SpaIM

Single-cell Spatial Transcriptomics Imputation via Style Transfer [paper]

We introduce SpaIM, a novel style transfer learning model that leverages scRNA-seq data to accurately impute unmeasured gene expressions in spatial transcriptomics (ST) data. SpaIM separates scRNA-seq and ST data into data-agnostic contents and data-specific styles, capturing commonalities and unique differences, respectively. By integrating scRNA-seq and ST strengths, SpaIM addresses data sparsity and limited gene coverage, outperforming existing methods across 53 diverse ST datasets. It also enhances downstream analyses like ligand-receptor interaction detection, spatial domain characterization, and differentially expressed gene identification.

Getting Started

Environment

To get started with SpaIM, please follow the steps below to set up your environment:

git clone https://github.com/QSong-github/SpaIM
cd SpaIM
conda env create -f environment.yaml
conda activate SpaIM

Datasets

All datasets used in this study are publicly available.

• Data sources and details are provided in Supplemental_Table_1. After downloading the data, follow the processing flow in get_adata_cluster.py to analyse it for clustering.

• All processed datasets and example 'Dataset 1' can be downloaded at Zenodo and Synapse.

The datasets should be organized in the following structure:

|-- dataset
    |-- Dataset1
    |-- Dataset2
    |-- ......
    |-- Dataset52
    |-- Dataset53

SpaIM Training and Testing

Train all 53 datasets with a single command:

chmod +x ./*
./run_SpaIM.sh

The trained models and metric results will be saved in the following directories:

./SpaIM_results/Dataset1/

SpaIM Inference

Run the following command to perform inference:

python test_imputation.py

The inference results will will be saved in './SpaIM_results/Dataset1/impute_sc_result_%d.pkl'.

Reference

If you find this project is useful for your research, please cite:

@article{li2025spaim,
  title={SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer},
  author={Li, Bo and Tang, Ziyang and Budhkar, Aishwarya and Liu, Xiang and Zhang, Tonglin and Yang, Baijian and Su, Jing and Song, Qianqian},
  journal={bioRxiv},
  pages={2025--01},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}

Acknowledgments

Our code is based on the neural-style. Special thanks to the authors and contributors for their invaluable work.