scGRIP: a graph-based explainable AI framework for single-cell multi-omics Gene Regulatory Inference with Prior Knowledge
Bibliographic record
Abstract
Abstract Multi-omics sequencing technologies can jointly measure transcriptome and chromatin accessibility at the single-cell resolution. This enables inference of gene regulatory networks (GRNs) at the cellular level, thereby elucidating high-resolution differential GRNs associated with diseases. However, existing methods lack interpretability and scalability. We present single-cell Gene Regulatory Inference with Prior knowledge (scGRIP), a graph variational autoencoder with 3 technical contributions. First, we treat transcription factors (TF), target genes (TG), and regulatory elements (RE) as nodes and their potential TF-RE and RE-TG interactions as edges using a prior cis-regulatory knowledge graph. Second, we tokenize the single-cell chromatin accessibility and gene expression with a shared codebook to compute cell-specific feature embedding. Third, we incorporate a GraphSHAP technique to infer GRN edge attribution at the single-cell level. scGRIP, evaluated against existing regulatory inference frameworks such as LINGER and scGLUE, demonstrates that integrating prior regulatory knowledge yields regulatory interaction features that improve discrimination of cell types and disease conditions. In particular, scGRIP identified coordinated activation of immune signaling, amyloid-related responses, and downstream target gene programs. The inferred GRNs for Alzheimer’s disease were supported by functional enrichment analyses and independent reference dataset.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".