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SpatialDataAgent: Autonomous Spatial Omics Data Curation at Decade Scale

Ji, J.-H.; Zou, Q.; Cheng, J.; She, Z.; Hao, Y.; Liu, W.; Zhang, D.; Wang, Z.; Yu, J.-T.; Yuan, Z.

2026-05-30 bioinformatics
10.64898/2026.05.27.727615 bioRxiv
Show abstract

Fragmented metadata in spatial omics archives has rendered large volumes of multimodal molecular-histological data inaccessible as dark data. Here, we introduce SpatialDataAgent, an agentic workflow for autonomous spatial omics data curation, combining schema-constrained evidence evaluation with a self-refining standardization agent. Applied to a decade of GEO records, SpatialDataAgent identified 769 paired H&E-spatial transcriptomics (ST) datasets, representing a 6.4-fold scale expansion over existing manually curated baselines. Within the benchmarking window, the framework achieved a 141% increase in high-confidence Class A paired datasets. We further assembled a recent high-confidence subset into HESRT, a standardized datalake containing 29.2 million spots/cells, establishing a blueprint for evidence-grounded autonomous curation of multimodal biomedical archives.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.