SpaTM: topic models for inferring spatially informed transcriptional programs
Bibliographic record
Abstract
Spatial transcriptomics enables the contextualization of gene expression with spatial organization, advancing our understanding of development, disease, and tissue architecture. However, existing analysis pipelines require multiple tools to explore spatial domains, and few methods can jointly analyse spatial data from annotation-free and annotation-guided perspectives with high interpretability. We therefore propose the Spatial Topic Model (SpaTM), a topic-modelling framework capable of annotation-guided and annotation-free analysis of spatial transcriptomes. SpaTM can learn gene programs that represent histology-based annotations while also inferring spatial domains with an annotation-free approach if manual annotations are limited or noisy. In benchmarking experiments, SpaTM achieves competitive performance at spatial label prediction and clustering when compared with existing state-of-the-art methods. We demonstrate SpaTM's interpretability by using topic mixtures to capture transcriptional programs in dorsolateral prefrontal cortex and ductal carcinoma samples and show how its intuitive framework facilitates the integration of spatial transcriptomics tasks. Finally, we showcase how SpaTM can extend the analysis of large-scale snRNA-seq atlases in human brains with Major Depressive Disorder. Overall, SpaTM provides a unified and interpretable analysis framework for spatial transcriptomics, enabling competitive performance in multiple tasks while inferring biologically informed gene programs.
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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 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".