STAnalyzer: Transparent Spatial Transcriptomics Analysis via an Agentic Architecture

Abstract

Spatial transcriptomics enables high-resolution profiling of gene expression within spatial contexts, yet its potential is often hindered by fragmented toolchains, intricate parameters, and cognitive bottlenecks of interpreting high-dimensional data. While recent Large Language Model agents have attempted to automate this process, they remain constrained by rigid execution logic, lack multimodal feedback for self-correction, and operate in epistemic isolation from established biological knowledge. Here, we present STAnalyzer, an intelligent multi-agent framework designed to automate the end-to-end analytical lifecycle--from raw data processing to biological hypothesis generation. Transcending traditional pipelines, STAnalyzer employs a collaborative intelligence architecture to achieve three core capabilities: (1) Intent-Driven Orchestration, which dynamically translates natural language queries into rigorous bioinformatics workflows; (2) Multi-Modal Self-Refinement, which autonomously ensures analytical robustness through closed-loop synthesis of evidence from visual patterns and statistical metrics; and (3) Evidence-based Cross-Validation, which bridges the gap between data-driven correlations and biological causation by anchoring findings in ground-truth literature and structured databases. By eliminating manual analytical bottlenecks and ensuring rigorous evidentiary traceability and transparency, STAnalyzer makes high-resolution spatial omics more accessible to a broader research community. It provides a robust and scalable framework for cross-platform automated analysis and accelerated biological discovery, translating massive spatial datasets into verifiable biological insights.