Connectomics

!pip install --upgrade vfb_connect
!pip install --upgrade git+git://github.com/schlegelp/navis@master
!pip install --upgrade git+git://github.com/schlegelp/pymaid@master
!pip install --upgrade ipywidgets
!pip install --upgrade jsonpath_rw
!pip install --upgrade seaborn
!pip install --upgrade neuprint-python
!pip install --upgrade flybrains

# Import libs and initialise API objects
from vfb_connect.cross_server_tools import VfbConnect
import pandas as pd
vc = VfbConnect()

import pymaid
import navis

navis.set_pbars(jupyter=False)
pymaid.set_pbars(jupyter=False)

# Connect to the VFB CATMAID server hosting the FAFB data
rm = pymaid.connect_catmaid(server="https://fafb.catmaid.virtualflybrain.org/", api_token=None, max_threads=10)

# Test call to see if connection works 
print(f'Server is running CATMAID version {rm.catmaid_version}')

WARNING: Could not load OpenGL library.
INFO  : Global CATMAID instance set. Caching is ON. (pymaid)
Server is running CATMAID version 2020.02.15-905-g93a969b37

VFB provides a simple methods for exploring connectomics data

get_connected_neurons_by_type allows queries for connections between defined upstream and downstream neuron types/classes. This supports a level of abstraction not available without VFB, where grouping of neurons by higher level classifications is limited to extraction of classification from standardised names. The following examples take advantage of the abstract clasifications on VFB to explore direct synaptic connections.

# Find all GABA-regic inputs onto descending neurons

vc.get_connected_neurons_by_type(upstream_type='GABAergic neuron',
                                 downstream_type='adult descending neuron',
                                 weight=10).sort_values('weight', ascending=False, ignore_index=True)

# Get connections between pacemaker neurons
vc.get_connected_neurons_by_type(upstream_type='pacemaker neuron',
                                 downstream_type='pacemaker neuron',
                                 weight=10).sort_values('weight', ascending=False)

# Get connections between visual projection neurons and descending neurons
vc.get_connected_neurons_by_type(upstream_type='visual projection neuron',
                                 downstream_type='adult descending neuron',
                                 weight=10).sort_values('weight', ascending=False)

Get neurons downstream/downstream of a specific neuron

Starting from an individual neuron, in this case an individual neuron from hemibrain, find all neurons upstream or downstream, restricting to edges >= some specified wieght (synaptic connections) and optionally limiting the output by neuron type/class.

# Starting point - let's take one of the individual descending neurons from
# the previous exploratory queries and look for other inputs

vc.get_neurons_upstream_of('DNp04_R - 1405231475', weight = 20)

Let’s try a similar query from one of the LC4 neurons to see what other descending neuron types are downstream of it.

vc.get_neurons_downstream_of('LC4 - 1249932198', classification="'adult descending neuron'", weight = 20)

Running query: FBbt:00047511
Query URL: http://owl.virtualflybrain.org/kbs/vfb/instances?object=FBbt%3A00047511&prefixes=%7B%22FBbt%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFBbt_%22%2C+%22RO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_%22%2C+%22BFO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FBFO_%22%7D&direct=False
Query results: 139

More sophisticated connectomics queries require direct queries of connectomics DB APIs

CATMAID connectivity queries

Broadly speaking, using pymaid to query CATMAID servers lets you fetch connectivity data as either lists of up- and downstream partners or as whole adjacency matrices.

These examples use the VFB FAFB server.

# Using VFB to get neurons by type
DA1 = vc.get_instances("'adult antennal lobe projection neuron DA1'", summary=True)
DA1_tab = pd.DataFrame.from_records(DA1)
len(DA1_tab)
DA1_tab

Running query: FBbt:00048096
Query URL: http://owl.virtualflybrain.org/kbs/vfb/instances?object=FBbt%3A00048096&prefixes=%7B%22FBbt%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFBbt_%22%2C+%22RO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_%22%2C+%22BFO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FBFO_%22%7D&direct=False
Query results: 31

# Map neurons to CATMAID Skeleton IDs (skids)

da1_skids = vc.neo_query_wrapper.vfb_id_2_xrefs(DA1_tab['id'], db='catmaid_fafb', reverse_return=True)
da1_skids

{'2863104': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010124e'}],
 '57353': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101202'}],
 '2381753': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010123b'}],
 '1811442': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010121x'}],
 '57323': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101201'}],
 '57311': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101200'}],
 '2345089': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122p'}],
 '27295': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101199'}],
 '2379517': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122y'}],
 '2380564': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122z'}],
 '755022': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101205'}],
 '61221': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101204'}],
 '57381': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101203'}],
 '3239781': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010124l'}],
 '4207871': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010126e'}],
 '2334841': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122m'}],
 '2319457': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122k'}]}

# Map neurons to CATMAID Skeleton IDs (skids)

da1_skids = vc.neo_query_wrapper.vfb_id_2_xrefs(DA1_tab['id'], db='catmaid_fafb', reverse_return=True)
da1_skids_int = list(map(int, da1_skids))
da1_skids_int

[2863104,
 57353,
 2381753,
 1811442,
 57323,
 57311,
 2345089,
 27295,
 2379517,
 2380564,
 755022,
 61221,
 57381,
 3239781,
 4207871,
 2334841,
 2319457]

Generate a connectivity table for neurons downstream of DA1 neurons

da1 = pymaid.get_neurons(da1_skids_int)
da1_ds = pymaid.get_partners(da1_skids_int,
                             threshold=3,  # anything with >= 3 synapses
                             directions=['outgoing']  # downstream partners only
                            )

# Result is a pandas DataFrame
da1_ds.head()

INFO  : Fetching connectivity table for 17 neurons (pymaid)
INFO  : Done. Found 0 pre-, 270 postsynaptic and 0 gap junction-connected neurons (pymaid)

# Get an adjacency matrix between all Bates, Schlegel et al. neurons
bates = pymaid.find_neurons(annotations='Paper: Bates and Schlegel et al 2020')
adj = pymaid.adjacency_matrix(bates)
adj.head()

INFO  : Found 583 neurons matching the search parameters (pymaid)

import seaborn as sns 

ax = sns.clustermap(adj, vmax=10, cmap='Greys')

/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/matrix.py:649: UserWarning:

Clustering large matrix with scipy. Installing `fastcluster` may give better performance.

# Axo-axonic connections between two different types of DA1 PNs
cn = pymaid.get_connectors_between(2863104, 1811442)
cn.head()

import numpy as np 

points = np.vstack(cn.connector_loc)

navis.plot3d([da1.idx[[1811442, 57353]],  # plot the two neurons
              points],  # plot the points of synaptic contacts as scatter 
              scatter_kws=dict(name="synaptic contacts")
              )

#neu_da1 = neu.fetch_skeletons(neu.NeuronCriteria(type='DA1_lPN.*', regex=True))
neu_da1

split = navis.split_axon_dendrite(neu_da1[0])
split

navis.plot3d(split, connectors=True)

WARNING : Skipping empty neuron: DA1_lPN_R (722817260) (navis)