CellNEST reveals cell–cell relay networks using attention mechanisms on spatial transcriptomics
Why this work is in the frame
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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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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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 it