Bayesian Segmentation of Spatial Transcriptomics 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).

DeepSpot: Deep learning model for predicting spatial transcriptomics from H&E histopathology images. Supports spot-level (Visium) and single-cell (Xenium) resolution.

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

A Julia package for single cell and spatial data analysis

A bioinformatics best-practice processing and quality control pipeline for Xenium and Artera data

R-based Xenium Spatial Analysis Toolkit to assess gene expression gradients

Code companion to the publication "High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue"

Ecotypes of Triple-Negative Breast Cancer in Response to Chemotherapy

A Julia package for single cell and spatial data analysis

Identifying cell-type-specific spatially variable genes

Analysis workflow for 10x Genomics Xenium spatial transcriptomics data. Includes scripts to unpack raw Xenium outputs, convert them into AnnData objects, and perform downstream analyses.

Fast, exact-preserving doublet-mode backend for spacexr RCTD. Up to 15× faster on Xenium, identical calls.

Cell-GPS is the Python package and reference implementation for Cophenetic Spatial Topology Embedding (COSTE), a spatial topology analysis framework for spatial omics data.

Data-centric marker distillation for zero-shot cell-type and spatial annotation with LLMs.

Spatial transcriptomics analysis for Bangs et al., 2025

End-to-end pipeline for spinal-cord microscopy segmentation with Cellpose, tiling, and mask stitching.

Additional MultiQC report sections for 10x Genomics Xenium data.

A Flask web app for joint visualization of multimodal spatial transcriptomics data