Inferring pathway activity from single-cell and spatial transcriptomics data with PaaSc
Fig 2
Comprehensive performance evaluation of PaaSc and other gene set scoring methods using REAP-seq data and benchmark datasets.
(A) UMAP visualization of nine distinct cell populations identified in human PBMCs profiled by REAP-seq, including CD4 + T cells, CD8 + T cells, natural killer (NK) cells, plasmacytoid dendritic cells (pDCs), dendritic cells (DCs), CD14 + monocytes, CD16 + monocytes, and megakaryocytes (Mk). (B) Performance comparison of different gene set scoring methods using established cell type-specific markers. Box plots show the distribution of AUC scores across all cell types for each method. The centerline represents the median value, the box limits indicate the first and third quartiles, and the whiskers extend to the minimum and maximum values. (C) Assessment of the robustness of the gene set scoring tools against random noise. The line plot shows the mean AUC scores of different methods when varying proportions (10–80%) of random genes were introduced into cell type-specific marker sets. (D) Cell type annotation performance of different methods evaluated using marker genes from 20 predefined cell types. Box plots show the distributions of recall (upper), precision (middle), and F1 scores (lower) across all cell types. Unassigned cells were excluded from the calculation. (E) Cross-dataset validation using five independent benchmark datasets (Liver, Pancreas, Spleen, Bmcite, and Hcortex). The heatmap shows the mean AUC scores for each method across different datasets.