Abstract
We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the NeuroMorpho.org database, with more than 5,000 neurons. Each neuron in a given analysis is represented by a feature vector composed of 20 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations involving one or more categories of cells, as well as for detection of new categories and possible artifacts.
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Notes
The software DONE (Detection of Outlier NEurons) is an implementation for MATLAB that is available on our web-site (http://www.biological-networks.org/p/outliers/).
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Acknowledgements
Luciano da F. Costa is grateful to FAPESP (05/00587-5) and CNPq (301303/06-1 and 573583/2008-0) for sponsorship. Krissia Zawadzki is grateful to FAPESP sponsorship (2010/01994-1). Matheus P. Viana is grateful to FAPESP sponsorship (2010/16310-0). Marcus Kaiser and Christoph Feenders acknowledge support by EPSRC (EP/G03950X/1) and the CARMEN e-science Neuroinformatics project ( http://www.carmen.org.uk ) funded by EPSRC (EP/E002331/1). Marcus Kaiser is also funded through the WCU program of the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (R32-10142).
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Zawadzki, K., Feenders, C., Viana, M.P. et al. Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells. Neuroinform 10, 379–389 (2012). https://doi.org/10.1007/s12021-012-9150-5
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DOI: https://doi.org/10.1007/s12021-012-9150-5