NBLAST

NBLAST (Costa et al., 2016) is a method to quantify morphological similarity. It works on “dotprops” which represent neurons as tangent vectors. For each tangent vector in the query neuron, NBLAST finds the closest tangent vector in the target neuron and calculates a score from the distance between and the dotproduct of the two vectors. The final NBLAST score is the sum over all query-target vector pairs. Typically, this score is normalized to a self-self comparison (i.e. a perfect match would be 1).

nblast

VFB computes NBLAST scores for all (?) neurons in its database. So if all you want is a list of similar neurons, it’s fastest (and easiest) to get those directly from VFB.

# Import libs and initialise API objects
from vfb_connect.cross_server_tools import VfbConnect
import navis.interfaces.neuprint as neu

import pandas as pd
import navis

navis.set_pbars(jupyter=False)

vc = VfbConnect()
client = neu.Client('https://neuprint.janelia.org', dataset='hemibrain:v1.1')

First let’s write a little function to grab VFB neurons and turn them into navis neurons. This is modified from vc.neo_query_wrapper.get_images.

import requests
from tqdm import tqdm

def get_vfb_neurons(vfb_ids, template='JRC2018Unisex'):
    """Load neurons from VFB as navis.TreeNeurons."""
    vfb_ids = list(navis.utils.make_iterable(vfb_ids))
    inds = vc.neo_query_wrapper.get_anatomical_individual_TermInfo(short_forms=vfb_ids)
    nl = []
    # Note: queries should be parallelized in the future 
    # -> for pymaid I use request-futures which works really well
    for i in tqdm(inds, desc='Loading VFB neurons', leave=False):        
        if not ('has_image' in i['term']['core']['types']):
            continue
        label = i['term']['core']['label']
        image_matches = [x['image'] for x in i['channel_image']]
        if not image_matches:
            continue
        for imv in image_matches:
            if imv['template_anatomy']['label'] == template:
                r = requests.get(imv['image_folder'] + '/volume.swc')
                ### Slightly dodgy warning - could mask network errors
                if not r.ok:
                    warnings.warn("No '%s' file found for '%s'." % (image_type, label))
                    continue

                # `id` should ideally be unique but that's not enforced
                n = navis.TreeNeuron(r.text, name=label, id=i['term']['core']['short_form'])   

                # This registers attributes that you want to show in the summary
                # alternatively we could have a generic attribute like so:
                # n.template = template
                n._register_attr("template", template, temporary=False)

                # I assume all available templates are in microns?
                # @Robbie -do we have this metadata on templates?  If not, we should add ASAP.
                n.units = '1 micron'

                nl.append(n)
    return navis.NeuronList(nl)

# Search for similar neurons using the VFB ID of an ellipsoid body neuron in FAFB
# We got that ID from a search on the VFB website
similar_to_EPG6L1 = vc.get_similar_neurons('VFB_001012ay')
similar_to_EPG6L1

	id	NBLAST_score	label	types	source_id	accession_in_source
0	VFB_jrchjtk6	0.525	EPG(PB08)_L6 - 541870397	[EB-PB 1 glomerulus-D/Vgall neuron]	neuprint_JRC_Hemibrain_1point1	541870397
1	VFB_jrchjtk8	0.524	EPG(PB08)_L6 - 912601268	[EB-PB 1 glomerulus-D/Vgall neuron]	neuprint_JRC_Hemibrain_1point1	912601268
2	VFB_jrchjtkb	0.504	EPG(PB08)_L6 - 788794171	[EB-PB 1 glomerulus-D/Vgall neuron]	neuprint_JRC_Hemibrain_1point1	788794171
3	VFB_jrchjtji	0.481	EL(EQ5)_L - 1036753721	[EBw.AMP.s-Dga-s.b neuron]	neuprint_JRC_Hemibrain_1point1	1036753721

# Read those neurons from VFB 
query = get_vfb_neurons('VFB_001012ay')
matches = get_vfb_neurons(similar_to_EPG6L1.id.values)

matches

	type	name	id	n_nodes	n_connectors	n_branches	n_leafs	cable_length	soma	units	template
0	navis.TreeNeuron	EPG(PB08)_L6 - 912601268	VFB_jrchjtk8	9819	None	1000	1015	2211.292188	None	1 micron	JRC2018Unisex
1	navis.TreeNeuron	EL(EQ5)_L - 1036753721	VFB_jrchjtji	13864	None	1942	1974	3403.311508	[1]	1 micron	JRC2018Unisex
2	navis.TreeNeuron	EPG(PB08)_L6 - 788794171	VFB_jrchjtkb	9651	None	962	987	2125.364857	None	1 micron	JRC2018Unisex
3	navis.TreeNeuron	EPG(PB08)_L6 - 541870397	VFB_jrchjtk6	11042	None	1099	1127	2432.101351	[1]	1 micron	JRC2018Unisex

# Define colors such that query is black 
import seaborn as sns 
colors = ['k'] + sns.color_palette('muted', len(matches))

navis.plot3d([query, matches], color=colors)