DeepSpot: Deep learning model for predicting spatial transcriptomics from H&E histopathology images. Supports spot-level (Visium) and single-cell (Xenium) resolution.
Interactive spatial omics viewer in the browser for thousands of images | https://rakaia.io/
HistoJS: Web-Based Analytical Tool for Multiplexed Images. Limited Github Online Demo
AESTETIK: Convolutional autoencoder for learning spot representations from spatial transcriptomics and morphology data
Official repository for Characterization of tumor heterogeneity through segmentation-free representation learning on multiplexed imaging data
k-NN-based mapping of cells across representations to transfer labels, embeddings, and expression values.
DeepSpot2Cell: Predicting virtual single-cell spatial transcriptomics from H&E images using spot-level supervision
Browser-based OME-ZARR microscopy viewer. Open local files, annotate, overlay segmentation labels, share deep links. GPU-accelerated, privacy-first, zero-install.
Code associated with the manuscript "A Single-Cell Bioprinting Approach with Subcellular Resolution to Reconstruct Native Cellular Microenvironments and Interrogate Spatial Biology"
Interpretable Graph Neural Networks for Spatial Cell Biology. Learn complex, biologically meaningful cell-cell interaction functions with unprecedented interpretability.
Spatial transcriptomics workflows for 10x Xenium data. Developed and maintained while at the Allen Institute as part of the analysis for Zhang et al., Nature, 2026.
Modeling the Tumor Microenvironment (TME) as a many-body gravitational system. A synthetic data engine that applies astrophysics principles (N-Body dynamics) to simulate immune-tumor topology and generate ground-truth spatial data.
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