This package is intended as a toolkit for manipulating hierarchical brain atlases. For example, the Allen Mouse Brain Atlas has a parent-child ontology of this structure:
{
"id": 997,
"acronym": "root",
"name": "root",
"graph_order": 0,
"parent_structure_id": null,
"children": [
{
"id": 8,
"acronym": "grey",
"name": "Basic cell groups and regions",
"graph_order": 1,
"parent_structure_id": 997,
"children": [
{
"id": 567,
"acronym": "CH",
"name": "Cerebrum",
"graph_order": 2,
"parent_structure_id": 8,
"children": [
...
- python>=3.7
- A system-wide installation of graphviz: https://www.graphviz.org/ if you are going to use any of the visualization tools in this package.
$ pip install brain-atlas-toolkit
from brain_atlas_toolkit import graph_tools
import json
json_file = "allen_ontology.json"
with open(json_file,'r') as infile:
ontology_dict = json.load(infile)
Note that this JSON file must have the structure of the example ontology shown above. The minimal set of keys in each element are:
- id
- name
- parent_structure_id
ontology_graph = graph_tools.Graph(ontology_dict)
Get all progeny (a.k.a. descendents or subregions) of a region of interest returned in a flattened list
ontology_graph.get_progeny('Somatomotor areas')
which returns:
['Somatomotor areas, Layer 1', 'Somatomotor areas, Layer 2/3', 'Somatomotor areas, Layer 5', 'Somatomotor areas, Layer 6a', 'Somatomotor areas, Layer 6b', 'Primary motor area', 'Primary motor area, Layer 1', 'Primary motor area, Layer 2/3', 'Primary motor area, Layer 5', 'Primary motor area, Layer 6a', 'Primary motor area, Layer 6b', 'Secondary motor area', 'Secondary motor area, layer 1', 'Secondary motor area, layer 2/3', 'Secondary motor area, layer 5', 'Secondary motor area, layer 6a', 'Secondary motor area, layer 6b']
Get progeny (a.k.a. descendents or subregions) of a region of interest down to a certain depth of the tree, returned in a flattened list
ontology_graph.get_progeny('Somatomotor areas',stoplevel=1) # only returns immediate children, stoplevel=2 means children and grandchildren, etc...
which returns:
['Somatomotor areas, Layer 1',
'Somatomotor areas, Layer 2/3',
'Somatomotor areas, Layer 5',
'Somatomotor areas, Layer 6a',
'Somatomotor areas, Layer 6b',
'Primary motor area',
'Secondary motor area']
The default stoplevel value is -1, which means get all descendents in the entire tree to max depth.
ontology_graph.get_parent('Somatomotor areas')
which returns:
Isocortex
ontology_graph.get_progenitors('Somatomotor areas')
which returns:
['Isocortex',
'Cortical plate',
'Cerebral cortex',
'Cerebrum',
'Basic cell groups and regions',
'root']
ontology_graph.get_id('Somatomotor areas')
which returns:
500
ontology_graph.get_parent('Somatomotor areas')
which returns:
MO
ontology_graph.print_branch('Somatomotor areas')
which returns
0 Somatomotor areas
1 Somatomotor areas, Layer 1
1 Somatomotor areas, Layer 2/3
1 Somatomotor areas, Layer 5
1 Somatomotor areas, Layer 6a
1 Somatomotor areas, Layer 6b
1 Primary motor area
2 Primary motor area, Layer 1
2 Primary motor area, Layer 2/3
2 Primary motor area, Layer 5
2 Primary motor area, Layer 6a
2 Primary motor area, Layer 6b
1 Secondary motor area
2 Secondary motor area, layer 1
2 Secondary motor area, layer 2/3
2 Secondary motor area, layer 5
2 Secondary motor area, layer 6a
2 Secondary motor area, layer 6b
ontology_graph.print_branch('Somatomotor areas',stoplevel=1)
which returns
0 Somatomotor areas
1 Somatomotor areas, Layer 1
1 Somatomotor areas, Layer 2/3
1 Somatomotor areas, Layer 5
1 Somatomotor areas, Layer 6a
1 Somatomotor areas, Layer 6b
1 Primary motor area
1 Secondary motor area
The default stoplevel value is -1, which means print the entire tree to max depth.
digraph = ontology_graph.visualize_graph('Somatomotor areas',level=2)
digraph.format='png' # control the image type; supports png, pdf and other formats
digraph.view()
The last line will save and then open up an image in your default image viewer application. The image should look like this (click image to view zoomed in version):
For full documentation of the digraph
object, see the graphviz Python API documentation: https://graphviz.readthedocs.io/en/stable/