def generate_random_ensemble(self, rnd_num=100):
"""
"""
self.random_graphs = list()
rewire = net_rnd.NetworkRewiring()
rewire.conditions = null.check_structured
self.mtf_counts = net_subs.triadic_census(self)
for i in xrange(rnd_num):
(rnd, success) = rewire.randomise(self)
rnd.mtf_counts = net_subs.triadic_census(rnd)
# print "random", i, "flip success rate", success
self.random_graphs.append(rnd)
def _normal_rewiring(args):
network = args[0]
i = args[1]
rewire = net_rnd.NetworkRewiring()
(rnd, success) = rewire.randomise(network)
rnd.mtf_counts = net_subs.triadic_census(rnd)
print "random", i, "flip success rate", success
return rnd
def _structured_rewiring(args):
network = args[0]
i = args[1]
rewire = net_rnd.NetworkRewiring()
rewire.conditions = check_structured
(rnd, success) = rewire.randomise(network)
rnd.mtf_counts = net_subs.triadic_census(rnd)
print "random", i, "flip success rate", success
return rnd
def compute_tsp(self):
if not self.mtf_counts:
self.mtf_counts = net_subs.triadic_census(self)
zscores = numpy.zeros(13)
for mtf_num in xrange(1, 14):
tricode = net_subs.num2tricode[mtf_num]
zscores[mtf_num - 1] = stats.compute_zscore(
self.mtf_counts.get(tricode, 0.0),
[rnd.mtf_counts.get(tricode, 0.0) for rnd in\
self.random_ensemble])
return zscores
def trn_stats(genes, trn, t_factors, version):
LOGGER.info("Computing TRN statistics")
nodes = sorted(trn.nodes_iter())
node2id = {n: i for (i, n) in enumerate(nodes)}
id2node = {i: n for (i, n) in enumerate(nodes)}
(grn, node2id) = to_simple(trn.to_grn(), return_map=True)
nodes = sorted(grn.nodes_iter())
regulating = {node for (node, deg) in grn.out_degree_iter() if deg > 0}
regulated = set(nodes) - regulating
components = sorted(nx.weakly_connected_components(grn), key=len,
reverse=True)
data = dict()
for (a, b) in itertools.product(("in", "out"), repeat=2):
data["{a}_{b}_ass".format(a=a, b=b)] = nx.degree_assortativity_coefficient(grn, x=a, y=b)
census = triadic_census(grn)
forward = census["030T"]
feedback = census["030C"]
num_cycles = sum(1 for cyc in nx.simple_cycles(grn) if len(cyc) > 2)
in_deg = [grn.in_degree(node) for node in regulated]
out_deg = [grn.out_degree(node) for node in regulating]
data["version"] = version,
data["release"] = pd.to_datetime(RELEASE[version]),
data["num_genes"] = len(genes),
data["num_tf"] = len(t_factors),
data["num_nodes"] = len(nodes),
data["num_regulating"] = len(regulating),
data["num_regulated"] = len(regulated),
data["num_links"] = grn.size(),
data["density"] = nx.density(grn),
data["num_components"] = len(components),
data["largest_component"] = len(components[0]),
data["feed_forward"] = forward,
data["feedback"] = feedback,
data["fis_out"] = trn.out_degree(TranscriptionFactor[FIS_ID, version]),
data["hns_out"] = trn.out_degree(TranscriptionFactor[HNS_ID, version]),
data["cycles"] = num_cycles,
data["regulated_in_deg"] = mean(in_deg),
data["regulating_out_deg"] = mean(out_deg),
data["hub_out_deg"] = max(out_deg)
stats = pd.DataFrame(data, index=[1])
in_deg = [grn.in_degree(node) for node in nodes]
out_deg = [grn.out_degree(node) for node in nodes]
bc = nx.betweenness_centrality(grn)
bc = [bc[node] for node in nodes]
dists = pd.DataFrame({
"version": version,
"release": [pd.to_datetime(RELEASE[version])] * len(nodes),
"node": [id2node[node].unique_id for node in nodes],
"regulated_in_degree": in_deg,
"regulating_out_degree": out_deg,
"betweenness": bc
})
return (stats, dists)
def stats(grn, version, description):
nodes = sorted(grn.nodes_iter())
regulating = {node for (node, deg) in grn.out_degree_iter() if deg > 0}
regulated = set(nodes) - regulating
components = sorted(nx.weakly_connected_components(grn), key=len,
reverse=True)
data = dict()
census = triadic_census(grn)
forward = census["030T"]
feedback = census["030C"]
cycles = list(nx.simple_cycles(grn))
in_deg = [grn.in_degree(node) for node in regulated]
out_deg = [grn.out_degree(node) for node in regulating]
data["version"] = version
data["num_components"] = len(components)
data["largest_component"] = len(components[0])
data["feed_forward"] = forward
data["feedback"] = feedback
data["cycles"] = len(cycles)
data["regulated_in_deg"] = np.mean(in_deg)
data["regulating_out_deg"] = np.mean(out_deg)
data["null_model"] = description
stats = pd.DataFrame(data, index=[1])
return stats
