spatial-omics

Single-cell spatial omics analysis that makes you happy!

Interpretable, cell-resolved virtual tissue from incomplete spatial multi-omics: one model over H&E, Visium and PhenoCycler that works with whichever modalities exist and reports its uncertainty. Unaite Fellowship for AI in Biology, 2026-27.

Quantifying senescence, the easy way.

End-to-end analysis of spatial multi-omics data

Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc.)

A modular framework for multimodal quality control (QC) of imaging-based spatially resolved transcriptomics (SRT).

A modern Python library for converting Mass Spectrometry Imaging (MSI) data into standardized SpatialData/Zarr format, enabling seamless integration with spatial omics analysis workflows.

Scalable spatial omics in the browser for hundreds of samples at once | 10x, IMC, IF, H&E, CosMX, & more | https://rakaia.io/

GPU-accelerated Python pipeline for image-based spatial omics, built on Sopa and SpatialData

Histology Spatial Topology for Omics Profiling and Inter-section Alignment

Python toolkit for image-based spatial transcriptomics (CosMx, MERFISH, Xenium). Unified data object for expression, polygons, and FOV images, plus QC, clustering, point- and polygon-based spatial statistics, and polygon-native cell-cell communication using real membrane contact geometry.

A standalone, GUI for Mass Spectrometry Imaging (MSI) analysis, combining unsupervised tissue segmentation, spatial molecular profiling, and supervised classification.

Graph-based foundation model for spatial transcriptomics data. Zero-shot spatial domain inference, batch-effect correction, and many other features.

Snakemake workflow that integrates Xenium spatial transcriptomics with CODEX/PhenoCycler spatial proteomics on the same tissue micro-array slides: per-cell RNA and protein quantification, QC, totalVI-derived joint embedding and Leiden clustering for cell-type annotation. Runs on SLURM in an Apptainer container.

Preprocessing pipeline for DBiT-Omics

Interactive visualization of spatial omics data

A model-agnostic confidence and reproducibility layer for spatial-omics models: know when to trust a spatial score and when to abstain. pip-installable, tested, CI.

Annotation of imaging mass spectrometry data with explicit confidence levels

OMERO.web plugin for the Vitessce multimodal data viewer.

Do spatial GNNs help cell-type classification? A controlled study on IMC breast cancer showing a feature-only baseline beats GCN and GAT due to label homophily (0.63).