Developing A Graph-based Deep Learning Method to Predict Cell-Cell Communication in Single-cell RNAseq Data
Bibliographic record
Abstract
Cell-cell interactions coordinate various functions across cell-types in health and disease. Novel single-cell techniques allow us to investigate cellular crosstalk at single-cell resolution. Cell-cell interactions are mediated by underlying gene-gene networks, however most current methods are unable to account for complex inter-connections within the cell as well as incorporate the effect of pathway and protein complexes on interactions. Therefore, to represent transcriptomic data as cell-cell networks connecting cells to ligands and receptors for relevant cell-cell communication inference as well as incorporating descriptive information independent of gene expression, we present GraphComm - a new graph-based deep learning method for predicting cell-cell communication in single-cell RNAseq datasets. By learning off of a prior model and fine-tuning a network on single-cell transcriptomic data, GraphComm is able to predict cell-cell communication (CCC) activity across cells, and its impact on downstream pathways, spatially adjacent cells and changes due to drug perturbations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".