R/flowcentrality.R
flow.centrality.Rdimplementation of the algorithm for calculating flow centralities from Schneider-Mizell et al. (2016). Note that the neurites() function will retrieve these clusters as separate neuron objects in a neuronlist.
flow.centrality(x, mode = c("sum", "centrifugal", "centripetal"), polypre = TRUE, primary.dendrite = 0.9, bending.flow = FALSE, split = c("postsynapses", "presynapses", "distance"), catmaid = TRUE, ...) # S3 method for neuron flow.centrality(x, mode = c("sum", "centrifugal", "centripetal"), polypre = TRUE, primary.dendrite = 0.9, bending.flow = FALSE, split = c("postsynapses", "presynapses", "distance"), catmaid = TRUE, ...) # S3 method for neuronlist flow.centrality(x, mode = c("sum", "centrifugal", "centripetal"), polypre = T, primary.dendrite = 0.9, bending.flow = FALSE, split = c("postsynapses", "presynapses", "distance"), catmaid = TRUE, ...)
| x | a neuronlist or neuron object |
|---|---|
| mode | type of flow centrality to calculate. There are three flavors: (1) centrifugal, which counts paths from proximal inputs to distal outputs; (2) centripetal, which counts paths from distal inputs to proximal outputs; and (3) the sum of both. |
| polypre | whether to consider the number of presynapses as a multiple of the numbers of connections each makes |
| primary.dendrite | whether to try to assign nodes to a 'primary dendrite'. Defaults to considering nodes of 0.9*maximal flow centrality. Assigning to NULL will prevent generating this compartment. |
| bending.flow | we may need to add the 'bending flow' to all the branchpoints if looking at centripetal flow centrality |
| split | the algorithm will assign two main neurite compartments, which as per SWC format will be indicates as either axon (Label =2) or dendrite (Label = 3) in the returned objects, at neuron$d$Label. This assignment can be based which compartment contains the most postsynapses ("postsynapses") or presynapses ("presynapses"), or the Euclidean distance of its first branch point from the primary branch point (i.e. the first branch point from the soma) ("distance"). |
| catmaid | if TRUE, the default CATMAID server cna be queried in order to find the number of connections each connector has, and use this to weight this each connector's influence in the flow through the neuronal arbour |
| ... | additional arguments passed to methods. |
the neuron or neuron list object inputted, with centripetal flow centrality information added to neuron$d, a segregation index score and estimation of neuronal type (interneuron or PN) based on this score (>0.05 = PN).
From Schneider-Mizell et al. (2016): "We use flow centrality for four purposes. First, to split an arbor into axon and dendrite at the maximum centrifugal SFC, which is a preliminary step for computing the segregation index, for expressing all kinds of connectivity edges (e.g. axo-axonic, dendro-dendritic) in the wiring diagram, or for rendering the arbor in 3d with differently colored regions. Second, to quantitatively estimate the cable distance between the axon terminals and dendritic arbor by measuring the amount of cable with the maximum centrifugal SFC value. Third, to measure the cable length of the main dendritic shafts using centripetal SFC, which applies only to insect neurons with at least one output syn- apse in their dendritic arbor. And fourth, to weigh the color of each skeleton node in a 3d view, providing a characteristic signature of the arbor that enables subjective evaluation of its identity."
Schneider-Mizell, C. M., Gerhard, S., Longair, M., Kazimiers, T., Li, F., Zwart, M. F., … Cardona, A. (2015). Quantitative neuroanatomy for connectomics in Drosophila. bioRxiv, 026617. http://doi.org/10.1101/026617