specMatCalc: Calculating the matrix used for spectral unmixing
In flowSpecs: Tools for processing of high-dimensional cytometry data

Description Usage Arguments Value Examples

This algoritm takes a flowSet containing single-stained controls and negative controls, including an autofluorescence control and estimates the unmixing for all fluorescent variables.

1	specMatCalc(unmixCtrls, groupNames, autoFluoName)

`unmixCtrls`	A flowSet containing all the single stained and unstained files necessary to create an spectral unmixing matrix.
`groupNames`	A character vector containing strings common to the groups of non-autofluoresence unmixCtrls that could be present. If for example all antibodies single stains are anti-mouse bead-based the dead cell marker is stained PBMC, and the files congruently either have a prefix containing "Bead" or "PBMC", then the vector should be c("Bead", "PBMC"). The system is not case specific.
`autoFluoName`	The sample name of the autofluorescence control.

A data frame with each row representing a fluorochrome or or autofluorescence and each column representing a detector.

# Load suitable unmixing controls. NB! If these originate from different
# sample types, such as beads and PBMC, there should be a negative control
# for each group and the names should reflect this, so that all PBMC samples
# would be called PBMC_unstained, PBMC_DCM, etc.
data(unmixCtrls)

# If the dataset contains cell controls, make sure that the cell population
# interest dominates FSC-A, as the data highest peak in this channel will be
# used.

# And run the function
specMat <- specMatCalc(unmixCtrls, groupNames = c("Beads_", "Dead_"),
autoFluoName = "PBMC_unstained.fcs")