Fig. 2: Benchmarking of DeepMAPS in terms of cell clustering.
From: Single-cell biological network inference using a heterogeneous graph transformer
a Benchmark cell clustering results of ten datasets in ARI for the three multiple scRNA-seq data and the three CITE-seq data with benchmark labels, and ASW for the four scRNA-ATAC-seq data without benchmark labels. Each box showcases the minimum, first quartile, median, third quartile, and maximum ARI or AWS results of a tool using different parameter settings (DeepMAPS: n = 96, Seurat: n = 16 for RNA-RNA and CITE-seq and 36 for RNA-ATAC, Harmony: n = 36, MOFA + : n = 36, TotalVI: n = 48, and GLUE: n = 72). Dots represent outliers. b Results comparison on five independent datasets. No repeated experiment was conducted. c Robustness test of DeepMAPS using the cell cluster leave-out method for the three independent test datasets with benchmarking cell labels. p-values were calculated based on two-tail t.test. Each box showcases the minimum, first quartile, median, third quartile, and maximum ARI results of a tool performed on different data subsets (R-test: n = 5, C-test: n = 20, and A-test-1: n = 5). Dots represent outliers. d–f UMAP comparison of R-test, C-test, and A-test-1 datasets between DeepMAPS and other tools using the original cell labels. Source data are provided as a Source Data file. ASW average Silhouette width, ARI adjusted rand index.
