Hi !

I've tried to compare the output from Cellranger 3.0 with DropletUtils::emptyDrops() in term of number of cell-containing droplets. The web-summary from the Cellranger run outputs 1240 cells which is indeed very consistant when looking at the UMI Vs ranked-barcodes plot (the drop of the curve is around the 1100-1300th barcode). It is far less than I expected with regard to the number of cells I've put as input but this could be explain by some technical reasons I guess (wrong cell counting, bad cell lysis).

However when running DropletUtils::emptyDrops() on the unfiltered Cellranger matrix.h5, I obtain very high number of cell-containing barcodes (15307 !!). This is with default parameters but I've tried to adjust and it doesn't change anything (see following code). How comes that DropletUtils::emptyDrops() outputs so different numbers than cell ranger 3.0? What should I do?

Thanks a lot !

sce <- read10xCounts("/Users/Gregoire/Desktop/Dowloaded_from_cluster/count/raw_feature_bc_matrix.h5", col.names=T)
sce
#CASE 1 use emptyDrops with default parameters : it will retain every cells above knee of UMI Vs ranked-barcode plot
counts <- counts(sce)
tic()
set.seed(100)
e.out.1 <- DropletUtils::emptyDrops(counts,BPPARAM = SerialParam())
summary(e.out.1$FDR < 0.001)
sum(e.out.1$FDR <= 0.001, na.rm=TRUE)
toc()

# RESULT = 15307

#CASE 2 use emptyDrops with retain=inf: it will retain every cells above inflection point of UMI Vs ranked-barcode plot
tic()
set.seed(100)
e.out.2 <- DropletUtils::emptyDrops(counts, retain = Inf)
summary(e.out.2$FDR < 0.001)
sum(e.out.2$FDR <= 0.001, na.rm=TRUE)
toc()

# RESULT = 15307

#CASE 3 use emptyDrops on counts matrix without ribosomal or mitochondrial genes that could be captures in non-cell-containing drops
qc <- list(Mito = grep("^MT-",rowData(sce)$Symbol),
Ribo = grep("^RPS",rowData(sce)$Symbol)
)
keep <- !row.names(counts) %in% unlist(qc)

tic()
set.seed(100)
e.out.3 <- DropletUtils::emptyDrops(counts[keep, ])
summary(e.out.3$FDR < 0.001)
sum(e.out.3$FDR <= 0.001, na.rm=TRUE)
toc()

# RESULT = 15307

#CASE 4 use emptyDrops with lower =200 : in case the knee is artificially put in small bump of the curve at high ranked-barcode position
tic()
set.seed(100)
e.out.4 <- DropletUtils::emptyDrops(counts,lower =200)
summary(e.out.4$FDR < 0.001)
sum(e.out.4$FDR <= 0.001, na.rm=TRUE)
toc()

# RESULT = 5960