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DONE: Detection of Outlier NEurons

Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011). This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface. References: * L. d. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser. Beyond the average: Detecting global singular nodes from local features in complex networks, Europhysics Letters, 87, 1 (2009) * C. Echtermeyer, L. d. Fontoura Costa, F. A. Rodrigues, M. Kaiser. Automatic Network Fingerprinting Through Single-Node Motifs, PLoS ONE 6, 9 (2011) * K. d. Zawadzki, C. Feenders, M. P. Viana, M. Kaiser, and L. d. Fontoura Costa. Morphological Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells, Neuroinformatics (2012)

URL: http://www.biological-networks.org/p/outliers/

Resource ID: nlx_144348     Resource Type: Resource     Version: Latest Version

Keywords

neuron, feature-space, archetype, outlier, matlab, neuromorphometry, computational neuroscience

Funding Information

FAPESP, CNPq, EPSRC, Code Analysis Repository and Modelling for e-Neuroscience, Korean Ministry of Education Science and Technology, 05/00587-5, 301303/06-1, 2010/01994-1, 2010/16310-0, 573583/2008-0, EP/G03950X/1, R32-10142

Listed By

NITRC

Supercategory

Resource

Related To

Resource:NeuroMorpho.Org

Abbreviation

DONE

Parent Organization

Synonyms

Detection of Outlier NEurons

Additional Resource Types

software application

Availability

GNU General Public License

Publication Link

http://www.biological-networks.org/pubs/Zawadzki2012.pdf

Original Submitter

Anonymous

Version Status

Curated

Submitted On

12:00am April 2, 2012

Originated From

SciCrunch

Changes from Previous Version

First Version

Version 1

Created 3 years ago by Anonymous

Morphological homogeneity of neurons: searching for outlier neuronal cells.

  • Zawadzki K
  • Neuroinformatics
  • 2012 11

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.