interest Plot2DScatters(fsom = flowSOM.res, channelpairs = list(c("PE-Cy7-A", "PE-Cy5-A"), c("PE-Texas Red-A", "Pacific Blue-A")), clusters = c(1, 48, 49, 82, 95), metaclusters = list(c(1, 4), 9), density = FALSE) Plot2DScatters(fsom = flowSOM.res, channelpairs = list(c("PE-Texas Red-A", "Pacific Blue-A")), metaclusters = list(c(1, 4)), density = FALSE, colors = list(c("red", "green")))# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Make the 2D scatter plots of the clusters and metaclusters of interest Plot2DScatters(fsom = flowSOM.res, channelpairs = list(c("PE-Cy7-A", "PE-Cy5-A"), c("PE-Texas Red-A", "Pacific Blue-A")), clusters = c(1, 48, 49, 82, 95), metaclusters = list(c(1, 4), 9), density = FALSE) Plot2DScatters(fsom = flowSOM.res, channelpairs = list(c("PE-Texas Red-A", "Pacific Blue-A")), metaclusters = list(c(1, 4)), density = FALSE, colors = list(c("red", "green")))
Plot cluster centers on a 2D plot
PlotCenters(fsom, marker1, marker2, MST = TRUE)PlotCenters(fsom, marker1, marker2, MST = TRUE)
fsom |
FlowSOM object, as generated by |
marker1 |
Marker to show on the x-axis |
marker2 |
Marker to show on the y-axis |
MST |
Type of visualization, if 1 plot tree, else plot grid |
Plot FlowSOM nodes on a 2D scatter plot of the data
Nothing is returned. A 2D scatter plot is drawn on which the nodes of the grid are indicated
PlotStars,PlotPies,
PlotMarker,BuildMST
# 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)# 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)
Plot nodes on scatter plot
PlotClusters2D( 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])), ... )PlotClusters2D( 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])), ... )
fsom |
FlowSOM object, as generated by |
marker1 |
Marker to plot on the x-axis |
marker2 |
Marker to plot on the y-axis |
nodes |
Nodes of which the cells should be plotted in red |
col |
Colors for all the cells in the selected nodes (ordered array) |
maxBgPoints |
Maximum number of background points to plot |
pchBackground |
Character to use for background cells |
pchCluster |
Character to use for cells in cluster |
main |
Title of the plot |
xlab |
Label for the x axis |
ylab |
Label for the y axis |
xlim |
Limits for the x axis |
ylim |
Limits for the y axis |
... |
Other parameters to pass on to plot |
Plot a 2D scatter plot. All cells of fsom$data are plotted in black, and those of the selected nodes are plotted in red. The nodes in the grid are indexed starting from the left bottom, first going right, then up. E.g. In a 10x10 grid, the node at top left will have index 91.
Nothing is returned. A plot is drawn in which all cells are plotted in black and the cells of the selected nodes in red.
PlotNumbers, PlotCenters,
BuildMST
## Deprecated - use Plot2DScatters instead ## # Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE, scale = TRUE) flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Plot cells ## Not run: Plot2DScatters(flowSOM.res, c(1, 2), clusters = 91) ## End(Not run)## Deprecated - use Plot2DScatters instead ## # Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE, scale = TRUE) flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Plot cells ## Not run: Plot2DScatters(flowSOM.res, c(1, 2), clusters = 91) ## End(Not run)
Plot a dimensionality reduction
PlotDimRed( fsom, colsToUse = fsom$map$colsUsed, colorBy = "metaclusters", colors = NULL, lim = NULL, cTotal = NULL, dimred = Rtsne::Rtsne, extractLayout = function(dimred) { dimred$Y }, label = TRUE, returnLayout = FALSE, seed = NULL, title = NULL, ... )PlotDimRed( fsom, colsToUse = fsom$map$colsUsed, colorBy = "metaclusters", colors = NULL, lim = NULL, cTotal = NULL, dimred = Rtsne::Rtsne, extractLayout = function(dimred) { dimred$Y }, label = TRUE, returnLayout = FALSE, seed = NULL, title = NULL, ... )
fsom |
FlowSOM object, as generated by |
colsToUse |
The columns used for the dimensionality reduction. Default = fsom$map$colsUsed. |
colorBy |
Defines how the dimensionality reduction will be colored. Can be "metaclusters" (default), "clusters" (or abbreviations) or a marker/channel/index. |
colors |
A vector of custom colors. Default returns ggplot colors for categorical variables and the FlowSOM colors for continuous variables. When using a categorical variable, the vector must be as long as the levels of the categorical variable. |
lim |
Limits for the colorscale |
cTotal |
The total amount of cells to be used in the dimensionality reduction. Default is all the cells. |
dimred |
A dimensionality reduction function. Default = Rtsne::Rtsne. Alternatively, a data.frame or matrix with either equal number of rows to the fsom or an OriginalID column. Recommended to put cTotal to NULL when providing a matrix (or ensuring that the dimred corresponds to subsampling the flowSOM data for cTotal cells with the same seed). |
extractLayout |
A function to extract the coordinates from the results of the dimred default = function(dimred)dimred$Y. |
label |
If label = TRUE (default), labels are added to plot. |
returnLayout |
If TRUE, this function returns a dataframe with the layout of dimred and the original IDs and the plot. Default = FALSE. |
seed |
A seed for reproducibility. |
title |
A title for the plot. |
... |
Additional arguments to pass to dimred. |
Plot a dimensionality reduction of fsom$data
A dimensionality reduction plot made in ggplot2
file <- system.file("extdata", "68983.fcs", package="FlowSOM") flowSOM.res <- FlowSOM(file, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, silent = FALSE, xdim = 7, ydim = 7) PlotDimRed(flowSOM.res, cTotal = 5000, seed = 1, title = "t-SNE") PlotDimRed(flowSOM.res, cTotal = 5000, colorBy = "CD3", seed = 1, title = "t-SNE")file <- system.file("extdata", "68983.fcs", package="FlowSOM") flowSOM.res <- FlowSOM(file, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, silent = FALSE, xdim = 7, ydim = 7) PlotDimRed(flowSOM.res, cTotal = 5000, seed = 1, title = "t-SNE") PlotDimRed(flowSOM.res, cTotal = 5000, colorBy = "CD3", seed = 1, title = "t-SNE")
Make a scatter plot per channel for all provided files
PlotFileScatters( input, fileID = "File", channels = NULL, yLim = NULL, yLabel = "marker", quantiles = NULL, names = NULL, groups = NULL, color = NULL, legend = FALSE, maxPoints = 50000, ncol = NULL, nrow = NULL, width = NULL, height = NULL, silent = FALSE, plotFile = "FileScatters.png" )PlotFileScatters( input, fileID = "File", channels = NULL, yLim = NULL, yLabel = "marker", quantiles = NULL, names = NULL, groups = NULL, color = NULL, legend = FALSE, maxPoints = 50000, ncol = NULL, nrow = NULL, width = NULL, height = NULL, silent = FALSE, plotFile = "FileScatters.png" )
input |
Either a flowSet, a flowFrame with a file ID column (e.g.
output from the |
fileID |
Name of the file ID column when the input is a flowFrame,
default to "File" (File ID column in the
|
channels |
Vector of channels or markers that need to be plotted, if NULL (default), all channels from the input will be plotted |
yLim |
Optional vector of a lower and upper limit of the y-axis |
yLabel |
Determines the label of the y-axis. Can be "marker" and\or "channel" or abbrevations. Default = "marker". |
quantiles |
If provided (default NULL), a numeric vector with values between 0 and 1. These quantiles are indicated on the plot |
names |
Optional parameter to provide filenames. If |
groups |
Optional parameter to specify groups of files, should have
the same length as the |
color |
Optional parameter to provide colors. Should have the same
lengths as the number of groups (or 1 if |
legend |
Logical parameter to specify whether the group levels
should be displayed. Default is |
maxPoints |
Total number of data points that will be plotted per channel, default is 50000 |
ncol |
Number of columns in the final plot, optional |
nrow |
Number of rows in the final plot, optional |
width |
Width of png file. By default NULL the width parameter is estimated based on the input. |
height |
Height of png file. By default NULL the width parameter is estimated based on the input. |
silent |
If FALSE, prints an update every time it starts processing a new file. Default = FALSE. |
plotFile |
Path to png file, default is "FileScatters.png". If
|
List of ggplot objects if plot is FALSE,
otherwise filePlot with plot is created.
# Preprocessing fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowCore::write.FCS(ff[1:1000, ], file = "ff_tmp1.fcs") flowCore::write.FCS(ff[1001:2000, ], file = "ff_tmp2.fcs") flowCore::write.FCS(ff[2001:3000, ], file = "ff_tmp3.fcs") # Make plot PlotFileScatters(input = c("ff_tmp1.fcs", "ff_tmp2.fcs", "ff_tmp3.fcs"), channels = c("Pacific Blue-A", "Alexa Fluor 700-A", "PE-Cy7-A"), maxPoints = 1000)# Preprocessing fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowCore::write.FCS(ff[1:1000, ], file = "ff_tmp1.fcs") flowCore::write.FCS(ff[1001:2000, ], file = "ff_tmp2.fcs") flowCore::write.FCS(ff[2001:3000, ], file = "ff_tmp3.fcs") # Make plot PlotFileScatters(input = c("ff_tmp1.fcs", "ff_tmp2.fcs", "ff_tmp3.fcs"), channels = c("Pacific Blue-A", "Alexa Fluor 700-A", "PE-Cy7-A"), maxPoints = 1000)
Base layer to plot a FlowSOM result
PlotFlowSOM( fsom, view = "MST", nodeSizes = fsom$map$pctgs, maxNodeSize = 1, refNodeSize = max(nodeSizes), equalNodeSize = FALSE, backgroundValues = NULL, backgroundColors = NULL, backgroundLim = NULL, title = NULL )PlotFlowSOM( fsom, view = "MST", nodeSizes = fsom$map$pctgs, maxNodeSize = 1, refNodeSize = max(nodeSizes), equalNodeSize = FALSE, backgroundValues = NULL, backgroundColors = NULL, backgroundLim = NULL, title = NULL )
fsom |
FlowSOM object, as created by |
view |
Preferred view, options: "MST", "grid" or "matrix" with a matrix/dataframe consisting of coordinates. Default = "MST" |
nodeSizes |
A vector containing node sizes. These will automatically be scaled between 0 and maxNodeSize and transformed with a sqrt. Default = fsom$MST$sizes |
maxNodeSize |
Determines the maximum node size. Default is 1. |
refNodeSize |
Reference for node size against which the nodeSizes will be scaled. Default = max(nodeSizes) |
equalNodeSize |
If |
backgroundValues |
Values to be used for background coloring, either numerical values or something that can be made into a factor (e.g. a clustering) |
backgroundColors |
Color palette to be used for the background coloring. Can be either a function or an array specifying colors. |
backgroundLim |
Only used when backgroundValues are numerical. Defaults to min and max of the backgroundValues. |
title |
Title of the plot |
Base layer of the FlowSOM plot, where you can choose layout (MST, grid or
coordinates of your own choosing), background colors and node size. Can
then be extended by e.g. AddStars, AddLabels,
AddPies, ...
A ggplot object with the base layer of a FlowSOM plot
PlotStars, PlotVariable,
PlotMarker, PlotLabels,
PlotNumbers, PlotPies,
QueryStarPlot, PlotSD
# Locate file on file system fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") # Build FlowSOM model flowSOM.res <- FlowSOM(fcs_file, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot with background coloring PlotFlowSOM(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) %>% AddLabels(seq(100))# Locate file on file system fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") # Build FlowSOM model flowSOM.res <- FlowSOM(fcs_file, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot with background coloring PlotFlowSOM(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) %>% AddLabels(seq(100))
Plot differences between groups
PlotGroups(fsom, groups, threshold = NULL, pThreshold = 0.05, ...)PlotGroups(fsom, groups, threshold = NULL, pThreshold = 0.05, ...)
fsom |
FlowSOM object, as generated by |
groups |
Groups result as generated by |
threshold |
Relative difference in groups before the node is colored |
pThreshold |
Threshold on p-value from wilcox-test before the node is colored. If this is not NULL, threshold will be ignored. |
... |
Additional arguments to pass to |
Plot FlowSOM trees, where each node is represented by a star chart indicating mean marker values, the size of the node is relative to the mean percentage of cells present in each
A vector containing the labels assigned to the nodes for all groups except the first
PlotStars,PlotVariable,
PlotFlowSOM,PlotLabels,PlotNumbers,
PlotMarker,PlotPies,QueryStarPlot,
PlotSD
#Run FlowSOM fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") fsom <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9,12,14:18), nClus = 10) ff <- flowCore::read.FCS(fileName) # Make an additional file without cluster 7 and double amount of cluster 5 selection <- c(which(GetClusters(fsom) %in% which(fsom$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$tree), gr_2), heights = c(3, 1)) p#Run FlowSOM fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") fsom <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9,12,14:18), nClus = 10) ff <- flowCore::read.FCS(fileName) # Make an additional file without cluster 7 and double amount of cluster 5 selection <- c(which(GetClusters(fsom) %in% which(fsom$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$tree), gr_2), heights = c(3, 1)) p
Plot labels for each cluster
PlotLabels( fsom, labels, maxNodeSize = 0, textSize = 3.88, textColor = "black", ... )PlotLabels( fsom, labels, maxNodeSize = 0, textSize = 3.88, textColor = "black", ... )
fsom |
FlowSOM object, as generated by |
labels |
A vector of labels for every node. |
maxNodeSize |
Determines the maximum node size. Default is 0. |
textSize |
Size for geom_text. Default (=3.88) is from geom_text. |
textColor |
Color for geom_text. Default = black. |
... |
Additional arguments to pass to |
Plot FlowSOM grid or tree, with in each node a label. Especially useful to show metacluster numbers
Nothing is returned. A plot is drawn in which each node is represented by a label.
PlotStars, PlotVariable,
PlotFlowSOM, PlotMarker,
PlotNumbers, PlotPies,
QueryStarPlot, PlotSD
# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot the node IDs PlotLabels( flowSOM.res, flowSOM.res$metaclustering)# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package="FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot the node IDs PlotLabels( flowSOM.res, flowSOM.res$metaclustering)
Function to plot the manual labels per FlowSOM (meta)cluster in a barplot
PlotManualBars( fsom, fcs = NULL, manualVector, manualOrder = NULL, colors = NULL, list_insteadof_plots = FALSE )PlotManualBars( fsom, fcs = NULL, manualVector, manualOrder = NULL, colors = NULL, list_insteadof_plots = FALSE )
fsom |
FlowSOM object, as generated by |
fcs |
FCS file that should be mapped on the FlowSOM object. Default is NULL. |
manualVector |
Vector with cell labels, e.g. obtained by manual gating |
manualOrder |
Optional vector with unique cell labels to fix in which order the cell labels should be shown |
colors |
Optional color vector, should have the same length as the number of unique cell labels |
list_insteadof_plots |
If |
Either a plot or a ggplot objects list is returned.
# Identify the files fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") gating_file <- system.file("extdata", "gatingResult.csv", package = "FlowSOM") # Specify the cell types of interest for assigning one label per cell cellTypes <- c("B cells", "gd T cells", "CD4 T cells", "CD8 T cells", "NK cells", "NK T cells") # Load manual labels (e.g. GetFlowJoLabels can be used to extract labels from # an fcs file) gatingResult <- as.factor(read.csv(gating_file, header = FALSE)[, 1]) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs_file, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Make the barplot of the manual labels pdf("PlotManualBars.pdf") PlotManualBars(fsom = flowSOM.res, fcs = fcs_file, manualVector = gatingResult, manualOrder = c(cellTypes, "Unlabeled"), colors = c("#F8766D", "#B79F00", "#00BA38", "#00BFC4", "#619CFF", "#F564E3", "#D3D3D3")) dev.off()# Identify the files fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") gating_file <- system.file("extdata", "gatingResult.csv", package = "FlowSOM") # Specify the cell types of interest for assigning one label per cell cellTypes <- c("B cells", "gd T cells", "CD4 T cells", "CD8 T cells", "NK cells", "NK T cells") # Load manual labels (e.g. GetFlowJoLabels can be used to extract labels from # an fcs file) gatingResult <- as.factor(read.csv(gating_file, header = FALSE)[, 1]) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs_file, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Make the barplot of the manual labels pdf("PlotManualBars.pdf") PlotManualBars(fsom = flowSOM.res, fcs = fcs_file, manualVector = gatingResult, manualOrder = c(cellTypes, "Unlabeled"), colors = c("#F8766D", "#B79F00", "#00BA38", "#00BFC4", "#619CFF", "#F564E3", "#D3D3D3")) dev.off()
Plot comparison with other clustering
PlotMarker( fsom, marker, refMarkers = fsom$map$colsUsed, title = GetMarkers(fsom, marker), colorPalette = FlowSOM_colors, lim = NULL, ... )PlotMarker( fsom, marker, refMarkers = fsom$map$colsUsed, title = GetMarkers(fsom, marker), colorPalette = FlowSOM_colors, lim = NULL, ... )
fsom |
FlowSOM object |
marker |
A vector of markers/channels to plot. |
refMarkers |
Is used to determine relative scale of the marker that will be plotted. Default are all markers used in the clustering. |
title |
A vector with custom titles for the plot. Default is the marker name. |
colorPalette |
Color palette to use. Can be a function or a vector. |
lim |
Limits for the scale |
... |
Additional arguments to pass to |
Plot FlowSOM grid or tree, colored by node values for a specific marker
A ggplot figure is returned in which every cluster is colored according to the MFI value for the specified marker
# Build FlowSOM model fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = FALSE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot one marker PlotMarker(flowSOM.res, "CD19") PlotMarker(flowSOM.res, "CD19", colorPalette = c("gray", "red")) # Plot all markers PlotMarker(flowSOM.res, c(9, 12, 14:18)) # Use specific limits if the ones from the columns used for clustering # are not relevant for your marker of choice PlotMarker(flowSOM.res, "FSC-A", lim = c(55000, 130000)) # Example with additional FlowSOM plotting options PlotMarker(flowSOM.res, "CD19", view = "grid", equalNodeSize = TRUE, backgroundValues = 1:100 == 27, backgroundColors = c("white", "red"))# Build FlowSOM model fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = FALSE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot one marker PlotMarker(flowSOM.res, "CD19") PlotMarker(flowSOM.res, "CD19", colorPalette = c("gray", "red")) # Plot all markers PlotMarker(flowSOM.res, c(9, 12, 14:18)) # Use specific limits if the ones from the columns used for clustering # are not relevant for your marker of choice PlotMarker(flowSOM.res, "FSC-A", lim = c(55000, 130000)) # Example with additional FlowSOM plotting options PlotMarker(flowSOM.res, "CD19", view = "grid", equalNodeSize = TRUE, backgroundValues = 1:100 == 27, backgroundColors = c("white", "red"))
Plot a star chart indicating median marker values of a single node
PlotNode( fsom, id, markers = fsom$map$colsUsed, colorPalette = grDevices::colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")), main = paste0("Cluster ", id) )PlotNode( fsom, id, markers = fsom$map$colsUsed, colorPalette = grDevices::colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")), main = paste0("Cluster ", id) )
fsom |
FlowSOM object, as generated by |
id |
Id of the node to plot (check PlotNumbers to get the ids) |
markers |
Array of markers to use. Default: the markers used to build the tree |
colorPalette |
Color palette to be used for the markers |
main |
Title of the plot |
Nothing is returned. A plot is drawn in which the node is represented by a star chart indicating the median fluorescence intensities.
PlotStars,PlotNumbers,
FlowSOM
# 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)# 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)
Plot cluster ids for each cluster
PlotNumbers(fsom, level = "clusters", maxNodeSize = 0, ...)PlotNumbers(fsom, level = "clusters", maxNodeSize = 0, ...)
fsom |
FlowSOM object |
level |
Character string, should be either "clusters" or "metaclusters". Can be abbreviated. |
maxNodeSize |
Determines the maximum node size. Default is 0.
See |
... |
Additional arguments to pass to |
Plot FlowSOM grid or tree, with in each node the cluster id.
Nothing is returned. A plot is drawn in which each node is labeled by its cluster id.
PlotStars, PlotVariable,
PlotFlowSOM, PlotLabels,
PlotMarker, PlotPies,
QueryStarPlot, PlotSD
# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::estimateLogicle(ff, flowCore::colnames(ff)[8:18])) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot the node IDs PlotNumbers(flowSOM.res) PlotNumbers(flowSOM.res, "metaclusters") PlotNumbers(flowSOM.res, view = "grid") PlotNumbers(flowSOM.res, maxNodeSize = 1, equalNodeSize = TRUE)# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::estimateLogicle(ff, flowCore::colnames(ff)[8:18])) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot the node IDs PlotNumbers(flowSOM.res) PlotNumbers(flowSOM.res, "metaclusters") PlotNumbers(flowSOM.res, view = "grid") PlotNumbers(flowSOM.res, maxNodeSize = 1, equalNodeSize = TRUE)
Visual overview of outliers
PlotOutliers(fsom, outlierReport)PlotOutliers(fsom, outlierReport)
fsom |
FlowSOM object. |
outlierReport |
Outlier overview as generated by TestOutliers() |
Plot
# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) outlierReport <- TestOutliers(flowSOM.res) p <- PlotOutliers(flowSOM.res, outlierReport)# Identify the files fcs <- flowCore::read.FCS(system.file("extdata", "68983.fcs", package = "FlowSOM")) # Build a FlowSOM object flowSOM.res <- FlowSOM(fcs, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) outlierReport <- TestOutliers(flowSOM.res) p <- PlotOutliers(flowSOM.res, outlierReport)
Plot metaclusters on scatter plots
PlotOverview2D(fsom, markerlist, metaclusters, colors = NULL, ff, ...)PlotOverview2D(fsom, markerlist, metaclusters, colors = NULL, ff, ...)
fsom |
FlowSOM object, as generated by |
markerlist |
List in which each element is a pair of marker names |
metaclusters |
Metaclusters of interest |
colors |
Named vector with color value for each metacluster. If NULL (default) colorbrewer "paired" is interpolated |
ff |
flowFrame to use as reference for the marker names |
... |
Other parameters to pass on to PlotClusters2D |
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.
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.
## 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 ## Not run: 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() ## End(Not run)## 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 ## Not run: 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() ## End(Not run)
Plot comparison with other clustering
PlotPies( fsom, cellTypes, colorPalette = grDevices::colorRampPalette(c("white", "#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")), ... )PlotPies( fsom, cellTypes, colorPalette = grDevices::colorRampPalette(c("white", "#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")), ... )
fsom |
FlowSOM object, as generated by |
cellTypes |
Array of factors indicating the celltypes |
colorPalette |
Color palette to use. |
... |
Additional arguments to pass to |
Plot FlowSOM grid or tree, with pies indicating another clustering or manual gating result
ggplot plot
PlotStars, PlotVariable,
PlotFlowSOM, PlotLabels,
PlotNumbers, PlotMarker,
QueryStarPlot, PlotSD
# Identify the files fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") gating_file <- system.file("extdata", "gatingResult.csv", package = "FlowSOM") # Specify the cell types of interest for assigning one label per cell cellTypes <- c("B cells", "gd T cells", "CD4 T cells", "CD8 T cells", "NK cells", "NK T cells") # Load manual labels (e.g. GetFlowJoLabels can be used to extract labels from # an fcs file) gatingResult <- as.factor(read.csv(gating_file, header = FALSE)[, 1]) # Build a FlowSOM tree flowSOM.res <- FlowSOM(fcs_file, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot pies indicating the percentage of cell types present in the nodes PlotPies(flowSOM.res, gatingResult, backgroundValues = flowSOM.res$metaclustering)# Identify the files fcs_file <- system.file("extdata", "68983.fcs", package = "FlowSOM") gating_file <- system.file("extdata", "gatingResult.csv", package = "FlowSOM") # Specify the cell types of interest for assigning one label per cell cellTypes <- c("B cells", "gd T cells", "CD4 T cells", "CD8 T cells", "NK cells", "NK T cells") # Load manual labels (e.g. GetFlowJoLabels can be used to extract labels from # an fcs file) gatingResult <- as.factor(read.csv(gating_file, header = FALSE)[, 1]) # Build a FlowSOM tree flowSOM.res <- FlowSOM(fcs_file, scale = TRUE, compensate = TRUE, transform = TRUE, toTransform = 8:18, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot pies indicating the percentage of cell types present in the nodes PlotPies(flowSOM.res, gatingResult, backgroundValues = flowSOM.res$metaclustering)
Plot FlowSOM grid or tree, colored by standard deviation.
PlotSD(fsom, marker = NULL, ...)PlotSD(fsom, marker = NULL, ...)
fsom |
FlowSOM object, as generated by |
marker |
If a marker/channel is given, the sd for this marker is shown. Otherwise, the maximum ratio is used. |
... |
Additional arguments to pass to |
Nothing is returned. A plot is drawn in which each node is colored depending on its standard deviation
PlotStars, PlotVariable,
PlotFlowSOM, PlotLabels,
PlotNumbers, PlotMarker,
PlotPies, QueryStarPlot
# 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) PlotSD(flowSOM.res)# 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) PlotSD(flowSOM.res)
Plots star legend
PlotStarLegend(markers, colors, starHeight = 1)PlotStarLegend(markers, colors, starHeight = 1)
markers |
Vector of markers used in legend |
colors |
Color palette for the legend. Can be a vector or a function. |
starHeight |
Star height. Default = 1. |
Function makes the legend of the FlowSOM star plot
Returns nothing, but plots a legend for FlowSOM star plot
PlotStarLegend(c("CD3", "CD4", "CD8"), FlowSOM_colors(3))PlotStarLegend(c("CD3", "CD4", "CD8"), FlowSOM_colors(3))
Plot star charts
PlotStars( fsom, markers = fsom$map$colsUsed, colorPalette = FlowSOM_colors, list_insteadof_ggarrange = FALSE, ... )PlotStars( fsom, markers = fsom$map$colsUsed, colorPalette = FlowSOM_colors, list_insteadof_ggarrange = FALSE, ... )
fsom |
FlowSOM object, as generated by |
markers |
Markers to plot (will be parsed by GetChannels) |
colorPalette |
Color palette to use |
list_insteadof_ggarrange |
If FALSE (default), the plot and the legend are combined by ggarrange. If TRUE, the separate elements are returned in a list, to allow further customization. |
... |
Additional arguments to pass to |
Plot FlowSOM grid or tree, where each node is represented by a star chart indicating median marker values
Nothing is returned. A plot is drawn in which each node is represented by a star chart indicating the median fluorescence intensities. Resets the layout back to 1 plot at the end.
PlotMarker, PlotVariable,
PlotFlowSOM, PlotLabels,
PlotNumbers, PlotPies,
QueryStarPlot, PlotSD
# 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)) # Plot stars indicating the MFI of the cells present in the nodes PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) newLayout <- igraph::layout_with_fr(flowSOM.res[["MST"]][["graph"]]) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering, view = newLayout) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering, view = "grid")# Read from file, build self-organizing map and minimal spanning tree fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18)) # Plot stars indicating the MFI of the cells present in the nodes PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) newLayout <- igraph::layout_with_fr(flowSOM.res[["MST"]][["graph"]]) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering, view = newLayout) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering, view = "grid")
Plot a variable for all nodes
PlotVariable( fsom, variable, variableName = "", colorPalette = FlowSOM_colors, lim = NULL, ... )PlotVariable( fsom, variable, variableName = "", colorPalette = FlowSOM_colors, lim = NULL, ... )
fsom |
FlowSOM object |
variable |
A vector containing a value for every cluster |
variableName |
Label to show on the legend |
colorPalette |
Color palette to use. Can be a function or a vector. |
lim |
Limits for the scale |
... |
Additional arguments to pass to |
Plot FlowSOM grid or tree, colored by node values given in variable
PlotStars, QueryStarPlot,
PlotFlowSOM, PlotLabels,
PlotNumbers, PlotMarker,
PlotPies, PlotSD
# Build FlowSOM model fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = FALSE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot some random values rand <- runif(flowSOM.res$map$nNodes) PlotVariable(flowSOM.res, variable = rand, variableName = "Random") PlotVariable(flowSOM.res, variable = flowSOM.res$metaclustering, variableName = "Metaclustering") %>% AddLabels(labels = flowSOM.res$metaclustering)# Build FlowSOM model fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- FlowSOM(fileName, compensate = TRUE, transform = TRUE, scale = FALSE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) # Plot some random values rand <- runif(flowSOM.res$map$nNodes) PlotVariable(flowSOM.res, variable = rand, variableName = "Random") PlotVariable(flowSOM.res, variable = flowSOM.res$metaclustering, variableName = "Metaclustering") %>% AddLabels(labels = flowSOM.res$metaclustering)
Print FlowSOM object
## S3 method for class 'FlowSOM' print(x, ...)## S3 method for class 'FlowSOM' print(x, ...)
x |
FlowSOM object to print information about |
... |
Further arguments, not used |
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) print(flowSOM.res)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) print(flowSOM.res)
Calculate mean weighted cluster purity
Purity(realClusters, predictedClusters, weighted = TRUE)Purity(realClusters, predictedClusters, weighted = TRUE)
realClusters |
array with real cluster values |
predictedClusters |
array with predicted cluster values |
weighted |
logical. Should the mean be weighted depending on the number of points in the predicted clusters |
Mean purity score, worst score, number of clusters with score < 0.75
# Generate some random data as an example realClusters <- sample(1:5, 100, replace = TRUE) predictedClusters <- sample(1:6, 100, replace = TRUE) # Calculate the FMeasure Purity(realClusters, predictedClusters)# Generate some random data as an example realClusters <- sample(1:5, 100, replace = TRUE) predictedClusters <- sample(1:6, 100, replace = TRUE) # Calculate the FMeasure Purity(realClusters, predictedClusters)
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).
query_multiple(fsom, cell_types, pdf_name = "query_multiple.pdf", ...)query_multiple(fsom, cell_types, pdf_name = "query_multiple.pdf", ...)
fsom |
FlowSOM object |
cell_types |
Description of the cell types. Named list, with one named vector per cell type containing "high"/"low" values |
pdf_name |
Path to a pdf file to save figures |
... |
Additional arguments to pass to |
A label for every FlowSOM cluster (Unknown or one of the celltype names of the list, if selected by QueryStarPlot)
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")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")
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).
QueryMultiple(fsom, cellTypes, plotFile = "queryMultiple.pdf", ...)QueryMultiple(fsom, cellTypes, plotFile = "queryMultiple.pdf", ...)
fsom |
FlowSOM object |
cellTypes |
Description of the cell types. Named list, with one named vector per cell type containing "high"/"low" values |
plotFile |
Path to a pdf file to save the plots (default is
queryMultiple.pdf). If |
... |
Additional arguments to pass to |
A label for every FlowSOM cluster (Unknown or one of the celltype names of the list, if selected by QueryStarPlot)
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) cellTypes <- 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, cellTypes, "query_multiple.pdf")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) cellTypes <- 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, cellTypes, "query_multiple.pdf")
Query a certain cell type
QueryStarPlot( fsom, query, plot = TRUE, colorPalette = FlowSOM_colors, backgroundColors = "#CA0020", ... )QueryStarPlot( fsom, query, plot = TRUE, colorPalette = FlowSOM_colors, backgroundColors = "#CA0020", ... )
fsom |
FlowSOM object, as generated by |
query |
Array containing "high" or "low" (or abbreviations) for the specified column names of the FlowSOM data. |
plot |
If true, a plot with a gradient of scores for the nodes is shown. |
colorPalette |
Color palette to be used for colors for "stars", "pies" or "marker". Can be either a function or an array specifying colors. |
backgroundColors |
Color to use for nodes with a high score in the plot. Default is red. |
... |
Additional arguments to pass to |
Identify nodes in the tree which resemble a certain profile of "high" or "low" marker expressions.
A list, containing the ids of the selected nodes, the individual scores for all nodes and the scores for each marker for each node
PlotStars, PlotVariable,
PlotFlowSOM, PlotLabels,
PlotNumbers, PlotMarker,
PlotPies, PlotSD
file <- system.file("extdata", "68983.fcs", package="FlowSOM") flowSOM.res <- FlowSOM(file, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, silent = FALSE, xdim = 7, ydim = 7) query <- c("CD3" = "high", #CD3 "CD4" = "low", #TCRb "CD8" = "high") #CD8 query_res <- QueryStarPlot(flowSOM.res, query, equalNodeSize = TRUE) cellTypes <- factor(rep("Unlabeled", 49), levels = c("Unlabeled", "CD8 T cells")) cellTypes[query_res$selected] <- "CD8 T cells" PlotStars(flowSOM.res, backgroundValues = cellTypes, backgroundColors = c("#FFFFFF00", "#ca0020aa"))file <- system.file("extdata", "68983.fcs", package="FlowSOM") flowSOM.res <- FlowSOM(file, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, silent = FALSE, xdim = 7, ydim = 7) query <- c("CD3" = "high", #CD3 "CD4" = "low", #TCRb "CD8" = "high") #CD8 query_res <- QueryStarPlot(flowSOM.res, query, equalNodeSize = TRUE) cellTypes <- factor(rep("Unlabeled", 49), levels = c("Unlabeled", "CD8 T cells")) cellTypes[query_res$selected] <- "CD8 T cells" PlotStars(flowSOM.res, backgroundValues = cellTypes, backgroundColors = c("#FFFFFF00", "#ca0020aa"))
Take some input and return FlowSOM object containing a matrix with the preprocessed data (compensated, transformed, scaled)
ReadInput( input, pattern = ".fcs", compensate = FALSE, spillover = NULL, transform = FALSE, toTransform = NULL, transformFunction = flowCore::logicleTransform(), transformList = NULL, scale = FALSE, scaled.center = TRUE, scaled.scale = TRUE, silent = FALSE )ReadInput( input, pattern = ".fcs", compensate = FALSE, spillover = NULL, transform = FALSE, toTransform = NULL, transformFunction = flowCore::logicleTransform(), transformList = NULL, scale = FALSE, scaled.center = TRUE, scaled.scale = TRUE, silent = FALSE )
input |
a flowFrame, a flowSet, a matrix with column names or an array of paths to files or directories |
pattern |
if input is an array of file- or directorynames, select only files containing pattern |
compensate |
logical, does the data need to be compensated |
spillover |
spillover matrix to compensate with
If |
transform |
logical, does the data need to be transformed |
toTransform |
column names or indices that need to be transformed.
Will be ignored if |
transformFunction |
Defaults to logicleTransform() |
transformList |
transformList to apply on the samples. |
scale |
logical, does the data needs to be rescaled |
scaled.center |
see |
scaled.scale |
see |
silent |
if |
FlowSOM object containing the data, which can be used as input for the BuildSOM function
# Read from file fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE, scale = TRUE) # Or read from flowFrame object ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- ReadInput(ff, scale = TRUE) # Build the self-organizing map and the minimal spanning tree flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Apply metaclustering metacl <- MetaClustering(flowSOM.res$map$codes, "metaClustering_consensus", max = 10) # Get metaclustering per cell flowSOM.clustering <- metacl[flowSOM.res$map$mapping[, 1]]# Read from file fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE, scale = TRUE) # Or read from flowFrame object ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- ReadInput(ff, scale = TRUE) # Build the self-organizing map and the minimal spanning tree flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18)) flowSOM.res <- BuildMST(flowSOM.res) # Apply metaclustering metacl <- MetaClustering(flowSOM.res$map$codes, "metaClustering_consensus", max = 10) # Get metaclustering per cell flowSOM.clustering <- metacl[flowSOM.res$map$mapping[, 1]]
Write FlowSOM clustering results to the original FCS files
SaveClustersToFCS( fsom, originalFiles, preprocessedFiles = NULL, selectionColumn = NULL, silent = FALSE, outputDir = ".", suffix = "_FlowSOM.fcs", ... )SaveClustersToFCS( fsom, originalFiles, preprocessedFiles = NULL, selectionColumn = NULL, silent = FALSE, outputDir = ".", suffix = "_FlowSOM.fcs", ... )
fsom |
FlowSOM object as generated by BuildSOM |
originalFiles |
FCS files that should be extended |
preprocessedFiles |
FCS files that correspond to the input of FlowSOM, If NULL (default), the originalFiles are used. |
selectionColumn |
Column of the FCS file indicating the original cell ids. If NULL (default), no selection is made. |
silent |
If FALSE (default), print some extra output |
outputDir |
Directory to save the FCS files. Default to the current working directory (".") |
suffix |
Suffix added to the filename. Default _FlowSOM.fcs |
... |
Options to pass on to the read.FCS function (e.g. truncate_max_range) |
Saves the extended FCS file as [originalName]_FlowSOM.fcs
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) SaveClustersToFCS(flowSOM.res, fileName)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) SaveClustersToFCS(flowSOM.res, fileName)
Scales starheights
ScaleStarHeights(data, nodeSizes)ScaleStarHeights(data, nodeSizes)
data |
Median values of relevant markers extracted from FlowSOM object |
nodeSizes |
A vector that is returned from |
Function that scales the star values between 0 and the node size
A dataframe consisting of the scaled values of the stars. The stars are scaled between 0 and the maximum of all stars
PlotFlowSOM, ParseNodeSize,
AutoMaxNodeSize
Build a self-organizing map
SOM( data, xdim = 10, ydim = 10, rlen = 10, mst = 1, alpha = c(0.05, 0.01), radius = stats::quantile(nhbrdist, 0.67) * c(1, 0), init = FALSE, initf = Initialize_KWSP, distf = 2, silent = FALSE, map = TRUE, codes = NULL, importance = NULL )SOM( data, xdim = 10, ydim = 10, rlen = 10, mst = 1, alpha = c(0.05, 0.01), radius = stats::quantile(nhbrdist, 0.67) * c(1, 0), init = FALSE, initf = Initialize_KWSP, distf = 2, silent = FALSE, map = TRUE, codes = NULL, importance = NULL )
data |
Matrix containing the training data |
xdim |
Width of the grid |
ydim |
Hight of the grid |
rlen |
Number of times to loop over the training data for each MST |
mst |
Number of times to build an MST |
alpha |
Start and end learning rate |
radius |
Start and end radius |
init |
Initialize cluster centers in a non-random way |
initf |
Use the given initialization function if init == T (default: Initialize_KWSP) |
distf |
Distance function (1 = manhattan, 2 = euclidean, 3 = chebyshev, 4 = cosine) |
silent |
If FALSE, print status updates |
map |
If FALSE, data is not mapped to the SOM. Default TRUE. |
codes |
Cluster centers to start with |
importance |
array with numeric values. Parameters will be scaled according to importance |
A list containing all parameter settings and results
This code is strongly based on the kohonen package.
R. Wehrens and L.M.C. Buydens, Self- and Super-organising Maps
in R: the kohonen package J. Stat. Softw., 21(5), 2007
Test if any cells are too far from their cluster centers
TestOutliers( fsom, madAllowed = 4, fsomReference = NULL, plotFile = NULL, channels = NULL )TestOutliers( fsom, madAllowed = 4, fsomReference = NULL, plotFile = NULL, channels = NULL )
fsom |
FlowSOM object |
madAllowed |
Number of median absolute deviations allowed. Default = 4. |
fsomReference |
FlowSOM object to use as reference. If NULL (default), the original fsom object is used. |
plotFile |
If |
channels |
If channels are given, the number of outliers in the original space for those channels will be calculated and added to the final results table. |
For every cluster, the distance from the cells to the cluster centers is
used to label cells which deviate too far as outliers. The threshold is
chosen as the median distance + madAllowed times the median absolute
deviation of the distances.
An outlier report
FlowSOMSubset if you want to get a subset of the
current data instead of a new dataset
# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) flowSOM.res <- FlowSOM(ff, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Map new data outlier_report <- TestOutliers(flowSOM.res, madAllowed = 5, channels = flowSOM.res$map$colsUsed) # Number of cells which is an outlier for x channels outlier_on_multiple_markers <- table(rowSums(outlier_report$channel_specific != 0)) outlier_type <- paste(GetClusters(flowSOM.res), apply(outlier_report$channel_specific, 1, paste0, collapse = "")) outlier_counts <- table(grep(" .*1.*", outlier_type, value = TRUE)) outliers_of_interest <- names(which(outlier_counts > 10)) outlier_boolean <- outlier_type %in% outliers_of_interest# Build FlowSom result fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) flowSOM.res <- FlowSOM(ff, compensate = TRUE, transform = TRUE, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10) # Map new data outlier_report <- TestOutliers(flowSOM.res, madAllowed = 5, channels = flowSOM.res$map$colsUsed) # Number of cells which is an outlier for x channels outlier_on_multiple_markers <- table(rowSums(outlier_report$channel_specific != 0)) outlier_type <- paste(GetClusters(flowSOM.res), apply(outlier_report$channel_specific, 1, paste0, collapse = "")) outlier_counts <- table(grep(" .*1.*", outlier_type, value = TRUE)) outliers_of_interest <- names(which(outlier_counts > 10)) outlier_boolean <- outlier_type %in% outliers_of_interest
Update old FlowSOM object to a new one and checks if it is a flowSOM object
UpdateFlowSOM(fsom)UpdateFlowSOM(fsom)
fsom |
Determines whether or not the fsom input is of class "FlowSOM" and returns the FlowSOM object and metaclustering object inside fsom
A FlowSOM object
Adapt the metacluster levels. Can be used to rename the metaclusters, split or merge existing metaclusters, add a metaclustering and/or reorder the levels of the metaclustering.
UpdateMetaclusters( fsom, newLabels = NULL, clusterAssignment = NULL, levelOrder = NULL )UpdateMetaclusters( fsom, newLabels = NULL, clusterAssignment = NULL, levelOrder = NULL )
fsom |
Result of calling the FlowSOM function. |
newLabels |
Optional. Named vector, with the names the original metacluster names and the values the replacement. Can be used to rename or merge metaclusters. |
clusterAssignment |
Optional. Either a named vector, with the names the cluster numbers (characters) or a vector of length NClusters(fsom). Can be used to assign clusters to existing or new metaclusters. |
levelOrder |
Optional. Vector showing the preferred order of the fsom metacluster levels. |
Updated FlowSOM object
fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) GetCounts(flowSOM.res) # Merge MC8 and MC9 flowSOM.res <- UpdateMetaclusters(flowSOM.res, newLabels = c("8" = "8+9", "9" = "8+9")) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) GetCounts(flowSOM.res) # Split cluster 24 from metacluster 2 and order the metacluster levels flowSOM.res <- UpdateMetaclusters(flowSOM.res, clusterAssignment = c("24" = "debris?"), levelOrder = c("debris?", as.character(c(1:7)), "8+9", "10")) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) PlotNumbers(flowSOM.res, level = "metaclusters") GetCounts(flowSOM.res)fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM") ff <- flowCore::read.FCS(fileName) ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]]) ff <- flowCore::transform(ff, flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]), flowCore::logicleTransform())) flowSOM.res <- FlowSOM(ff, scale = TRUE, colsToUse = c(9, 12, 14:18), nClus = 10, seed = 1) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) GetCounts(flowSOM.res) # Merge MC8 and MC9 flowSOM.res <- UpdateMetaclusters(flowSOM.res, newLabels = c("8" = "8+9", "9" = "8+9")) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) GetCounts(flowSOM.res) # Split cluster 24 from metacluster 2 and order the metacluster levels flowSOM.res <- UpdateMetaclusters(flowSOM.res, clusterAssignment = c("24" = "debris?"), levelOrder = c("debris?", as.character(c(1:7)), "8+9", "10")) PlotStars(flowSOM.res, backgroundValues = flowSOM.res$metaclustering) PlotNumbers(flowSOM.res, level = "metaclusters") GetCounts(flowSOM.res)
Update nodesize of FlowSOM object
UpdateNodeSize( fsom, count = NULL, reset = FALSE, transform = sqrt, maxNodeSize = 15, shift = 0, scale = NULL )UpdateNodeSize( fsom, count = NULL, reset = FALSE, transform = sqrt, maxNodeSize = 15, shift = 0, scale = NULL )
fsom |
FlowSOM object, as generated by |
count |
Absolute cell count of the sample |
reset |
Logical. If |
transform |
Transformation function. Use e.g. square root to let counts correspond with area of node instead of radius |
maxNodeSize |
Maximum node size after rescaling. Default: 15 |
shift |
Shift of the counts, defaults to 0 |
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 |
Add size property to the graph based on cellcount for each node
Updated FlowSOM object
# 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)# 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)