Additional file 1 of Spatial analysis of stromal signatures identifies invasive front carcinoma-associated fibroblasts as suppressors of anti-tumor immune response in esophageal cancer
Bibliographic record
Abstract
Additional file 1: Fig. S1. Representative images showing the scoring process by the automated quantitative pathology imaging system. Fig. S2. Violin plots displaying the expression level of representative markers in each cell cluster. Fig. S3. Kaplan-Meier survival curves for total α-SMA+ CAFs, lamina propria α-SMA+ CAFs and stromal α-SMA+ CAFs in the generation (n=103) and validation (n=99) dataset of patients with ESCC. Fig. S4. The number of intratumoral macrophages correlates with clinical outcome in ESCC patients. Fig. S5. The density of CD68+ and CD163+ MØs correlates with clinical outcome in patients with ESCC. Fig. S6. Crucial cell-to-cell interaction pathways among the distinct cell populations predicted by CellChat. Fig. S7. Cell-to-cell communication among the CAFs and other cell types. Fig. S8. Differentially-expressed gene (DEG) enrichment analysis for α-SMA+ CAFs. Supplementary Table S1. The clinicopathological parameters of 11 patients profiled by scRNA-seq. Supplementary Table S2. Metal-conjugated antibodies and element-containing reagents used for IMC. Supplementary Table S3. Clinicopathological characteristics in the generation and validation dataset of patients with ESCC. Supplementary Table S4. Correlation between markers and clinicopathological characteristics in the generation and validation datasets. Supplementary Table S5. Differential expressed genes between α-SMA+ CAFs and α-SMA- CAFs. Supplementary Table S6. Univariate and multivariate analyses of factors associated with overall survival (OS) and disease-free survival (DFS) in the generation and validation datasets of patients with ESCC.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.798 | 0.125 |
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".