pymaid.cluster_by_synapse_placement

pymaid.cluster_by_synapse_placement(x, sigma=2000, omega=2000, mu_score=True, restrict_cn=None, remote_instance=None)[source]

Cluster neurons based on their synapse placement.

Distances score is calculated by calculating for each synapse of neuron A: (1) the Eucledian distance to the closest synapse in neuron B and (2) comparing the synapse density around synapse A and B. This is type-sensitive: presynapses will only be matched with presynapses, post with post, etc. The formula is described in Schlegel et al., eLife (2017):

f(i_{s},j_{k}) = \exp(\frac{-d^{2}_{sk}}{2\sigma^{2}}) \exp(-\frac{|n(i_{s})-n(j_{k})|}{n(i_{s})+n(j_{k})})

The synapse similarity score for neurons i and j being the average of f(i_{s},j_{k}) over all synapses s of i. Synapse k is the closest synapse of the same sign (pre/post) in neuron j to synapse s. d^{2}_{sk} is the eucledian distance between these distances. Variable \sigma (sigma) determines what distance between s and k is considered “close”. n(i_{s}) and n(j_{k}) are defined as the number of synapses of neuron i/j that are within given radius \omega (omega) of synapse s and j, respectively (same sign only). This esnures that in cases of a strong disparity between n(i_{s}) and n(j_{k}), the synapse similarity will be close to zero even if the distance between s and k is very small.

Parameters:

  • x –

    Neurons as single or list of either:

    1. skeleton IDs (int or str)

    2. neuron name (str, exact match)

    3. annotation: e.g. ‘annotation:PN right’

    4. CatmaidNeuron or CatmaidNeuronList object

  • sigma (int, optional) – Distance in nanometer between synapses that is considered to be “close”.

  • omega (int, optional) – Radius in nanometer over which to calculate synapse density.

  • mu_score (bool, optional) – If True, score is calculated as mean between A->B and B->A comparison.

  • restrict_cn (int | list | None, optional) –

    Restrict to given connector types:

    • 0: presynapses

    • 1: postsynapses

    • 2: gap junctions

    • 3: abutting connectors

    If None, will use all connectors. Use either single integer or list. E.g. restrict_cn=[0, 1] to use only pre- and postsynapses.

  • remote_instance (CatmaidInstance, optional) – If not passed, will try using globally defined. Need to provide if neurons are only skids or annotation(s).

Returns:

Object that contains distance matrix and methods to plot dendrograms.

Return type:

ClustResults