MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spatial transcriptomics (ST) technology provides gene expression profiles with spatial context, offering critical insights into cellular interactions and tissue architecture. A core task in ST is spatial domain identification, which involves detecting coherent regions with similar spatial expression patterns. However, existing methods often fail to fully exploit spatial information, leading to limited representational capacity and suboptimal clustering accuracy. Here, we introduce MAEST, a novel graph neural network model designed to address these limitations in ST data. MAEST leverages graph masked autoencoders to denoise and refine representations while incorporating graph contrastive learning to prevent feature collapse and enhance model robustness. By integrating one-hop and multi-hop representations, MAEST effectively captures both local and global spatial relationships, improving clustering precision. Extensive experiments across diverse datasets, including the human brain, mouse hippocampus, olfactory bulb, brain, and embryo, demonstrate that MAEST outperforms seven state-of-the-art methods in spatial domain identification. Furthermore, MAEST showcases its ability to integrate multi-slice data, identifying joint domains across horizontal tissue sections with high accuracy. These results highlight MAEST's versatility and effectiveness in unraveling the spatial organization of complex tissues. The source code of MAEST can be obtained at https://github.com/clearlove2333/MAEST.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it