Fig. 2: Assessment of dsb model assumptions and performance of dsb normalization on external datasets.

Panels a–h Application of dsb to a publicly available dataset generated using 10X genomics “NextGem” chemistry measuring 29 proteins across ~5K cells. a The protein library size distribution of empty and cell-containing droplets used for dsb normalization. b UMAP of single cells based on dsb normalized protein values with colors representing clusters obtained from clustering cells on dsb normalized protein values. c Heatmap of the average of dsb normalized values per protein-based cluster shown in (b). d The distribution of CD14 and CD4 dsb normalized values. e As in Fig. 1e, Gaussian mixture model parameters fit to the dsb normalized values of each single cell after step I (ambient noise/background droplet based correction). The Bayesian Information Criterion (BIC) of the model vs. number of components in the model fit for each cell (n = 3774 cells). Boxplots show the median with hinges at the 25th and 75th percentile and whiskers extending plus or minus 1.5 times the inter quartile range. f As in Fig. 1f, Pearson correlation coefficient matrix of variables used to define each cell’s technical component; each isotype control and µ1, the Gaussian mixture model background mean across proteins for each cell. g As in Fig. 1g, Pearson correlation coefficient between the inferred cell-specific background mean µ1 from the Gaussian mixture model vs. the mean of isotype controls in each cell. h The relationship between each cell’s technical component and the cell’s protein library size (Pearson correlation coefficient shown as in Supplementary Fig 3a with 95% confidence interval in gray). i Summary statistics for the eight independent datasets assessed in this study; Cor 1 and 2 correspond to the Pearson correlation coefficient for assessing the relationships between variables shown in (h) and (g) across cells for each dataset.