fafbseg.flywire.synapses.get_synapses

fafbseg.flywire.synapses.get_synapses(x, pre=True, post=True, attach=True, filtered=True, min_score=None, transmitters=False, neuropils=False, clean=True, materialization='auto', batch_size=10, *, dataset=None, progress=True)

Fetch Buhmann et al. (2019) synapses for given neuron(s).

Parameters:

• x (int | list of int | Neuron/List | NeuronCriteria) – Either a FlyWire segment ID (i.e. root ID), a list thereof or a Neuron/List. For neurons, the .id is assumed to be the root ID. If you have a neuron (in FlyWire space) but don’t know its ID, use fafbseg.flywire.neuron_to_segments() first.

• pre (bool) – Whether to fetch presynapses for the given neurons.

• post (bool) – Whether to fetch postsynapses for the given neurons.

• transmitters (bool) – Whether to also load per-synapse neurotransmitter predictions from Eckstein et al. (2020).

• neuropils (bool) – Whether to add a column indicating which neuropil a synapse is in.

• attach (bool) – If True and x is Neuron/List, the synapses will be added as .connectors table. For TreeNeurons (skeletons), the synapses will be mapped to the closest node. Note that the function will still return the full synapse table.

• filtered (bool) – Whether to use the filtered synapse table. Briefly, this filter removes redundant and low confidence (<= 50 cleft score) synapses. See also https://tinyurl.com/4j9v7t86 (links to CAVE website).

• min_score (int) – Minimum “cleft score”. Buhmann et al. used a threshold of 30 in their paper. However, for FlyWire analyses that threshold was raised to 50 (see also filtered).

• clean (bool) –

  If True, we will perform some clean up of the connectivity compared with the raw synapse information. Currently, we:

  - drop autapses
  - drop synapses from/to background (id 0)
  - drop synapses that are >10um from the skeleton (only
    if ``attach=True``)
  

• batch_size (int) – Number of IDs to query per batch. Too large batches might lead to truncated tables: currently individual queries can not return more than 500_000 rows and you will see a warning if that limit is exceeded.

• materialization (int | str, optional) –

  Which materialization to query:

  • ’auto’ (default) tries to find the most recent materialization version at which all the query IDs existed

  • ’latest’ uses the latest materialized table

  • ’live’ queries against the live data - this will be much slower!

  • pass an integer (e.g. 447) to use a specific materialization version

• dataset ("public" | "production" | "sandbox", optional) – Against which FlyWire dataset to query. If None will fall back to the default dataset (see set_default_dataset()).

Returns:

Note that each synapse (or rather synaptic connection) will show up only once. Depending on the query neurons (x), a given row might represent a presynapse for one and a postsynapse for another neuron.

Return type:

pandas.DataFrame

See also

get_connectivity()

Use this function to fetch the edges between neurons instead of individual synapses.

Examples

Fetch synapses for a given root ID:

>>> from fafbseg import flywire
>>> syn = flywire.get_synapses(720575940603231916)
Using materialization version 783.
>>> syn.head()                                               
                  pre                post  cleft_score   pre_x   pre_y  pre_z  post_x  post_y  post_z         id
0  720575940631406673  720575940603231916           60  434336  218108  28240  434340  218204   28240    3535576
1  720575940608044501  720575940603231916          136  429180  212316  51520  429244  212136   51520   15712693
2  720575940627777265  720575940603231916          142  440272  215372  35240  440152  215392   35200   29684635
3  720575940606227890  720575940603231916          147  429932  224436  41120  429968  224584   41120  111586446
4  720575940627777265  720575940603231916          146  423856  216648  51280  423844  216528   51240   15689207

Skeletonize a neuron and attach its synapses:

>>> from fafbseg import flywire
>>> sk = flywire.get_skeletons(720575940603231916)
>>> _ = flywire.get_synapses(sk, attach=True)
Using materialization version 783.
>>> sk.connectors.head()                                     
   connector_id       x       y       z  cleft_score          partner_id type  node_id
0             0  356304  146840  145120          145  720575940627592977  pre      217
1             1  344456  164324  162440          153  720575940537249676  pre        5
2             2  373200  149464  162440           52  720575940599849357  pre      390
3             3  355220  156784  151000          144  720575940537605841  pre      171
4             4  346320  154520  151720          142  720575940635161060  pre       30