Author: Charles Tapley Hoyt
Estimated Run Time: 2 minutes
This notebook explores methods of comparing subgraphs and identifying meaningful overlaps between them.
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import logging
import os
import sys
import time
from collections import Counter, defaultdict
from operator import itemgetter
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
import numpy as np
import seaborn as sns
import pybel
import pybel_tools as pbt
from pybel.constants import *
from pybel_tools.visualization import to_jupyter
from pybel_tools.utils import barh, barv
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#%config InlineBackend.figure_format = 'svg'
%matplotlib inline
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time.asctime()
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pybel.__version__
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pbt.__version__
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To make this notebook interoperable across many machines, locations to the repositories that contain the data used in this notebook are referenced from the environment, set in ~/.bashrc to point to the place where the repositories have been cloned. Assuming the repositories have been git clone'd into the ~/dev folder, the entries in ~/.bashrc should look like:
...
export BMS_BASE=~/dev/bms
...
The biological model store (BMS) is the internal Fraunhofer SCAI repository for keeping BEL models under version control. It can be downloaded from https://tor-2.scai.fraunhofer.de/gf/project/bms/
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bms_base = os.environ['BMS_BASE']
The Alzheimer's Disease Knowledge Assembly has been precompiled with the following command line script, and will be loaded from this format for improved performance. In general, derived data, such as the gpickle representation of a BEL script, are not saved under version control to ensure that the most up-to-date data is always used.
pybel convert --path "$BMS_BASE/aetionomy/alzheimers.bel" --pickle "$BMS_BASE/aetionomy/alzheimers.gpickle"
The BEL script can also be compiled from inside this notebook with the following python code:
>>> import os
>>> import pybel
>>> # Input from BEL script
>>> bel_path = os.path.join(bms_base, 'aetionomy', 'alzheimers.bel')
>>> graph = pybel.from_path(bel_path)
>>> # Output to gpickle for fast loading later
>>> pickle_path = os.path.join(bms_base, 'aetionomy', 'alzheimers.gpickle')
>>> pybel.to_pickle(graph, pickle_path)
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pickle_path = os.path.join(bms_base, 'aetionomy', 'alzheimers', 'alzheimers.gpickle')
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graph = pybel.from_pickle(pickle_path)
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graph.version
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Possible definitions of subgraph overlap:
The overlap of edges between each subgraph is quantified with the tanimoto similarity then clustered with seaborn.
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edge_overlap_data = pbt.summary.summarize_subgraph_edge_overlap(graph, 'Subgraph')
edge_overlap_df = pd.DataFrame(edge_overlap_data)
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plt.title('Histogram of pairwise subgraph overlaps')
plt.ylabel('Frequency')
plt.xlabel('Subgraph overlap')
plt.hist(edge_overlap_df.as_matrix().ravel(), log=True)
plt.show()
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cg = sns.clustermap(edge_overlap_df.as_matrix())
plt.show()
The subgraphs are analyzed for overlap by their shared nodes with pbt.summary.summarize_subgraph_node_overlap. Ultimately, there isn't a huge overlap by node definitions. By using the expansion workflow from before, subgraph distances can be more readily calculated.
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node_overlap_data = pbt.summary.summarize_subgraph_node_overlap(graph)
node_overlap_df = pd.DataFrame(node_overlap_data)
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plt.hist(node_overlap_df.as_matrix().ravel(), log=True)
plt.show()
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cg = sns.clustermap(node_overlap_data, figsize=(10, 10))
plt.show()