spade (version 1.20.0)

SPADE.markerMedians: Compute marker medians, coefficient of variations and counts for clusters

Description

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

Usage

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)

Arguments

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

Value

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

Details

SPADE.annotateMarkers is deprecated.

See Also

SPADE.addClusterToFCS, SPADE.annotateGraph

Examples

	# 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"))