While the expansion of spatial omics has revolutionized our ability to dissect tissue architecture, the accumulation of incompatible computational methods has heavily fragmented end-to-end analysis, rendering complex workflows irreproducible. Generic conversational agents lack the domain-specific precision necessary to navigate the intricate biological pipelines. To overcome this, we present SpatialClaw, a memory-augmented autonomous ecosystem to unify spatial omics analysis under a single natural-language interaction. SpatialClaw integrates 30 specialized skills, spanning raw data preprocessing, spatial domain identification, deconvolution, spatially variable gene detection, cell-cell communication analysis, multi-sample and cross-modality integration. Distinct from existing agents, SpatialClaw introduces a graph-based persistent memory architecture that stores dataset metadata, analysis lineage, biological insights, and user preferences as versioned nodes and edges across three hierarchical layers (Session, Episodic, and Semantic), governed by a deterministic promotion policy. A Memory-Augmented Reasoning (MAR) Operator bridges the memory store and the main agent, synthesizing retrieved experiences into task-specific guidance for each query. In rigorous benchmarking spanning three memory-sensitive scenarios across 10 spatial-omics skills, SpatialClaw outperforms both a standard large language model and the memory-only configuration. Furthermore, we demonstrate its robust biological utility by dissecting the complex tumor microenvironment of a 15-section human triple-negative breast cancer cohort. In merely three conversational turns and with zero direct scripting, SpatialClaw executes a comprehensive end-to-end workflow, yielding standardized output bundles. Ultimately, by synergizing comprehensive analytical tools with structured persistent memory, SpatialClaw elevates spatial omics from disjointed computational stitching to a fully traceable, reproducible, and self-improving discovery ecosystem.
Du, G., Lan, O., Wei, X., Wu, Y., Meng, G., Wu, J., Li, Z., Li, X., Shang, X.
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