Additional file 1 of GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes
Bibliographic record
Abstract
Additional file 1. Supplementary Section S1 (Evaluation on gene selection strategies); S2 (Evaluation on raw count and transformation strategies); S3 (Experimenting hyperparameters $$\alpha_{\mathrm m,\mathrm k}$$ α m , k for cell-type mixture prior); S4 (Experimenting hyperparameter $$\beta$$ β for CTS topics); S5 (Experimenting number of topics per cell type); S6 (Benchmark time and memory usage); S7 (Effect of sparsification on phenotype classification); S8 (Phenotype-CTS topic modeling of single-cell breast cancer transcriptomes for TCGA-BRCA bulk deconvolution); S9 (Effect of cell size on inference of cell-type deconvolution); Table S1-S3; Figure S1-S31.
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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 | 0.000 | 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) | 0.188 | 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".