Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc)
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Updated
Oct 28, 2025 - Python
Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc)
Integrated pipeline for multiplexed image analysis
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
astir | Automated cell identity from single-cell multiplexed imaging and proteomics 🖥🔬✨
Probabilistic topic model for identifying cellular micro-environments.
An end-to-end processing pipeline that transforms multi-channel whole-slide images into single-cell data.
HistoJS: Web-Based Analytical Tool for Multiplexed Images. Limited Github Online Demo 👇
Spatial Multiomics Profiler for Spatial Characterization of Tissue Microenvironment (https://smprofiler.io)
A Python package for unsupervised analysis of spatial (prote)omics data.
🎇 Scale interactive spatial omics in the browser to thousands of images | https://rakaia.io/
Rapid and efficient image preprocessing pipeline for multiplexed spatial proteomics. Code to compare several techniques to denoise Imaging Mass cytometry
MIAAIM: Multi-omics Image Alignment and Analysis by Information Manifolds
Rapid and efficient image preprocessing pipeline for multiplexed spatial proteomics. Code to compare several techniques to denoise Imaging Mass cytometry
Rapid and efficient image preprocessing pipeline for multiplexed spatial proteomics. Code to compare several techniques to denoise Imaging Mass cytometry
A workflow designed to perform multiplexed image analysis, specialising in (but not limited to) analysis of metal distribution in LA-ICP-TOFMS data.
Rust library for reading imaging mass cytometry (IMC) data stored in .mcd files.
High-dimensional image preparation module for MIAAIM
Multi-omics image alignment and analysis by information manifolds (MIAAIM)
SpatialVisVR is a VR platform tailored for advanced visualization and analysis of medical images in immuno-oncology. It allows real-time capture and comparison of mIF and mIHC images via mobile devices. Leveraging deep learning, it matches and displays similar images, supporting up to 100 protein channels.
General importing and utility functions for high-dimensional image data
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