🗺️ Best AI tools for Spatial omics (2026)
Tools for spatial transcriptomics and proteomics: deconvolution, domain detection, alignment, histology-to-expression prediction and spatial data frameworks.
13 tools · 13 open source · each entry links to its website, code and paper.
Language-conditioned model that predicts cell-level spatial gene expression from histology across tissues and gene panels.
Foundation model pretrained on dissociated and spatial single-cell data.
Multi-omics single-cell optimal transport for space and time mapping.
Unified data framework for spatial omics technologies.
Graph self-supervised contrastive learning for spatial transcriptomics.
Spatial single-cell analysis toolkit in the scverse ecosystem.
Graph attention autoencoder for spatial domain identification.
Optimal-transport alignment and integration of spatial transcriptomics slices.
Bayesian mapping of fine-grained cell types in spatial transcriptomics.
Bayesian clustering and resolution enhancement of spatial transcriptomics.
Maps single-cell profiles onto spatial data with deep learning.
Robust cell type decomposition for spatial transcriptomics.
Comprehensive R toolbox for spatial data analysis and visualization.