SMART: reference-free deconvolution for spatial transcriptomics using marker-gene-assisted topic models
Bibliographic record
Abstract
Abstract Spatial transcriptomics (ST) offers valuable insights into gene expression patterns within the spatial context of tissue. However, most technologies do not have a single-cell resolution, masking the signal of the individual cell types. Here, we present SMART, a reference-free deconvolution method that simultaneously infers the cell type-specific gene expression profile and the cellular composition at each spot. Unlike most existing methods that rely on having a single-cell RNA-sequencing dataset as the reference, SMART only uses marker gene symbols as the prior knowledge to guide the deconvolution process and outperforms the existing methods in realistic settings when an ideal reference dataset is unavailable. SMART also provides a two-stage approach to enhance its performance on cell subtypes. Allowing the inclusion of covariates, SMART provides condition-specific estimates and enables the identification of cell type-specific differentially expressed genes across conditions, which elucidates biological changes at a single-cell-type resolution.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".