Switch to a curve-tracing algorithm for knee detection.

[LTLA]

I gotta say, now that I don't have to deal with the daily grind of full time employment, the ideas just keep on coming.

library(DropletTestFiles)
X <- listTestFiles()
keep <- grep("Raw HDF5", X$description)

png("results.png", width=6, height=10, units="in", res=120)
library(DropletUtils)
par(mfrow=c(3,2))
for (i in keep) {
    path <- getTestFile(X$rdatapath[i], prefix=FALSE)
    se <- read10xCounts(path, type="HDF5")
    mat <- as(assay(se), "SVT_SparseMatrix")
    current <- DropletUtils:::.barcode_ranks(mat)
    o <- order(current$rank)
    plot(current$rank[o], current$total[o], log="xy", type="l", main=X$file.dataset[i])
    abline(h=metadata(current)$knee, lty=2)
    abline(h=metadata(current)$inflection, lty=3)
}
dev.off()

And for Pete's data here:

X <- readRDS("pete_data.rds")
mat <- rbind(X$total) # pretend it's a matrix, only the colSums matter anyway
current <- DropletUtils:::.barcode_ranks(mat)
o <- order(current$rank)

png("pete_results.png", width=6, height=6, units="in", res=120)
plot(current$rank[o], current$total[o], log="xy", type="l")
abline(h=metadata(current)$knee, lty=2)
abline(h=metadata(current)$inflection, lty=3)
dev.off()