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Poster
in
Workshop: 2nd Generative AI for Biology Workshop

PhenoGraph: A Multi-Agent Framework for Phenotype-driven Discovery in Spatial Transcriptomics Data Augmented with Knowledge Graphs

Seyednami Niyakan · Xiaoning Qian

Keywords: [ Knowledge Graph ] [ LLM ] [ AI Agents ] [ Phenotype Discovery ] [ Spatial Transcriptomics ] [ TCGA ]


Abstract:

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 \textit{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.

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