Fig. 3
From: diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering
Benchmarking results for dataset BCR-XL-sim. a Performance metrics for dataset BCR-XL-sim, testing for differential states (DS) within cell populations. Panels show (i) receiver operating characteristic (ROC) curves and (ii) true positive rate (TPR) vs. false discovery rate (FDR) (also indicating observed TPR and FDR at FDR cutoffs 1%, 5%, and 10%). b Results for additional null simulations, where no true spike-in signal was included; p-value distributions are approximately uniform (additional replicates are included in Supplementary Fig. 13). c Heatmap displaying phenotypes (expression profiles) of detected and true differential clusters, along with the signal of interest (expression of signaling marker pS6, by sample), for method diffcyt-DS-limma. Expression values represent median arcsinh-transformed expression per cluster across all samples (left panel) or by individual samples (right panel). Rows (clusters) are grouped by hierarchical clustering with Euclidean distance and average linkage; the heatmap shows the top 20 most highly significant clusters. Vertical annotation indicates detected significant cluster–marker combinations at 10% FDR (red) and clusters containing >50% true spiked-in cells (black). (Additional heatmaps are included in Supplementary Fig. 14.) d Results for varying clustering resolution (between 9 and 1600 clusters); showing partial area under ROC curves (pAUC) for false positive rates (FPR) <0.2. Performance metric plots generated using iCOBRA42; heatmaps generated using ComplexHeatmap43
