# Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics Data

arXiv: https://arxiv.org/abs/2408.06377 · HTML: https://mlsanity.com/paper/2408.06377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [GraphMAE: Self-Supervised Masked Graph Autoencoders](https://mlsanity.com/paper/2205.10803/) (2022)
- [Augmentation-Free Self-Supervised Learning on Graphs](https://mlsanity.com/paper/2112.02472/) (2021)
- [Multimodal contrastive learning for spatial gene expression prediction using histology images](https://mlsanity.com/paper/2407.08216/) (2024)
- [RARE: Robust Masked Graph Autoencoder](https://mlsanity.com/paper/2304.01507/) (2023)
- [stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation](https://mlsanity.com/paper/2403.10863/) (2024)
- [SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq](https://mlsanity.com/paper/2407.13182/) (2024)

## Built on by (most cited first, 2 of 2)

- [Balanced Multi-view Clustering](https://mlsanity.com/paper/2501.02564/) (2025)
- [Mask-informed Deep Contrastive Incomplete Multi-view Clustering](https://mlsanity.com/paper/2502.02234/) (2025)

## Abstract (arXiv)

With the rapid advancement of Spatial Resolved Transcriptomics (SRT) technology, it is now possible to comprehensively measure gene transcription while preserving the spatial context of tissues. Spatial domain identification and gene denoising are key objectives in SRT data analysis. We propose a Contrastively Augmented Masked Graph Autoencoder (STMGAC) to learn low-dimensional latent representations for domain identification. In the latent space, persistent signals for representations are obtained through self-distillation to guide self-supervised matching. At the same time, positive and negative anchor pairs are constructed using triplet learning to augment the discriminative ability. We evaluated the performance of STMGAC on five datasets, achieving results superior to those of existing baseline methods. All code and public datasets used in this paper are available at https://github.com/wenwenmin/STMGAC and https://zenodo.org/records/13253801.