Supplementary Figure 11: Validation of cycling cells and comparison with other methods.
From: SCENIC: single-cell regulatory network inference and clustering
(a) Identification of cell cycle cells based on the Z-score of gene sets related to cell cycle. (b) Heatmap showing the Z-score of cell cycle gene-sets on the Oligodendroglioma dataset. The blue/red bars on the top of the heatmap highlight the cells selected as cycling (red) by three approaches: (1) the AUC scores of SCENIC's E2F1 regulon; (2) the G1/S scores according to Tirosh et al. and the G2/M scores according to Tirosh et al; Tirosh et al. approach with a permissive cut-off (cells are classified as cycling if their G1/S and G2/M scores are above twice the mean within the cell population); Tirosh et al. approach with a more restrictive cut-off (cells are classified as cycling if their G1/S and G2/M scores are above four times the mean within the cell population); and (3) the GO genesets based Z-score. (c,d) Comparison of the capacity of different methods to identify the cycling cells: (c) Number of cells recovered, sorted by True Positive Rate (TPR, also known as sensitivity or recall). Colored bars represent the number of cell cycle cells identified by the method, in white the number of non-cell cycle cells included in the selected cluster (the cluster with the highest number of cell cycle cells). (d) Recall vs Precision (SCENICA: High-confidence cells, SCENICB: Lower confidence/regulon activity).