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Bijari, K.* ; Valera, G. ; López-Schier, H. ; Ascoli, G.A.*

Quantitative neuronal morphometry by supervised and unsupervised learning.

STAR Protoc. 2:100867 (2021)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
We present a protocol to characterize the morphological properties of individual neurons reconstructed from microscopic imaging. We first describe a simple procedure to extract relevant morphological features from digital tracings of neural arbors. Then, we provide detailed steps on classification, clustering, and statistical analysis of the traced cells based on morphological features. We illustrate the pipeline design using specific examples from zebrafish anatomy. Our approach can be readily applied and generalized to the characterization of axonal, dendritic, or glial geometry. For complete context and scientific motivation for the studies and datasets used here, refer to Valera et al. (2021).
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Bioinformatics ; Cell Biology ; Computer Sciences ; Microscopy ; Neuroscience
e-ISSN 2666-1667
Zeitschrift STAR Protocols
Quellenangaben Band: 2, Heft: 4, Seiten: , Artikelnummer: 100867 Supplement: ,
Begutachtungsstatus Peer reviewed
Förderungen National Cancer Institute of the National Institutes of Health (NIH)