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. 2020 Sep 30;12:31. doi: 10.3389/fnsyn.2020.00031

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Copyright © 2020 Pchitskaya and Bezprozvanny.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Application of principal component analysis (PCA) method to dendritic spine clusterization. Spine shape is characterized by a set of parameters (A). PCA method is used to reduce data dimensionality before clusterization. Newly generated parameters called principal components composed from initial one and form an orthonormal basis (B). After PCA dataset in new coordinates is subjected to clusterization (C). Cluster shape and content depend on the clusterization method used (k-means, c-means, hierarchical, et cetera).