correctUnmix {flowSpecs} | R Documentation |
This function provides a way to reduce the defects in the spectral unmixing, by creating a secondary correction matrix, which is symmetrical.
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 |
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.