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.
Show 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Advances in Gene Ontology Utilization Improve Statistical Power of Annotation Enrichment 92%
- Caveolae and scaffold detection from single molecule localization microscopy data using deep learning 91%
- ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells 91%
Similar papers in this journal
- Identification of Monotonically Classifying Pairs of Genes for Ordinal Disease Outcomes 93%
- ScaleSC: A superfast and scalable single cell RNA-seq data analysis pipeline powered by GPU. 91%
- The impact of similarity metrics on cell type clustering in highly multiplexed in situ imaging cytometry data 91%
Similar papers in this journal
- Extraction of biological terms using large language models enhances the usability of metadata in the BioSample database 92%
- Stardust: improving spatial transcriptomics data analysis through space aware modularity optimization based clustering. 92%
- Scalable Analysis of Multi-Modal Biomedical Data 92%
Similar papers in this journal
"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.