scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics
Bibliographic record
Abstract
Abstract Spatial transcriptomics has emerged as a pivotal technology for profiling gene expression of cells within their spatial context. The rapid growth of publicly available spatial data presents an opportunity to further our understanding of microenvironments that drive cell fate decisions and disease progression. However, existing foundation models, largely pretrained on single-cell RNA sequencing (scRNA-seq) data, fail to resolve the spatial relationships among samples or capture the unique distributions from various sequencing protocols. We introduce scGPT-spatial , a specialized foundation model for spatial transcriptomics continually pretrained on our previously published scGPT scRNA-seq foundation model. We also curate SpatialHuman30M, a comprehensive spatial transcriptomics dataset comprising of 30 million spatial transcriptomic profiles, encompassing both imaging- and sequencing-based protocols. To facilitate integration, scGPT-spatial introduces a novel MoE (Mixture of Experts) decoder that adaptively routes samples for protocol-aware decoding of gene expression profiles. Moreover, scGPT-spatial employs a spatially-aware sampling strategy and a novel neighborhood-based training objective to better capture spatial co-localization patterns among cell states within tissue. Empirical evaluations demonstrate that scGPT-spatial robustly integrates spatial data in mulit-slide and multi-modal settings, and effectively supports cell-type deconvolution and contextualized missing gene expression imputation, outperforming many existing methods. The scGPT-spatial codebase is publicly available at https://github.com/bowang-lab/scGPT-spatial .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".