Barcode rank plots
For each sample, barcodes are ranked in descending order based on
their library size (number of unique molecular identifiers (UMIs)).
Typically, one can observe two plateaus in the barcode-rank curve: the
first corresponds to droplets containing cells with high RNA content,
while the second represents empty droplets containing ambient RNA. Key
transition points on the curve (shin and knee points), are annotated
with horizontal lines. Two main parameters infered by
scprocess based on transition points are
expected_cells and empty_plateau_middle.
empty_plateau_middle should extend a few thousand barcodes
into the second plateau. The absence of a clear empty plateau indicates
poor sample quality due to ambient RNA contamination.
Ordered by slope ratio
The slope ratio helps identify low-quality samples with high ambient RNA contamination. This ratio is calculated by dividing the slope of the barcode-rank curve in the empty droplet plateau by the slope at the first inflection point. Samples are ordered from highest to lowest based on the slope ratio.
for (i in 1:length(slope_ratio_split_l) ) {
# get knee dfs for all selected samples
samples = slope_ratio_split_l[[i]]
name = sprintf('sample chunk #%d', i)
knee_fs = rna_knee_fs[samples]
cat('#### ', name, '\n')
suppressWarnings(print(plot_barcode_ranks_w_params(knee_fs, rna_knee_params_df,
sample_var = sample_var, bender_priors_df = NULL)))
cat('\n\n')
}sample chunk #1
sample chunk #2
Ordered by ratio of expected cells to empty droplets
Inaccurate detection of the transition points (knee and shin points)
in the barcode-rank curve can lead to incorrect estimates of barcodes
corresponding to cells or empty droplets. To evaluate this, samples are
ordered by the ratio of expected_cells to
empty_plateau_middle.
for (i in 1:length(exp_tot_ratio_split_l) ) {
# get knee dfs for all selected samples
samples = exp_tot_ratio_split_l[[i]]
name = sprintf('sample chunk #%d', i)
knee_fs = rna_knee_fs[samples]
cat('#### ', name, '\n')
suppressWarnings(print(plot_barcode_ranks_w_params(knee_fs, rna_knee_params_df,
sample_var = sample_var, bender_priors_df = NULL)))
cat('\n\n')
}sample chunk #1
sample chunk #2
Dotplot with diagnotic ratios
Both the slope ratio and the expected cells to empty droplets ratio are displayed in a dot plot, where each dot represents a sample. Outliers are labeled with their corresponding sample_id values.
print(plot_amb_params_dotplot(rna_knee_params_df, sample_var = sample_var, scales = 'free'))
Library sizes and splice read proportions: comparison between cells and empty droplets across samples
This plot shows the distribution of UMIs and spliced read proportions for both empty droplets and expected cells, split by sample. The UMI counts in empty droplets serve as an indicator of ambient RNA contamination in the dataset.
print(plot_qc_metrics_split_by_cells_empties(rna_knee_fs, sample_var = sample_var, n_cores = n_cores))
R session info
Details of the R package versions used are given below.
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.3 (2025-02-28)
## os Red Hat Enterprise Linux 8.10 (Ootpa)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/Zurich
## date 2026-03-25
## pandoc 3.8.2.1 @ /home/macnairw/packages/scprocess/.snakemake/conda/4fef11cadd34f9d2d13a0d6139d09340_/bin/ (via rmarkdown)
## quarto NA
##
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