Fig. 1

Overview of the SICILIAN statistical framework and its performance evaluation based on benchmarking datasets. A High and variable fraction of junctional reads across diverse cell types in the HLCA dataset [11]. Each violin plot shows the fraction of mapped reads in each cell (within a cell type) that are junctional. B SICILIAN takes the alignment information file (usually in the form of a BAM file) from a spliced aligner such as STAR and then deploys its statistical modeling to assign a statistical score to each junction. C SICILIAN utilizes the cell-level statistical scores (empirical p values) for each junction across 10x samples to correct for increased false discovery rates due to multiple hypothesis testing. The corrected score is called the “SICILIAN score” and can be used to consistently call junctions across cells. (D) SICILIAN improves the concordance between detected splicing junctions in single cells and bulk cell lines. (E) ROC curves by SICILIAN and read count criteria for four simulated datasets [8, 12] (the top two based on data from [12] and the bottom two based on data from [8])