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Quantitative neuronal morphometry by supervised and unsupervised learning.
STAR Protoc. 2:100867 (2021)
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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Publication type
Article: Journal article
Document type
Review
Keywords
Bioinformatics ; Cell Biology ; Computer Sciences ; Microscopy ; Neuroscience
e-ISSN
2666-1667
Journal
STAR Protocols
Quellenangaben
Volume: 2,
Issue: 4,
Article Number: 100867
Non-patent literature
Publications
Reviewing status
Peer reviewed
Grants
National Cancer Institute of the National Institutes of Health (NIH)