# stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images

## TL;DR

stDyer-image is a deep learning tool that improves clustering of spatial transcriptomics and proteomics data by using morphological images, leading to better analysis of gene and protein patterns in tissues.

## Contribution

stDyer-image introduces a novel deep learning framework that directly links image features to cluster labels for spatial omics data.

## Key findings

- stDyer-image outperforms existing methods in clustering performance for spatial omics data.
- The framework is effective across various technologies and large-scale datasets.
- It mimics pathologists by using morphology images to identify cell types or tumor regions.

## Abstract

Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance.

Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis.

The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.