Description Usage Arguments Value Note Author(s) See Also Examples
Hierarchically cluster observations in a set of FCS files and build a minimum spanning tree connecting those clusters.
1 2 3 | SPADE.FCSToTree(infilenames, outfilename, graphfilename, clusterfilename,
cols = NULL, k = 200, arcsinh_cofactor=NULL, transforms=flowCore::arcsinhTransform(a=0, b=0.2),
desired_samples = 50000, comp=TRUE)
|
infilenames |
Vector of FCS file names that should be used as input |
outfilename |
Name of FCS file to write subset of cells used for clustering along with their cluster assignment |
graphfilename |
Name of file to write gml graph description |
clusterfilename |
Name of file to write table of cluster centers |
cols |
Usually a vector of strings specifying the columns to be used in the density calculation, e.g., c("(Cd110)D","(Cs111)D"). Strings will be matched against the parameter names extracted from the FCS file. The default=NULL will use all parameters. |
k |
Desired number of clusters. Algorithm might create between [k/2,3k/2] clusters. |
arcsinh_cofactor |
DEPRECATED. Cofactor used in arcsinh transform |
transforms |
Transform object to apply to data. A single transform object will be applied to all channels. To apply different transforms to specific channels use a named vector of transform objects (where names are parameter names). |
desired_samples |
Desired number of samples to be used in clustering. Usually leave at default. |
comp |
Apply compensation matrix if present in SPILL or SPILLOVER keywords |
None.
Underlying implementations have been parallelized with OpenMP. Set
OMP_NUM_THREADS
in environment to control the number of threads used.
Implementation can be very memory intensive.
Michael Linderman
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Load two-parameters sample data included in package
#data_file_path = paste(installed.packages()["spade","LibPath"],"spade","extdata","SimulatedRawData.fcs",sep=.Platform$file.sep)
#output_dir <- tempdir()
#
## Compute and annotate FCS file with density
#density_file_path <- paste(output_dir,.Platform$file.sep,basename(data_file_path),".density.fcs",sep="")
#SPADE.addDensityToFCS(data_file_path, density_file_path, cols=c("marker1","marker2"))
## Downsample FCS file based on density
#downsample_file_path <- paste(output_dir,.Platform$file.sep,basename(data_file_path),".density.fcs",sep="")
#SPADE.downsampleFCS(density_file_path, downsample_file_path)
## Create tree from downsampled FCS file
#cells_file_path <- paste(output_dir,"clusters.fcs",sep="")
#clust_file_path <- paste(output_dir,"clusters.table",sep="")
#graph_file_path <- paste(output_dir,"mst.gml",sep="")
#SPADE.FCSToTree(downsample_file_path, cells_file_path, graph_file_path, clust_file_path, cols=c("marker1","marker2"))
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