Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Bibliographic record
Abstract
Single-cell spatial transcriptomics can provide subcellular resolution for a deep understanding of molecular mechanisms. However, accurate segmentation and annotation remain a major challenge that limits downstream analysis. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn spatial colocalization patterns. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. To evaluate performance, we benchmark Bering with state-of-the-art methods and observe better cell segmentation accuracies and more detected transcripts across technologies and tissues. To streamline segmentation processes, we construct expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation. These improved capabilities enable Bering to enhance cell annotations for the rapidly expanding field of spatial omics. Cell segmentation remains a great challenge in high-resolution spatial-omics data. Here, the authors introduce a graph-based deep learning model that exploits transcript colocalization patterns to jointly perform noise-aware cell segmentation and annotation in spatial transcriptomics data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".