Single-cell data integration across weakly linked modalities
Fig 7
Application of MMIHCL in disease classification and drug target discovery.
(a) UMAP visualization of joint embeddings on the HPAP dataset. The top row subgraphs are colored by disease status (Control vs. T1D), and the bottom row subgraphs are colored by cell type. The clustering performance metrics (NMI and ARI) in the top row are calculated using all cells, whereas those in the bottom row are computed using exclusively the T1D subpopulation. (b) Split violin plots comparing the expression distributions of three representative interferon-stimulated genes (CXCL10, CXCL11, and CCL2) from the Kang18 PBMC dataset across Seurat, MARIO, and MMIHCL. The numeric values annotated above the violins indicate the statistical significance (P-values) derived from the Welch’s t-tests. (c) Volcano plot visualizing the DEGs identified by MMIHCL on the Kang18 PBMC dataset. Significantly up-regulated and down-regulated genes are highlighted in color, whereas non-significant genes are displayed in gray.