SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

2026-07-26

AI Summary

This study addresses the instability and poor interpretability often encountered in identifying therapeutic target genes from single-cell RNA sequencing data, which stem from the sensitivity of analytical pipelines. To overcome these limitations, the authors propose SCTA, a novel framework that introduces a decision-aware multi-agent orchestration mechanism. SCTA decomposes target gene discovery into specialized agents aligned with critical analytical decisions and constrains their reasoning with structured biological evidence. By integrating differential expression analysis, cell subpopulation selection strategies, and multi-source biological knowledge, the method substantially enhances the stability and interpretability of target gene prioritization. Validation in hereditary chronic pancreatitis demonstrates that SCTA not only improves reproducibility but also successfully recapitulates known disease mechanisms.

Abstract

Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captures heterogeneous cellular states and rare subpopulations, but this same heterogeneity makes target discovery highly sensitive to analytical choices throughout the pipeline, including preprocessing, cell population selection, differential expression analysis, and downstream biological interpretation. As a result, existing workflows and general-purpose analysis agents often produce unstable or difficult-to-interpret target hypotheses, limiting their reliability for disease-focused discovery. We present SCTA (Single-Cell Target Agent), a decision-centric agentic framework for stable and interpretable target gene discovery from scRNA-seq data. Rather than treating analysis as a single general-purpose reasoning task, SCTA decomposes target discovery into specialized agents aligned with key decision points in the single-cell pipeline and constrains downstream reasoning with structured biological evidence. In a representative ablation study on hereditary chronic pancreatitis, we demonstrate that SCTA's full evidence integration yields the most stable target selection across independent runs among the tested configurations, while recovering biologically coherent, disease-relevant mechanisms validated in prior studies. These results suggest that decision-aware agent orchestration tailored to the structure of single-cell analysis can improve the robustness, interpretability, and practical utility of target discovery in precision medicine.

Problem

• target gene discovery
• single-cell RNA sequencing
• analytical stability
• biological interpretability
• translational biology

Innovation

• agentic framework
• single-cell RNA sequencing
• target gene discovery
• interpretable AI
• evidence integration