CellNEST reveals cell–cell relay networks using attention mechanisms on spatial transcriptomics
Bibliographic record
Abstract
Dysregulation of communication between cells mediates complex diseases such as cancer and diabetes; however, detecting cell–cell communication at scale remains one of the greatest challenges in transcriptomics. Most current single-cell RNA sequencing and spatial transcriptomics computational approaches exhibit high false-positive rates, do not detect signals between individual cells and only identify single ligand–receptor communication. To overcome these challenges, we developed Cell Neural Networks on Spatial Transcriptomics (CellNEST) to decipher patterns of communication. Our model introduces a new type of relay-network communication detection that identifies putative ligand–receptor–ligand–receptor communication. CellNEST detects T cell homing signals in human lymph nodes, identifies aggressive cancer communication in lung adenocarcinoma and colorectal cancer, and predicts new patterns of communication that may act as relay networks in pancreatic cancer. Along with CellNEST, we provide a web-based, interactive visualization method to explore in situ communication. CellNEST is available at https://github.com/schwartzlab-methods/CellNEST . Cell Neural Networks on Spatial Transcriptomics (CellNEST) deciphers patterns of communication between cells in spatially resolved transcriptomics data and can detect both signals between individual cells and relay networks of communication.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".