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

A Toolbox for Spatial Transcriptomics Analysis

BANKSY: spatial clustering

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

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

Histology Spatial Topology for Omics Profiling and Inter-section Alignment

SGS, is a user-friendly, collaborative and versatile browser for visualizing single-cell and spatial multiomics data.

a cutting-edge cell segmentation model specifically designed for single-molecule resolved spatial omics datasets. It addresses the challenge of accurately segmenting individual cells in complex imaging datasets, leveraging a unique approach based on graph neural networks (GNNs).

Interactive visualization of spatial omics data

BANKSY: Spatial Clustering Algorithm that Unifies Cell-Typing and Tissue Domain Segmentation. Python package for spatial transcriptomics analysis.

Single-cell spatial omics analysis that makes you happy!

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

a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution

Dependency-aware deep generative models for multitasking analysis of spatial genomics data

ClusterMap for multi-scale clustering analysis of spatial gene expression

Cell-type Relationship Analysis Workflow Done Across Distances

Spatial analysis toolkit for single cell multiplexed tissue data

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

Interoperability between SpatialData and the Xenium Explorer

DECIPHER for learning high-fidelity disentangled embeddings from spatial omics data