Extended Data Fig. 9: Clustering results for a mouse brain spatial transcriptomics (10x Visium) and metabolomics (MALDI-MSI) dataset, which was generated following the protocol described in Vicari et al. [9].
From: Resolving tissue complexity by multimodal spatial omics modeling with MISO
To make the super-resolution spatial molecular data inferred by iStar more suitable for input to all methods, we merged superpixels obtained from iStar to create 4,687 pseudo-spots of size 128×128 pixels, containing paired gene expression and metabolite information. Because the RNA data is of low quality (e), the RNA-specific features extracted by MISO were not used to produce any of the results provided, but RNA was still accounted for in its interactions with metabolomics and image features. For all applicable results, metabolomics data were normalized by total intensity and log transformed. a, Clustering results from MISO, MUSE, and SpatialGlue when taking RNA and histology image data as input. b, Clustering results from MISO, MUSE, and SpatialGlue when taking RNA and metabolomics data as input. c, Clustering results from MISO, MUSE, and SpatialGlue when taking metabolomics and histology image data as input. d, Clustering results from MISO when taking RNA, histology image, and metabolomics data as input. e, Total UMI counts across all spots in the dataset. The UMI counts are low because the tissue section was analyzed using MALDI-MSI prior to Visium. f, Total metabolite intensities across all spots in the dataset. g, H&E-stained histology image of analyzed tissue section. h, RNA, image, and metabolomics ICC distributions across all clusters and features for each method when taking each possible combination of modalities as input (n = 1500 ICC values for each group). For each method, the mean ICC for each modality is printed on the corresponding box plot for its top-performing combination of modalities. Test statistics and p-values were obtained using one-sided t-tests when comparing each method’s top-performing results for a given modality. Box plots: center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers.
