Fig. 8

The specific strategies applied in scIBD. a Two cases that are suitable for different KNN-graphing strategies. Left panel illustrates the case where different cell types are distinguishable in UMAP based on PCA embeddings, and the doublets are also distinctly apart from the singlets, PCA-based graphing is applied. Right panel illustrates the case where the distinction of the cells is not clear by following PCA-based strategy, PCoA-based graphing is applied to further separate the doublets from singlets. b The distribution plot of the doublet scores during the iteration process. We separately show the doublet score distributions of three parts, the detected doublets in former iterations, the simulated doublets in each iteration, and the unlabeled droplets in raw sets. In each iteration, we aim at separating doublets from the unlabeled droplets. The doublet scores of the unlabeled droplets are modeled by the right side of a standard Gaussian. The scores of the simulated doublets (yellow) are used as the reference to obtain the threshold to determine the doublets in the unlabeled droplets. The scores of the doublets detected in former iterations (red) mostly locate at high intervals, showing their high confidence as the doublets