UCS: a unified approach to cell segmentation for subcellular spatial transcriptomics

This repository contains the code for the UCS method, a unified approach to cell segmentation for spatially resolved transcriptomics. UCS is a deep learning-based method that can be used to segment cells in spatially resolved transcriptomics data. UCS can be used to segment cells in spatially resolved transcriptomics data from different platforms.

Installation

Run

To prepare the environment:

cd /path/to/ucs
conda create -n ucs python=3.9
conda activate ucs
pip install -r requirements.txt

Then you can run the UCS method on your data. See scrips dir for examples on how to run UCS on Xenium data and Vizgen data.

Downstream analysis

See downstream dir README for examples on how to run downstream analysis to reproduce most of the results in the paper.