Fig. 1: Multimodal data integration and clustering analysis for identifying multimodal fusion subtypes. | Nature Communications

Fig. 1: Multimodal data integration and clustering analysis for identifying multimodal fusion subtypes.

From: Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities

Fig. 1

A Overview of the multimodal data integration process. Feature matrices from whole-exon sequencing (WES), transcriptomic (RNA-seq), proteomic (LC-MS), pathomic (WSI), and radiomic data were integrated using 11 distinct algorithms for intermediate fusion, followed by late fusion to generate a consensus clustering. B Clustering prediction index (CPI) and GAP statistics were calculated to determine the optimal number of clusters, with K = 3 identified as the optimal clustering number. C Heatmap of the consensus matrix for 122 IDH-wildtype glioma patients, showing three distinct multimodal fusion subtypes (MOFS1, MOFS2, and MOFS3). D Principal component analysis (PCA) demonstrating distinct separation among the three identified MOFS in two-dimensional space.

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