Fig. 3

Benchmark RESA to other methods in the in silico spike-in scRNA-seq datasets. a Workflow for in silico spike-in. scRNA-seq raw reads of pancreas tissues from 3 healthy juveniles using SMART-seq2 technology in Enge et al. 2017 [12] were collected as original BAM files. Somatic SNVs of 10 cancer cell lines covering 5 tissues of origin were identified from WES data. Bamsurgeon spiked cancer cell line somatic SNVs into the original BAM files to produce ‘Burn-in’ BAM files. In silico spike-in scRNA-seq datasets were ordered by the coverage of each cell, split into several subsets, and followed by further evaluations. b, The violin plot illustrates the distribution of coverage in each subset. The error bars display the average and standard deviation of the precision of RESA, RESA-jLR, and the other 5 previously published algorithms. If the minimum value of the error bar is less than 0, 0 is shown. Top: a 4-month-old infant (Blue). Middle: a 5-year-old child (Purple). Bottom: a 6-year-old child (Green). c The scatter plot shows precisions and sensitivities in different subsets of 10 cell lines. Points in red are the results of RESA, and points in blue are the results of RESA-jLR. Top: a 4-month-old infant. Middle: 5-year-old child. Bottom: a 6-year-old child. d F0.5 score in in silico spike-in scRNA-seq datasets (Wilcoxon rank-sum test, NS: not significant, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). Top: a 4-month-old infant (Blue). Middle: 5-year-old child (Purple). Bottom: a 6-year-old child (Green). e The bar plot illustrates the number of “spiked-in” mutations across 10 cell lines. Top: a 4-month-old infant (Blue). Middle: 5-year-old child (Purple). Bottom: a 6-year-old child (Green)