correctUnmix: Correct defects in spectral unmixing by compensation
In flowSpecs: Tools for processing of high-dimensional cytometry data

Description Usage Arguments Value See Also Examples

This function provides a way to reduce the defects in the spectral unmixing, by creating a secondary correction matrix, which is symmetrical.

1	correctUnmix(unmixFlowObj, corrMat, transCoFacs = 400)

`unmixFlowObj`	A flowframe or flowset post unmixing.
`corrMat`	A correction matrix. If this is the first round, the executionof this function needs to be preceeded by the generation of this matrix, for example by using the `corrMatCreate` function.
`transCoFacs`	If transformation should be performed, the transformation cofactors can be added here. Three possible inputs: a vector with specific cofactors for each variable, a set value that will be used for all variables, and FALSE. Note: It might be good to set this to FALSE in the final round, to optimize the transoformations externally.

The unmixed flow object, now corrected with the values from the corrMat.

specUnmix, arcTrans, corrMatCreate

# Load uncompensated data
data(fullPanel)

# Load the spectral unmixing matrix generated with controls from the same
# experiment. These can be generated using the specMatCalc function.
data(specMat)

# And now unmix
fullPanelUnmix <- specUnmix(fullPanel, specMat)


# Create an empty unmixinng matrix
corrMat <- corrMatCreate(specMat)

# Now correct the data with this. In the first instance, this will of course
# not have any effect, more  than transformation, as the corrMat is empty.
fullPanelCorr <- correctUnmix(fullPanelUnmix, corrMat)

# This now needs to be investigated, to identify any possible compensation
# defects. This is most easily done with the oneVsAllPlot executed in the
# following way:
## Not run: 
oneVsAllPlot(fullPanelCorr)

## End(Not run)
# One obvoius defect that shows when doing this is between CD56 and IgM:
oneVsAllPlot(fullPanelCorr, "BV650_CD56", saveResult = FALSE)

# This is corrcted the following way:
corrMat["BV650_CD56", "AF647_IgM"] <- -0.03
fullPanelCorr <- correctUnmix(fullPanelUnmix, corrMat)
oneVsAllPlot(fullPanelCorr, "BV650_CD56", saveResult = FALSE)

# This process is iterated until there are no remaining artifacts. Good help
# to do this is a set of fluorescence-minus-one controls. If that is not
# available, a rule of thumb is that if the signal in marker x is
# strongly negatively correlated to marker y, so that highly
# single-x-posisive values are below zero, then this is with all likelihood
# an artifact. The situation becomes more complicated with strong positive
# correlations, as they can occur in biology, so there one has to take more
# care and keep the marker biology in mind.