COMPASS: Comet Object Measurement Pipeline with Automated Selection and Scoring
Organizations: Department of Computer Science, Brown University, Providence, RI, USA · Department of Computer Science, Bryn Mawr College, Bryn Mawr, PA, USA · Department of Biology, University of Maryland, College Park, MD, USA · Department of Integrative Biology, W. K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI, USA ·
Abstract
Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However, image analysis often relies on manual inspection or semi-automated software, which can be labor-intensive, difficult to reproduce, and sensitive to image quality and comet morphology. COMPASS automates comet assay image analysis by combining deep learning-based comet segmentation with damage measurement and automated comet selection. The pipeline produces standardized DNA damage measurements while offering robust detection, reducing manual effort and improving reproducibility through transparent selection and optional manual review. Availability and implementation: COMPASS is implemented in Python and is freely available at https://github.com/ rsinghlab/COMPASS. Installation instructions, pretrained weights, and example usage are provided in the repository. Contact: [email protected], [email protected] Supplementary information: Available onlime upon publication.
Figures & tables
| Metric | OpenComet | COMPASS |
|---|---|---|
| Detection TP/FP/FN | 128/130/27 | 146/6/14 |
| Detection precision | 0.636 | 0.964 |
| Detection recall | 0.845 | 0.899 |
| Selection TP/FP/FN | – | 71/2/14 |
| Selection precision | – | 0.986 |
| Selection recall | – | 0.858 |
Microscopy Image Analysis
Microscopy image analysis uses computational methods to detect, segment, track, reconstruct, and quantify structures in scientific images.
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