def test_modularity():
source = np.matrix(test_matrices[4][0])
target = test_matrices[4][1]
clusters = mc.get_clusters(mc.run_mcl(source))
quality = mc.modularity(source, clusters)
assert np.isclose(quality, target)
def MCL(cdr3, edgelist=None, mcl_hyper=[1.2, 2], outfile=None):
"""
Perform clustering on a network of CDR3 amino acid sequences with
a known hamming distance, using the Markov clustering (MCL) algorithm.
For more info about the inflation and expansion parameters,
visit: https://micans.org/mcl/
Parameters
----------
edgelist : set, optional
Tab-separated edgelist. The default is None.
mcl_hyper : list, optional
MCL hyperparameters: inflation and expansion.
The default is [1.2,2].
outfile : str, optional
Name of outfile. The default is None.
Returns
-------
clusters : pd.DataFrame
pd.DataFrame containing two columns: 'CDR3' and 'cluster'.
The first column contains CDR3 sequences, the second column
contains the corresponding cluster ids.
"""
if edgelist is None:
edgelist = create_edgelist(cdr3)
try:
G = nx.parse_adjlist(edgelist, nodetype=str)
m = nx.to_scipy_sparse_array(G)
# Run MCL
result = mcl.run_mcl(m, inflation=mcl_hyper[0], expansion=mcl_hyper[1])
mcl_output = mcl.get_clusters(result)
identifiers = list(G.nodes())
# Map cluster ids back to seqs
cluster_ids = dict()
for i in range(len(mcl_output)):
cluster_ids[i] = list(identifiers[i] for i in mcl_output[i])
# Generate nodelist
clusters = {"CDR3": [], "cluster": []}
for c in cluster_ids:
for seq in cluster_ids[c]:
clusters["CDR3"].append(seq)
clusters["cluster"].append(c)
clusters = pd.DataFrame(data=clusters)
# Write to file
if outfile is not None:
clusters.to_csv(outfile, sep="\t", index=False)
except nx.NetworkXError:
clusters = pd.DataFrame({"CDR3": [], "cluster": []})
return clusters
