Decoding Spatial Transcriptomics at Any Resolution: From Multicellular or Subcellular Spots to Individual Cells

We presented STARS (Spatial Transcriptomics across Any Resolution for Single Cells). Leveraging Vision Transformer model and contrastive learning, STARS combines high-resolution histology images with spot-level transcriptomics data to decode true single-cell gene expression from any multicellular or subcellular platforms. We demonstrated the advantage of our true single-cell method using public datasets and in-house datasets of mouse lung from 3 ST platforms (Visium, Visium HD and Stereo-seq). STARS was applied at tissue, individual cell, and molecular levels.

Framework

The code is licensed under the MIT license.

1. Requirements

1.1 Operating systems:

The code in python has been tested on Linux (Ubuntu 20.04.1 LTS).

1.2 Required packages in python:

anndata
numpy
opencv-python
pandas
python-louvain
rpy2
scanpy
scipy
seaborn
torch
torch-geometric
torchvision
tqdm
umap-learn

1.3 How to install STARS:

(1) cd STARS

(2) conda create --name STARS

(3) conda activate STARS  

STARS can be installed via pip using the following command:

pip install stars-omics

After installation, you can import the package in Python as:

import stars_omics

2. Instructions: Demo on mouse lung data.
We provide an example notebook, visium_06.ipynb, to implement the experimental results from the paper.