stSCI

Overview

Spatial transcriptomics (ST) preserves spatial context in gene expression analysis yet faces limitations like low resolution and RNA capture inefficiency. To address these, we present stSCI, a computational method integrating single-cell (SC) and ST data into a unified, batch-corrected embedding space. stSCI employs a fusion module with three specialized optimization tasks to generate biologically preserved joint latent representations, enabling five key analyses: spatial domain identification in single/multi-slice ST data; ST deconvolution predicting cell type proportions in low-resolution spots; SC spatial coordinate reconstruction using ST references; and cross-modality batch correction. Evaluated on 12 ST datasets spanning sequencing- and imaging-based platforms, and benchmarked against 22 state-of-the-art methods, stSCI improves spatial domain identification, maps cell type proportions in ST data, accurately reconstructs tissue architecture and regional structures, and integrates SC/ST datasets by removing batch effects without compromising biological signals. stSCI's robustness and versatility make it a powerful tool for uncovering tissue organization and molecular functions.

Software dependencies

Details

python==3.10.13

numpy==1.26.4

pandas==2.2.2

matplotlib==3.8.2

scanpy==1.10.1

squidpy==1.4.1

faiss==1.7.4

igraph==0.11.5

scikit-learn==1.5.0

scikit-misc==0.3.1

louvain==0.8.2

scipy==1.12.0

tqdm==4.66.1

pytorch==2.3.1+cu121

torch_geometric==2.5.0

rpy2==3.2.2

R==3.6.1

mclust==6.1.1

Setup stSCI

Setup by Docker (Recommended):

1. Download the stSCI image from DockerHub and setup a container:

docker run --gpus all --name your_container_name -idt hannshu/stsci:latest

2. Access the container:

docker start your_container_name
docker exec -it your_container_name /bin/bash

3. Write a python script to run stSCI

The anaconda environment for stSCI will be automatically activate in the container. The stSCI source code is located at /root/stSCI, please run git pull to update the codes before you use. All dependencies of stSCI have been properly installed in this container, including the mclust R package, and the conda environment stSCI will automatically activate when you run the container.

• Note: Please make sure NVIDIA Container Toolkit is properly installed on your host device. (Or follow this instruction to setup NVIDIA Container Toolkit first)

Manually setup:

We suggest you to use the Docker to setup and run stSCI. If you want to manually setup stSCI, we recommend you to use Anaconda to build the runtime environment.

1. Clone this repository from Github:

git clone https://github.com/hannshu/stSCI.git

2. Download dataset repository:

git submodule init
git submodule update

3. Build the Anaconda environment, the package version of the essential dependencies noted at Software dependencies section. Or use the environment.yml to directly build the conda environment.

conda env create -f environment.yml

4. Write a python script to run stSCI

Tutorial

Read the Documentation for detailed tutorials.
The scripts used to execute the baseline methods in this study are publicly available at stsci_comparison.