Hello, I have produced quite some single cell CITE-seq data at the single cell level, using the 10x genomics platform and analyzed in Seurat. I am at the moment trying to visualize the protein markers from the CITEseq against to each other in a way that would resemble how we see and interpret FACS data (with that also one could possible "gate" on single-positive and double positive populations the way we do in FACS analysis). Many times when I plot the expression level(using the Seurat function FeatureScatter) after normalization what I see is a plot like this:

In this case, it is hard to recognize the single positive and/or double-positive populations but when I apply a log10 transformation with the code below, I get a better picture for the double positives.

FeatureScatter(alldata,"NKG2D","LAG3",group.by = "exp",cols = "grey") + scale_x_log10(limits=c(0.2,5)) + scale_y_log10(limits=c(0.2,5))

However, after the log transformation, all the cells with expression value 0 for these markers would just go to -Inf (we can see them stuck on the axes). Is there a way to correct for this?

For the same reason (generation of -Inf values), I also get this error after applying a geom_density_2d to the plot:

Caused by error in `MASS::kde2d()`:
! only finite values are allowed in 'lims' 
8: In min(x, na.rm = na.rm) :
  no non-missing arguments to min; returning Inf
9: In max(x, na.rm = na.rm) :
  no non-missing arguments to max; returning -Inf
10: In max(f) : no non-missing arguments to max; returning -Inf

Is there a better way to show these data? Is there a way to go for a bi-exponential correction as in FACS?