Extended Data Fig. 1: Individual metrics for evaluating integration performance.
From: Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
a, Mean average precision vs. Seurat alignment score for different integration methods. Higher mean average precision indicates higher cell type resolution, and higher Seurat alignment score indicates better omics mixing. b, Cell type vs. omics layer average silhouette width for different integration methods. Higher cell type average silhouette width indicates higher cell type resolution, and higher omics layer average silhouette width indicates better omics mixing. c, Neighbor conservation vs. graph connectivity for different integration methods. Higher neighbor conservation indicates better conservation of manifold structure in each original layer, and higher graph connectivity indicates better omics mixing. n=8 repeats with different model random seeds. The error bars indicate mean ± s.d.
