Figure 1
Applying a hard count threshold fails to remove droplets contaminated with background RNA in snRNA-seq. (a) Barcode-rank plots showing the droplet size (the total number of UMI read counts) of each droplet in descending order for the differentiating preadipocytes (DiffPA), mouse brain, and six human frozen adipose tissue (AT) snRNA-seq samples. The dotted red line indicates the quantile-based threshold. (b) The number of droplets above and below the quantile-based hard-count threshold is shown. The height of the red bar indicates the number of background droplets in the category indicated in the x-axis, while the height of the blue bar indicates the number of nuclear droplets. Background and nuclear droplets are defined using the percent spliced reads. Ideally, all nuclear droplets would occur above the threshold and all background droplets would occur below. (c) UMAP33 visualization of droplets in each of the three data sets with droplets colored by the percent of reads spliced. (d) The droplets above the quantile threshold were clustered using Seurat20. The x-axis shows the clusters, and the y-axis shows the distribution of the percent of reads spliced for each cluster. Background droplets with a high percent of reads spliced tend to cluster together.
