GSHAPA: Gene Set Analysis for Single-Cell RNAseq Using Random Forest and SHAP Values
Bibliographic record
Abstract
The increasing complexity and high dimensionality of single-cell RNA sequencing (scRNA-seq) data present significant challenges in extracting meaningful biological insights. To address these challenges, researchers often turn to gene set analysis methods, which aim to identify biological or functionally related sets of genes collectively associated with samples across various experimental conditions. Traditional gene set analysis methods, based on linear statistical approaches, often fail to capture complex relationships and do not provide sample-specific insights, limiting their effectiveness with scRNA-seq data. To address these limitations, we introduce GSHAPA, a novel method combining Random Forest with SHapley Additive exPlanations for gene set analysis. GSHAPA captures non-linear relationships, effectively handles high-dimensional sparse datasets, and enables sample-specific analysis. Evaluated on simulated and biomedical datasets, GSHAPA demonstrates superior precision, F1 score, and computational efficiency compared to the widely-used GSEA method, with reduced false positive rates. GSHAPA has broad potential for set-based feature analysis in high-dimensional data.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".