Authors
Harsh Sinha, Arun Das, Yu-Chiao Chiu, Shou-Jiang Gao, Yufei Huang
Published in
Bioinformatics (Oxford, England). Oct 06, 2026. Epub Oct 06, 2026.
Abstract
Tissue microarrays (TMAs) enable high-throughput spatial transcriptomic profiling of dozens of tissue cores on a single slide. However, existing dearraying methods operate on histological images and do not support the coordinate-based outputs of spatial transcriptomics platforms. Therefore, task of assigning cells to their respective cores (dearraying) remains a manual bottleneck. We present STiLE, a tool for automated TMA dearraying that operates solely on cell centroid coordinates. By eliminating dependence on image data, STiLE is robust to artefacts such as variable staining quality and uneven illumination. The algorithm combines connectivity-based component detection, density-based clustering (HDBSCAN), component-guided cluster merging, and optional grid-based peak detection. Validation on seven public TMA samples (50-150 cores, three platforms) achieved ARI > 0.99, while systematic benchmarking on 396 synthetic datasets with realistic artefacts demonstrated consistently robust performance (mean ARI = 0.992). STiLE accepts standard formats (AnnData, CSV) and is platform-agnostic, supporting diverse platforms including Vizgen MERSCOPE, 10x Xenium, and NanoString CosMx. An interactive Streamlit interface enables parameter tuning, visual inspection, and region-based processing for large slides. Availability and Implementation: https://pypi.org/project/stile; source at https://github.com/Huang-AI4Medicine-Lab/stile Supplementary Information: Supplementary data are available at Bioinformatics online.
PMID:
42836654
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.
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