Plot the average expression value of two subsets of the data. Generally these might be 1 cell and multiple-cell replicates, in which case if the mcols column ncells is set then the averages will be adjusted accordingly. But it could be any grouping.

plotSCAConcordance(SCellAssay, NCellAssay, filterCriteria = list(nOutlier
  = 2, sigmaContinuous = 9, sigmaProportion = 9), groups = NULL, ...)

Arguments

SCellAssay

is a FluidigmAssay for the 1-cell per well assay

NCellAssay

is a FluidigmAssay for the n-cell per well assay

filterCriteria

is a list of filtering criteria to apply to the SCellAssay and NCellAssay

groups

is a character vector naming the group within which to perform filtering. NULL by default.

...

passed to getConcordance

Value

printed plot

See also

getConcordance

Examples

data(vbetaFA) sca1 <- subset(vbetaFA, ncells==1) sca100 <- subset(vbetaFA, ncells==100) plotSCAConcordance(sca1, sca100)
#> Using primerid as id variables
#> Using primerid as id variables
#> Using primerid as id variables
#> Using primerid as id variables
#> Sum of Squares before Filtering: 14.89 #> After filtering: 14.01 #> Difference: 0.87