Niyakan, S.; Qian, X.

Spatial transcriptomics (ST) provides powerful insights into gene expression patterns within tissue structures, enabling the discovery of molecular mechanisms in complex tumor microenvironments (TMEs). Phenotype-based discovery in ST data holds transformative potential for linking spatial molecular expression patterns to clinical outcomes; however, appropriate ST data analysis remains fundamentally fragmented and highly labor-intensive. Due to its limited scalability considering the size of typical ST data in large cohorts, researchers must rely on other phenotype-annotated omics data modalities (e.g. bulk RNA-sequencing) and align them with ST data to extract clinically meaningful spatial patterns. Yet, this process requires manually identifying relevant cohorts, aligning multi-modal data, selecting and tuning analysis pipelines, and interpreting results, typically without any built-in support for biologically context-aware reasoning. In this paper, we present PhenoGraph, a large language model (LLM) based multi-agent system, that automates the full pipeline for phenotype-driven ST data analysis, augmented by biological knowledge graphs for enhanced interpretability. Built on a modular agent architecture, PhenoGraph dynamically selects, executes, and corrects phenotype analysis pipelines based on user-defined queries. We showcase the flexibility and effectiveness of PhenoGraph across a variety of TME ST datasets and phenotype classes, highlighting its potential to enhance biological discovery efficacy.