Authors
Dip, S. A., Zhang, L.
Abstract
Spatial transcriptomics (ST) links gene expression to tissue organization, yet automated annotation of spatial regions remains a persistent challenge. Recent studies have explored large language models (LLMs) for biological reasoning, but their applicability in low-compute, free-tier settings is largely unexplored. We investigate whether lightweight LLM agents can improve ST annotation by integrating rule-based heuristics, prototype discovery, and multi-role reasoning (Analyst, Consensus, Reviewer) within a unified agentic framework. Across six STARmap and MERFISH datasets, we benchmark single- and multi-agent variants using standard clustering and spatial coherence metrics (NMI, ARI, CHAOS, ASW). Our results show that small open-weight models such as llama3.2 and qwen3 match or slightly exceed deterministic baselines in cluster recovery, while producing more spatially consistent and interpretable predictions. These findings highlight the potential of modular LLM agents as resource-efficient components in future spatial omics annotation pipelines.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 11 Nov 2025.
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