SpaIM : Single-cell Spatial Transcriptomics Imputation via Style Transfer

To accurately impute unmeasured gene expressions in spatial transcriptomics (ST) data, we introduce SpaIM, a novel style transfer learning model leveraging scRNA-seq (SC) data. SpaIM segregates scRNA-seq and ST data into data-agnostic contents and data-specific styles, with the contents capture the commonalities between the two data types, while the styles highlight their unique differences. By integrating the strengths of scRNA-seq and ST, SpaIM overcomes data sparsity and limited gene coverage issues, making significant advancements over existing methods. This improvement is demonstrated across 53 diverse ST datasets, spanning sequencing- and imaging-based spatial technologies in various tissue types. Additionally, SpaIM enhances downstream analyses, including the detection of ligand-receptor interactions, spatial domain characterization, and identification of differentially expressed genes.

Getting Started

Environment

Please run the following command to install.

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 the data was downloaded, follow the processing flow in get_adata_cluster.py to analyse it for clustering.

• Example processed datasets 1 and 2 can be downloaded at synapse.

The datasets structure should be as follows:

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

SpaIM Training and Testing

Train all 53 datasets with one command

chmod +x ./*
./run_SpaIM.sh

The trained models and metric results are available in the defined folders:

./SpaIM_results  # for benchmark datasets

Acknowledgments

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