Extended Data Fig. 1: Image and transcriptomic representations.

a, Clustering performance on ST-bank data with cell type annotation. Left: clustering performance using transcriptomic embeddings generated from OmiCLIP model before and after training. Right: clustering performance usings image embeddings from OmiCLIP model before and after training. The Calinski-Harabasz scores were calculated on the embeddings (Methods) using the pretrained OmiCLIP transcriptomic (left) and image (right) encoders, evaluated for each organ type. Higher Calinski-Harabasz scores indicate better separation capability between clusters of the embeddings. In the box plots, the middle line represents the median, the box boundaries indicate the interquartile range, and the whiskers extend to data points within 1.5× the interquartile range. b, Image and transcriptomic embeddings of the lung, kidney cancer, healthy heart, and Myocardial Infarction (MI) heart samples. Each row corresponds to a WSI and showcases information from two modalities. The first column are H&E images showing tissue morphology; the second column are the heatmaps of ST data with the colors indicating the cell types; the third column are the UMAP of image embeddings colored by cell types before and after contrastive learning; the fourth column are the UMAP of transcriptomics embeddings colored by cell types before and after contrastive learning.