UniFORM: A New Method for Normalizing Tissue Imaging Data
Author Information
Author(s): Wang Kunlun, Ait-Ahmad Kaoutar, Kupp Samuel D, Sims Zachary, Cramer Eric, Sayar Zeynep, Yu Jessica, Wong Melissa H., Mills Gordon B., Eksi S. Ece, Chang Young Hwan
Primary Institution: Cold Spring Harbor Laboratory
Hypothesis
UniFORM can effectively normalize multiplex tissue imaging data to improve analysis accuracy.
Conclusion
UniFORM outperforms existing normalization methods, leading to better alignment of marker distributions and enhanced analysis results.
Supporting Evidence
- UniFORM preserves marker intensity distribution shapes while maintaining positive population proportions.
- Benchmarking shows that UniFORM mitigates batch effects better than existing methods.
- UniFORM enhances downstream analyses such as UMAP visualizations and Leiden clustering.
Takeaway
UniFORM is a tool that helps scientists make sense of complex images of tissues by making sure the colors and signals are consistent, so they can understand the data better.
Methodology
UniFORM is a non-parametric, Python-based pipeline that normalizes feature- and pixel-level MTI data without prior assumptions.
Digital Object Identifier (DOI)
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