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Peer-reviewedCC BYBioinformatics (Oxford, England)

STiLE: automated tissue microarray dearraying for spatial transcriptomics.

  • Harsh Sinha1
  • Arun Das2,3
  • Yu-Chiao Chiu3,4,5
  • Shou-Jiang Gao2,6
  • Yufei Huang1,2,3,7
7 affiliations
  1. 1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.
  2. 2Cancer Virology Program, UPMC Hillman Cancer Center, Pittsburgh, PA, United States.
  3. 3Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.
  4. 4Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.
  5. 5Cancer Therapeutics Program, UPMC Hillman Cancer Center, Pittsburgh, PA, United States.
  6. 6Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.
  7. 7Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States.
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Abstract

Summary

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, 10× 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.

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Cite

BibTeXRIS
BibTeX
@article{sinha2026stile,
  title = {{STiLE: automated tissue microarray dearraying for spatial transcriptomics.}},
  author = {Harsh Sinha and Arun Das and Yu-Chiao Chiu and Shou-Jiang Gao and Yufei Huang},
  journal = {Bioinformatics (Oxford, England)},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag572},
  url = {https://europepmc.org/article/MED/42836654},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - STiLE: automated tissue microarray dearraying for spatial transcriptomics.
AU  - Harsh Sinha
AU  - Arun Das
AU  - Yu-Chiao Chiu
AU  - Shou-Jiang Gao
AU  - Yufei Huang
PY  - 2026
DA  - 2026/10/01
JO  - Bioinformatics (Oxford, England)
DO  - 10.1093/bioinformatics/btag572
UR  - https://europepmc.org/article/MED/42836654
AB  - Summary  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, 10× 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.
SN  - 1367-4803
SN  - 1367-4811
ER  -