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bioRxiv · 10.64898/2026.07.02.736093

HERO: A hierarchy-aware analysis pipeline for reducing and refining whole-brain atlas-mapped cellular datasets

Shipman, A. L.·Centanni, S. W.
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Abstract

Advances in high-throughput mesoscale microscopy and machine learning-based image analysis pipelines have made unbiased whole-brain imaging widely accessible. However, translating the resulting atlas-mapped datasets into biologically meaningful results remains a substantial barrier owing to their sheer magnitude and complex hierarchical organization. Consequently, reporting structure and analysis methods vary widely across studies, undermining rigor and reproducibility. To address this, we developed a user-friendly data reduction workflow, HERO (Hierarchy-aware Expression Region Organization), designed to perform hierarchy-aware selection, refinement, ranking, and visualization of whole-brain cell detection datasets. The workflow is customizable to specific needs, requires minimal coding experience, and outputs transparent, curated results. HERO is designed to function as a seamless plug-in within larger-scale whole-brain cell-detection analysis pipelines, providing efficient, unbiased region selection to streamline subsequent statistical analyses and comparative evaluations. Although HERO is developed with mouse cell-detection datasets, it can, in principle, be applied to any atlas-mapped dataset that contains hierarchical information. In sum, HERO offers a standardized analysis workflow to reduce whole-brain cell-detection datasets, transforming raw regional cell counts into curated results and advancing the effectiveness, interpretability, and accessibility of whole-brain imaging in neuroscience.

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Shipman, A. L., Centanni, S. W.. 2026-07-08. HERO: A hierarchy-aware analysis pipeline for reducing and refining whole-brain atlas-mapped cellular datasets. https://doi.org/10.64898/2026.07.02.736093

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