SPADE.markerMedians: Compute marker medians, coefficient of variations and counts...
In spade: SPADE -- An analysis and visualization tool for Flow Cytometry

Description Usage Arguments Details Value Author(s) See Also Examples

Compute the marker medians, coefficients of variation and observations counts for cluster annoated FCS files.

1
2

SPADE.markerMedians(files, num.clusters, cols = NULL, arcsinh_cofactor=NULL, transforms=flowCore::arcsinhTransform(a=0, b=0.2), cluster_cols=NULL, comp=TRUE)
SPADE.annotateMarkers(files, cols = NULL, arcsinh_cofactor = 5)

`files`	Name of input FCS file or vector of input FCS file names. FCS files must have "cluster" column.
`num.clusters`	Number of clusters. Note not all clusters need to be present in all files.
`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.
`arcsinh_cofactor`	DEPRECATED. Cofactor used in arcsinh transform `asinh(data/arcsinh_cofactor)` of data.
`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).
`cluster_cols`	A vector of strings specifying columns that should be marked as having been used in clustering
`comp`	Apply compensation matrix if present in SPILL or SPILLOVER keywords

SPADE.annotateMarkers is deprecated.

List with:

`count`	Matrix of observation count for clusters
`percenttotal`	Matrix of percent of total number of cells [0-100] in each cluster
`medians`	Matrix of medians for specified columns
`cvs`	Matrix of coefficient of variation (CV), 100*sd(data)/abs(mean(data)), for specified columns

Michael Linderman

SPADE.addClusterToFCS, SPADE.annotateGraph

	# Not run
	## Load two-parameters sample data included in package
	#data_file_path = paste(installed.packages()["spade","LibPath"],"spade","extdata","SimulatedRawData.fcs",sep=.Platform$file.sep)

	## Run basic SPADE analyses, clustering on two parameters. 
	#output_dir <- tempdir()
	#SPADE.driver(data_file_path, out_dir=output_dir, cluster_cols=c("marker1","marker2"))

	## Compute medians, counts and other parameters from processed files
	#upsampled_file_path <- paste(output_dir,.Platform$file.sep,basename(data_file_path),".density.fcs.cluster.fcs",sep="")
	#mst_graph <- igraph:::read.graph(paste(output_dir,"mst.gml",sep=.Platform$file.sep),format="gml")
	#anno <- SPADE.markerMedians(upsampled_file_path, igraph:::vcount(mst_graph), cols = c("marker1","marker2"))