Unimodal Embeddings for SpatialFusion These WDL workflows generate the unimodal embedding inputs used by SpatialFusion: GenerateUnimodalEmbeddingsForSpatialFusion generates both scGPT.parquet and UNI.parquet from one Terra submission. GenerateScgptEmbeddingsForSpatialFusion generates scGPT.parquet from spatial transcriptomics data. GenerateUniEmbeddingsForSpatialFusion generates UNI.parquet from H&E / whole-slide imaging data. Use the combined workflow if you want both outputs from one Terra submission. Use the modality-specific workflows for scGPT-only or UNI-only runs. Combined Inputs adata: AnnData (.h5ad) file used for scGPT embeddings and for the spatial coordinates consumed by UNI. Spatial coordinates are expected in adata.obsm["spatial"]. input_is_log_normalized: whether the AnnData expression values in the selected layer are already log-normalized. wsi: H&E / whole-slide image in TIFF / OME-TIFF format. uni_weights: UNI model weights file (pytorch_model.bin) from Mahmood Lab. scGPT Inputs adata: AnnData (.h5ad) file used for scGPT embeddings. input_is_log_normalized: whether the AnnData expression values in the selected layer are already log-normalized. scGPT weights are bundled in the workflow Docker image at /app/scgpt_weights, so users do not need to provide a scGPT weights input. UNI Inputs adata: AnnData (.h5ad) file with spatial coordinates stored in adata.obsm["spatial"]. wsi: H&E / whole-slide image in TIFF / OME-TIFF format. uni_weights: UNI model weights file (pytorch_model.bin) from Mahmood Lab. UNI weights are not bundled with the workflow. Users must request access to the UNI2-h weights from Mahmood Lab at https://huggingface.co/MahmoodLab/UNI2-h, upload the weights file to an accessible gs:// bucket, and provide that path as the uni_weights input.