def read_graph(bestedges, maxerr=100, directed=False):
logging.debug("Max error = {0}%".format(maxerr))
tag = "dir." if directed else ""
bestgraph = bestedges.split(".")[0] + ".err{0}.{1}graph".format(
maxerr, tag)
if need_update(bestedges, bestgraph):
G = {} if directed else nx.Graph()
fp = open(bestedges)
best_store = {}
for row in fp:
if row[0] == "#":
continue
id1, lib_id, best5, o5, best3, o3, j1, j2 = row.split()
id1, best5, best3 = int(id1), int(best5), int(best3)
j1, j2 = float(j1), float(j2)
if j1 <= maxerr or j2 <= maxerr:
if not directed:
G.add_node(id1)
id1p5, id1p3 = "{0}-5'".format(id1), "{0}-3'".format(id1)
best5o5 = "{0}-{1}".format(best5, o5)
best3o3 = "{0}-{1}".format(best3, o3)
best_store[id1p5] = best5o5
best_store[id1p3] = best3o3
if best5 and j1 <= maxerr:
if directed:
G[id1p5] = best5o5
else:
G.add_edge(best5, id1, weight=10)
if best3 and j2 <= maxerr:
if directed:
G[id1p3] = best3o3
else:
G.add_edge(id1, best3, weight=10)
# Annotate edge weight for mutual best link, note that edge weights are
# (11) set close to 10, to minimize impact to layout (Yifan Hu's
# multilevel)
nmutuals = 0
for k, v in best_store.items():
if best_store.get(v) == k and k < v:
k, v = int(k.split("-")[0]), int(v.split("-")[0])
G[k][v]["weight"] = 11
nmutuals += 1
logging.debug("Mutual best edges: {0}".format(nmutuals))
if directed:
fw = open(bestgraph, "w")
dump(G, fw)
fw.close()
else:
nx.write_gpickle(G, bestgraph)
logging.debug("Graph pickled to `{0}`".format(bestgraph))
# Compute node degree histogram and save in (degree, counts) tab file
degrees = G.degree()
degree_counter = Counter(degrees.values())
degreesfile = "degrees.txt"
fw = open(degreesfile, "w")
for degree, count in sorted(degree_counter.items()):
print("{0}\t{1}".format(degree, count), file=fw)
fw.close()
logging.debug(
"Node degree distribution saved to `{0}`".format(degreesfile))
# Save high degree (top 1%) nodes in save in (node, degree) tab file
percentile = sorted(degrees.values(),
reverse=True)[len(degrees) / 1000]
logging.debug("Top 0.1% has degree of at least {0}".format(percentile))
hubs = [(k, v) for k, v in degrees.items() if v >= percentile]
hubs.sort(key=lambda x: x[1], reverse=True) # degress descending
hubsfile = "hubs.txt"
fw = open(hubsfile, "w")
for node, degree in hubs:
print("{0}\t{1}".format(node, degree), file=fw)
fw.close()
logging.debug("Hubs saved to `{0}`".format(hubsfile))
logging.debug("Read graph from `{0}`".format(bestgraph))
if directed:
G = load(open(bestgraph))
else:
G = nx.read_gpickle(bestgraph)
graph_stats(G)
return G
def read_graph(bestedges, maxerr=100, directed=False):
logging.debug("Max error = {0}%".format(maxerr))
tag = "dir." if directed else ""
bestgraph = bestedges.split(".")[0] + ".err{0}.{1}graph".format(maxerr, tag)
if need_update(bestedges, bestgraph):
G = {} if directed else nx.Graph()
fp = open(bestedges)
best_store = {}
for row in fp:
if row[0] == "#":
continue
id1, lib_id, best5, o5, best3, o3, j1, j2 = row.split()
id1, best5, best3 = int(id1), int(best5), int(best3)
j1, j2 = float(j1), float(j2)
if j1 <= maxerr or j2 <= maxerr:
if not directed:
G.add_node(id1)
id1p5, id1p3 = "{0}-5'".format(id1), "{0}-3'".format(id1)
best5o5 = "{0}-{1}".format(best5, o5)
best3o3 = "{0}-{1}".format(best3, o3)
best_store[id1p5] = best5o5
best_store[id1p3] = best3o3
if best5 and j1 <= maxerr:
if directed:
G[id1p5] = best5o5
else:
G.add_edge(best5, id1, weight=10)
if best3 and j2 <= maxerr:
if directed:
G[id1p3] = best3o3
else:
G.add_edge(id1, best3, weight=10)
# Annotate edge weight for mutual best link, note that edge weights are
# (11) set close to 10, to minimize impact to layout (Yifan Hu's
# multilevel)
nmutuals = 0
for k, v in best_store.items():
if best_store.get(v) == k and k < v:
k, v = int(k.split("-")[0]), int(v.split("-")[0])
G[k][v]["weight"] = 11
nmutuals += 1
logging.debug("Mutual best edges: {0}".format(nmutuals))
if directed:
fw = open(bestgraph, "w")
cPickle.dump(G, fw)
fw.close()
else:
nx.write_gpickle(G, bestgraph)
logging.debug("Graph pickled to `{0}`".format(bestgraph))
# Compute node degree histogram and save in (degree, counts) tab file
degrees = G.degree()
degree_counter = Counter(degrees.values())
degreesfile = "degrees.txt"
fw = open(degreesfile, "w")
for degree, count in sorted(degree_counter.items()):
print >> fw, "{0}\t{1}".format(degree, count)
fw.close()
logging.debug("Node degree distribution saved to `{0}`".format(degreesfile))
# Save high degree (top 1%) nodes in save in (node, degree) tab file
percentile = sorted(degrees.values(), reverse=True)[len(degrees) / 1000]
logging.debug("Top 0.1% has degree of at least {0}".format(percentile))
hubs = [(k, v) for k, v in degrees.items() if v >= percentile]
hubs.sort(key=lambda x: x[1], reverse=True) # degress descending
hubsfile = "hubs.txt"
fw = open(hubsfile, "w")
for node, degree in hubs:
print >> fw, "{0}\t{1}".format(node, degree)
fw.close()
logging.debug("Hubs saved to `{0}`".format(hubsfile))
logging.debug("Read graph from `{0}`".format(bestgraph))
if directed:
G = cPickle.load(open(bestgraph))
else:
G = nx.read_gpickle(bestgraph)
graph_stats(G)
return G
def graph(args):
"""
%prog graph best.edges
Convert Celera Assembler's "best.edges" to a GEXF which can be used to
feed into Gephi to check the topology of the best overlapping graph.
Reference:
https://github.com/PacificBiosciences/Bioinformatics-Training/blob/master/scripts/CeleraToGephi.py
"""
import networkx as nx
from jcvi.algorithms.graph import graph_stats, graph_local_neighborhood
p = OptionParser(graph.__doc__)
p.add_option("--maxerr", default=100, type="int", help="Maximum error rate")
p.add_option("--query", default=-1, type="int", help="Search from node")
p.add_option("--largest", default=1, type="int", help="Only show largest components")
p.add_option("--maxsize", default=100, type="int", help="Max graph size")
p.add_option("--contigs", help="Annotate graph with contig membership, "
" typically from `asm.posmap.frgctg`")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
bestedges, = args
maxerr = opts.maxerr
query = opts.query
largest = opts.largest
logging.debug("Max error = {0}%".format(maxerr))
bestgraph = bestedges.split(".")[0] + ".err{0}.graph".format(maxerr)
if need_update(bestedges, bestgraph):
G = nx.Graph()
fp = open(bestedges)
for row in fp:
if row[0] == '#':
continue
id1, lib_id, best5, o1, best3, o3, j1, j2 = row.split()
id1, best5, best3 = int(id1), int(best5), int(best3)
j1, j2 = float(j1), float(j2)
if j1 < maxerr or j2 < maxerr:
G.add_node(id1)
if best5 != '0' and j1 < maxerr:
G.add_edge(best5, id1)
if best3 != '0' and j2 < maxerr:
G.add_edge(id1, best3)
nx.write_gpickle(G, bestgraph)
logging.debug("Graph pickled to `{0}`".format(bestgraph))
logging.debug("Read graph from `{0}`".format(bestgraph))
G = nx.read_gpickle(bestgraph)
graph_stats(G)
if len(G) > 10000:
SG = nx.Graph()
H = graph_local_neighborhood(G, query=query,
maxsize=opts.maxsize)
SG.add_edges_from(H.edges())
G = SG
if largest > 1: # only works for un-directed graph
H = nx.connected_component_subgraphs(G)
c = min(len(H), largest)
logging.debug("{0} components found, {1} retained".format(len(H), c))
G = nx.Graph()
for x in H[:c]:
G.add_edges_from(x.edges())
if opts.contigs:
reads_to_ctgs = parse_ctgs(bestedges, opts.contigs)
annotate_contigs(G, reads_to_ctgs)
gexf = "best"
if query >= 0:
gexf += ".{0}".format(query)
gexf += ".gexf"
nx.write_gexf(G, gexf)
logging.debug("Graph written to `{0}` (|V|={1}, |E|={2})".\
format(gexf, len(G), G.size()))
