Inferring tumour microenvironment ecosystems from scRNA-seq atlases
Bibliographic record
Abstract
Accurate inference of granular cell states that co-occur within the tumour microenvironment (TME) is central to defining pro– and anti-tumour environments. Here, we describe how ecosystems of cell populations can be robustly inferred from nonspatial single-cell RNA-seq (scRNA-seq) atlases. Leveraging a unique discovery-validation setup across eight scRNA-seq datasets profiling pancreatic ductal adenocarcinoma (PDAC), we show highly consistent co-occurrence of fine-grained cell states across patients and characterize the positive predictive value of such analyses. Building on this, we develop a novel probabilistic model to quantify multi-cellular ecosystems directly from such atlas-scale scRNA-seq datasets. By mapping these ecosystems to spatial transcriptomics data, we demonstrate that such ecosystems represent bona fide spatial variation within individual tumours despite being learned from nonspatial data. Importantly, through mapping these ecosystems across two large clinical cohorts, we show they are more predictive of therapy response than any individual cell state and are associated with specific tumour somatic mutations. Together, this work lays the foundation for inferring reproducible multicellular ecosystems directly from large nonspatial scRNA-seq atlases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".