Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging.

Highly multiplexed single-cell imaging technologies have revolutionized our ability to capture spatial protein expression at the single-cell level, thereby enabling a deeper understanding of tissue organization and function. However, these advancements rely on accurate cell segmentation, which defines cell boundaries to generate expression profiles. Despite its importance, there is a gap in quantifying how segmentation inaccuracies propagate through analytical pipelines, particularly affecting cell clustering and phenotyping. We introduce a framework that uses affine transformations to simulate realistic segmentation errors. Our approach mimics the variations induced by segmentation algorithms, allowing us to evaluate the robustness of downstream analyses under controlled perturbation conditions. We show that even moderate segmentation errors can significantly distort estimated protein profiles and disrupt cellular neighborhood relationships in feature space. Effects are most pronounced in clustering analyses, where both unsupervised k-Means and graph-based Leiden algorithms exhibit reduced consistency with increasing perturbation - especially with smaller neighborhood sizes. Similarly, cell phenotyping via Gaussian Mixture Models is adversely impacted, with higher levels of segmentation error leading to notable misclassifications between closely related cell types. These results highlight the importance of ensuring high-quality segmentation and careful data processing strategies to mitigate spurious results for downstream analysis tasks. Considering segmentation inaccuracies, possibly in a probabilistic modeling framework, will improve the reliability and reproducibility of findings in multiplexed tissue imaging studies.