STCellbin

Introduction

STCellbin utilizes the cell nuclei staining image as a bridge to acquire cell membrane/wall staining image that align with spatial gene expression map. By employing advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profile. The enhanced capability of this updating provides valuable insights into the spatial organization of gene expression within cells and contributes to a deeper understanding of tissue biology.

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

Installation

STCellbin is developed by Python scripts. Please make sure Conda is installed before installation.

Download the project resource code and install requirements.txt in a python==3.8 environment.

# python3.8 in conda env
git clone https://github.com/STOmics/STCellbin.git
conda create --name=STCellbin python=3.8
conda activate STCellbin
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -c pytorch
cd STCellbin-main
pip install -r requirements.txt # install

• The pyvips package needs to be installed separately. The following is referenced from pyvips

On Windows, first you need to use pip to install like,

$ pip install --user pyvips==2.2.1

then you need to download the compiled library from vips-dev-8.12, To set PATH from within Python, you need something like this at the start:

import os
vipshome = 'c:\\vips-dev-8.7\\bin'
os.environ['PATH'] = vipshome + ';' + os.environ['PATH']

On Linux,

$ conda install --channel conda-forge pyvips==2.2.1

Tutorials

Test dataset

The demo datasets have been deposited into Spatial Transcript Omics DataBase (STOmics DB) of China National GeneBank DataBase (CNGBdb) with accession number STT0000048.

We also provide a backup link (PWD: JlI9) to share staining tiles and spatial gene expression data.

Command Line

STCellbin in one-stop is performed by command:

python STCellbin-main/STCellbin.py
-i /data/C01344C4,/data/C01344C4_Actin_IF
-m /data/C01344C4.gem.gz
-o /result
-c C01344C4

• -i Folder paths of cell nuclei staining image tiles and cell membrane/wall staining image tiles respectively.
• -m Compressed file of Stereo-seq spatial gene expression data.
• -o Output path.
• -c Chip number of Stereo-seq data.

License and Citation

STCellbin is released under the MIT license.

Please cite STCellbin in your publications if it helps your research:

B. Zhang et al. Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images. Preprint in bioRxiv. 2023.

Reference

M. Li et al. StereoCell enables highly accurate single-cell segmentation for spatial transcriptomics. Preprint in bioRxiv. 2023.

https://github.com/matejak/imreg_dft
https://github.com/rezazad68/BCDU-Net
https://github.com/libvips/pyvips