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bioRxiv · 10.64898/2026.08.10.743848

Autonomous Spatial Transcriptomics Analysis (ASTA): Demonstrating Performance Improvements through Clustering, Biological Annotation, and AI-Driven Discovery

Zhang, M.·Roe, M.·Pollett, C.·Andreopoulos, W. B.
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Abstract

Spatial transcriptomics keeps measurement of gene expression while preserving spatial context, yet traditional analysis methods face challenges in computational efficiency, biological interpretability, and autonomous discovery. This project presents a framework solving these issues through three parts: (1) an ensemble clustering system achieving 66.7% improvement over baseline average and 23.9% over best single method with silhouette score of 0.540 and statistical significance (p = 0.0032, Cohens d = 1.82); (2) a knowledge-based clustering framework that annotates 88.6% of cells across 8 ovarian cell types using 428 marker genes; and (3) a GPT-4o-mini-powered autonomous agent that generated 3 biological hypotheses with validations.

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Zhang, M., Roe, M., Pollett, C., Andreopoulos, W. B.. 2026-08-18. Autonomous Spatial Transcriptomics Analysis (ASTA): Demonstrating Performance Improvements through Clustering, Biological Annotation, and AI-Driven Discovery. https://doi.org/10.64898/2026.08.10.743848

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