Clustering Tool

For quantification and statistical analysis of cell clusters we implemented an algorithm that determines the number of cell bodies within a spherical volume around each neuron. If the total number of cells contained increases linearly with the diameter of spheres, the cell population is considered to be homogeneously distributed (Null hypothesis). In contrast, any deviation from the linear function is an indicative of clusters (alternative hypothesis). The critical diameters and cell counts at which deviations occur represent the critical density and cluster size. Applying the critical density and cluster size as thresholds, the program is able to select and visualize neurons which are part of a putative cluster. The user also have the ability to detect spatial overlap between a found cluster and any given cell population. The tool can work in batch mode allowing users to call the algorithm from scripts or other programs (e.g. Matlab).