# STransfer: a transfer learning-enhanced graph convolutional network for clustering spatial transcriptomics data

## TL;DR

STransfer improves clustering of spatial transcriptomics data by using transfer learning and graph networks to capture spatial patterns across tissue slices.

## Contribution

STransfer introduces a novel transfer learning framework combining GCNs and PPMI for enhanced spatial clustering in multi-slice datasets.

## Key findings

- STransfer outperforms existing methods in spatial modeling and cross-slice transfer performance.
- The attention-based module effectively fuses multi-graph features into unified node representations.
- Experiments show improved clustering accuracy with reduced manual annotation costs.

## Abstract

Capturing spatial structure is fundamental to the analysis of spatial transcriptomics data. However, most existing methods focus on clustering within individual tissue slices and often ignore the high inter-slice similarity inherent in multi-slice datasets.

To address this limitation, we propose STransfer, a novel transfer learning framework that combines graph convolutional networks (GCNs) with positive pointwise mutual information (PPMI) to model both local and global spatial dependencies. An attention-based module is introduced to fuse features from multiple graphs into unified node representations, facilitating the learning of low-dimensional embeddings that jointly encode gene expression and spatial context. By transferring knowledge from labeled slices to adjacent unlabeled ones, STransfer significantly enhances clustering accuracy while reducing manual annotation costs. Extensive experiments demonstrate that STransfer consistently outperforms state-of-the-art methods in both spatial modeling and cross-slice transfer performance.

The code for STransfer has been uploaded to GitHub: https://github.com/Saki-JSU/Publications/tree/main/STransfer.