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A model-based hierarchical Bayesian approach to Sholl analysis.
VonKaenel, Erik; Feidler, Alexis; Lowery, Rebecca; Andersh, Katherine; Love, Tanzy; Majewska, Ania; McCall, Matthew N.
Afiliación
  • VonKaenel E; Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, United States.
  • Feidler A; Department of Neuroscience, University of Rochester, Rochester, NY 14642, United States.
  • Lowery R; Department of Neuroscience, University of Rochester, Rochester, NY 14642, United States.
  • Andersh K; Department of Neuroscience, University of Rochester, Rochester, NY 14642, United States.
  • Love T; Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, United States.
  • Majewska A; Department of Neuroscience, University of Rochester, Rochester, NY 14642, United States.
  • McCall MN; Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, United States.
Bioinformatics ; 40(4)2024 03 29.
Article en En | MEDLINE | ID: mdl-38514403
ABSTRACT
MOTIVATION Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used.

RESULTS:

To fill this longstanding gap, we introduce a parametric hierarchical Bayesian model-based approach for analyzing Sholl data, so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to real data and perform simulation studies comparing the proposed method with a popular alternative. AVAILABILITY AND IMPLEMENTATION Software to reproduce the results presented in this article is available at https//github.com/vonkaenelerik/hierarchical_sholl. An R package implementing the proposed models is available at https//github.com/vonkaenelerik/ShollBayes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos Límite: Animals Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos Límite: Animals Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido