applyCutoffs: Single-cell debarcoding (2)
In CATALYST: Cytometry dATa anALYSis Tools

Description Usage Arguments Value Author(s) References Examples

Applies separation and mahalanobies distance cutoffs.

1	applyCutoffs(x, assay = "exprs", mhl_cutoff = 30, sep_cutoffs = NULL)

`x`	a `SingleCellExperiment`.
`assay`	character string specifying which assay data to use. Should be one of `assayNames(x)` and correspond to expression-like not count data.
`mhl_cutoff`	numeric mahalanobis distance threshold above which events should be unassigned; ignored if `metadata(x)$mhl_cutoff` exists.
`sep_cutoffs`	non-negative numeric of length one or of same length as the number of rows in the `bc_key(x)`. Specifies the distance separation cutoffs between positive and negative barcode populations below which events should be unassigned. If `NULL` (default), `applyCutoffs` will try to access `metadata(x)$sep_cutoffs`.

the input SingleCellExperiment x is returned with updated colData columns "bc_id" and "mhl_dist", and an additional int_metadata slot "mhl_cutoff" containing the applied mahalanobies distance cutoff.

Helena L Crowell helena.crowell@uzh.ch

Zunder, E.R. et al. (2015). Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nature Protocols 10, 316-333.

library(SingleCellExperiment)

# construct SCE
data(sample_ff, sample_key)
sce <- prepData(sample_ff)
    
# assign preliminary barcode IDs
# & estimate separation cutoffs
sce <- assignPrelim(sce, sample_key)
sce <- estCutoffs(sce)

# use estimated population-specific 
# vs. global separation cutoff(s)
sce1 <- applyCutoffs(sce)
sce2 <- applyCutoffs(sce, sep_cutoffs = 0.35)

# compare yields after applying cutoff(s)
c(global = mean(sce1$bc_id != 0), 
specific = mean(sce2$bc_id != 0))