dropkick recovers expected cell populations and eliminates low-quality barcodes in experimental data. (A) Plot of coefficient values for 2000 highly variable genes (top) and mean binomial deviance ± SEM (bottom) for fivefold cross-validation along the lambda regularization path defined by dropkick. The top and bottom three coefficients are shown, in axis order, along with total model sparsity representing the percentage of coefficients with values of zero (top). Chosen lambda value indicated by dashed vertical line. (B) Joint plot showing scatter of percentage of ambient counts versus arcsinh-transformed genes detected per barcode, with histogram distributions plotted on margins. Initial dropkick thresholds defining the training set are shown as dashed vertical lines. Each point (barcode) is colored by its final dropkick score after model fitting. (C) UMAP embedding of all barcodes kept by dropkick_label, CellRanger_2, and EmptyDrops. Points colored by each of the three filtering labels, as well as Leiden clusters determined by NMF analysis, dropkick score (cell probability), and percentage counts mitochondrial. Circled area shows high mitochondrial enrichment in a population discarded by dropkick. (D) Dot plot showing top differentially expressed genes for each NMF cluster. The size of each dot indicates the percentage of cells in the population with nonzero expression for the given gene, and the color indicates the average normalized expression value in that population. Bracketed genes indicate significantly enriched populations in EmptyDrops compared with dropkick_label as shown in E. (E) Table and bar graph enumerating the total number of barcodes detected by each algorithm in all NMF clusters. Significant cluster enrichment as determined by sc-UniFrac is denoted by brackets.
