specMatCalc {flowSpecs} | R Documentation

## Calculating the matrix used for spectral unmixing

### Description

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

### Usage

    specMatCalc(unmixCtrls, groupNames, autoFluoName)

### Arguments

`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.

### Value

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

### Examples

    # 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")

[Package flowSpecs version 1.2.0 Index]