def main(argv):
parser = argparse.ArgumentParser(description=__DESCRIPTION__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("-r", dest="reftree",
type=str, required=True,
help="""Reference tree""")
parser.add_argument("source_trees", metavar='source_trees',
type=str, nargs="+",
help='A list of newick tree files used as a source for node annotations')
parser.add_argument("--discard", dest="discard",
type=str, nargs="+", default=[],
help=("A list of attributes that should be ignored from source trees. "
"Node dist, name and support values are always ignored unless they"
" are explicitly passed as target features"))
parser.add_argument("--features", dest="features",
type=str, nargs="+", default = [],
help=("A list of attributes that should be transferred from source trees."))
parser.add_argument("-o", dest="output",
type=str, required=True,
help=("output file name for the annotated tree"))
args = parser.parse_args(argv)
ref = Tree(args.reftree)
TARGET_FEATURES = args.features
DISCARD_FEATURES = args.discard + ["support", "name", "dist"]
key2node = {}
for node in ref.traverse():
nodekey = frozenset(node.get_leaf_names())
key2node[nodekey] = node
out = ref.children[0].get_leaf_names()
out2 = ref.children[1].get_leaf_names()
transferred_features = defaultdict(int)
for target in args.source_trees:
print target
tt = Tree(target)
tt.prune(ref.get_leaf_names())
if len(out) > 1:
try:
tt.set_outgroup(tt.get_common_ancestor(out))
except ValueError:
tt.set_outgroup(tt.get_common_ancestor(out2))
else:
tt.set_outgroup(tt.search_nodes(name=out[0])[0])
for node in tt.traverse():
nodekey = frozenset([n.name for n in node.get_leaves()])
target_node = key2node.get(nodekey, None)
if target_node:
for f in node.features:
if f in DISCARD_FEATURES and not TARGET_FEATURES:
continue
elif TARGET_FEATURES and f not in TARGET_FEATURES:
continue
else:
transferred_features[f] += 1
target_node.add_feature(f, getattr(node, f))
ref.write(outfile=args.output, features=[], format_root_node=True)
print
print_table(transferred_features.items(), header=["feature name", "#nodes"])
def main(argv):
parser = argparse.ArgumentParser(
description=__DESCRIPTION__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("target_trees",
metavar='target_trees',
type=str,
nargs="*",
help='a list of target tree files')
parser.add_argument(
"--targets_file",
dest="targets_file",
type=str,
help="""path to a file containing target trees, one per line""")
parser.add_argument("-o",
dest="output",
type=str,
help="""Path to the tab delimited report file""")
parser.add_argument("-r",
dest="reftree",
type=str,
required=True,
help="""Reference tree""")
parser.add_argument(
"--outgroup",
dest="outgroup",
nargs="+",
help=
"""outgroup used to root reference and target trees before distance computation"""
)
parser.add_argument("--expand_polytomies",
dest="polytomies",
action="store_true",
help="""expand politomies if necessary""")
parser.add_argument("--unrooted",
dest="unrooted",
action="store_true",
help="""compare trees as unrooted""")
parser.add_argument(
"--min_support",
dest="min_support",
type=float,
default=0.0,
help=
("min support value for branches to be counted in the distance computation (RF, treeko and refTree/targeGene compatibility)"
))
parser.add_argument(
"--extract_species",
dest="extract_species",
action="store_true",
help=
"""When used, reference tree is assumed to contain species names, while target trees as expected to be gene trees. Species name will be extracted from gene tree nodes and treeko will be used if duplication events are found."""
)
parser.add_argument("--spname_delimiter",
dest="spname_delimiter",
type=str,
default="_",
help=("species code delimiter in node names"))
parser.add_argument(
"--spname_field",
dest="spname_field",
type=int,
default=-1,
help=
("position of the species code extracted from node names. -1 = last field"
))
parser.add_argument("--collateral",
dest="collateral",
action='store_true',
help=(""))
parser.add_argument("--ref_attr",
dest="ref_attr",
type=str,
help=("attribute in ref tree used as leaf name"))
parser.add_argument("--target_attr",
dest="target_attr",
type=str,
help=("attribute in target tree used as leaf name"))
args = parser.parse_args(argv)
print __DESCRIPTION__
reftree = args.reftree
if args.targets_file and args.target_trees:
print >> sys.stderr, 'The use of targets_file and targets at the same time is not supported.'
sys.exit(1)
if args.targets_file:
target_trees = tree_iterator(args.targets_file)
else:
target_trees = args.target_trees
t = Tree(reftree)
if args.ref_attr:
for lf in t.iter_leaves():
lf._origname = lf.name
if args.ref_attr not in lf.features:
print lf
lf.name = getattr(lf, args.ref_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = t.get_common_ancestor(args.outgroup)
else:
out = t.search_nodes(name=args.outgroup[0])[0]
t.set_outgroup(out)
ref_names = set(t.get_leaf_names())
reftree_len = len(t)
reftree_edges = (reftree_len * 2) - 2
ncollapsed_branches = len([
n for n in t.traverse() if n.children and n.support < args.min_support
])
#reftree_edges -= ncollapsed_branches
#if ncollapsed_branches:
# print '%d branches collapsed in reference tree' %ncollapsed_branches
HEADER = ("target tree", 'dups', 'subtrees', 'used trees', 'treeko', "RF",
"maxRF", 'normRF', "%reftree", "%genetree", "avgSize", "minSize",
"common tips", "refSize", "targetSize")
if args.output:
OUT = open(args.output, "w")
print >> OUT, '# ' + ctime()
print >> OUT, '# ' + ' '.join(sys.argv)
print >> OUT, '#' + '\t'.join(HEADER)
else:
print '# ' + ctime()
print '# ' + ' '.join(sys.argv)
COL_WIDTHS = [20, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]
print_table([HEADER], fix_col_width=COL_WIDTHS, wrap_style='wrap')
prev_tree = None
for counter, tfile in enumerate(target_trees):
if args.targets_file:
seedid, tfile = tfile
else:
seedid = None
if args.extract_species:
tt = PhyloTree(tfile,
sp_naming_function=lambda name: name.split(
args.spname_delimiter)[args.spname_field])
else:
tt = Tree(tfile)
if args.target_attr:
for lf in tt.iter_leaves():
lf._origname = lf.name
lf.name = getattr(lf, args.target_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = tt.get_common_ancestor(args.outgroup)
else:
out = tt.search_nodes(name=args.outgroup[0])[0]
tt.set_outgroup(out)
if args.target_trees:
fname = os.path.basename(tfile)
else:
fname = '%05d' % counter
max_size, min_size, avg_size, common = -1, -1, -1, -1
total_rf, max_rf, norm_rf = -1, -1, -1
treeko_d = -1
ref_branches_in_target, target_branches_in_ref = -1, -1
target_tree_len = -1
used_subtrees = -1
if args.extract_species:
orig_target_size = len(tt)
ntrees, ndups, sp_trees = tt.get_speciation_trees(
autodetect_duplications=True, newick_only=True)
if ntrees < 1000:
all_rf = []
ref_found = []
target_found = []
tree_sizes = []
all_max_rf = []
common_names = 0
for subtree_nw in sp_trees:
if seedid and not args.collateral and (seedid
not in subtree_nw):
continue
subtree = PhyloTree(
subtree_nw,
sp_naming_function=lambda name: name.split(
args.spname_delimiter)[args.spname_field])
# only necessary if rf function is going to filter by support value. It slows downs the analysis, obviously
if args.min_support:
subtree_content = subtree.get_cached_content(
store_attr='name')
for n in subtree.traverse():
if n.children:
n.support = tt.get_common_ancestor(
subtree_content[n]).support
rf, maxr, common, p1, p2, d1, d2 = t.robinson_foulds(
subtree,
expand_polytomies=args.polytomies,
unrooted_trees=args.unrooted,
attr_t2='species',
min_support_t2=args.min_support)
if maxr > 0 and p1 and p2:
all_rf.append(rf)
tree_sizes.append(len(common))
all_max_rf.append(maxr)
common_names = max(common_names, len(common))
ref_found.append(float(len(p2 & p1)) / reftree_edges)
p2bis = set([
p for p in (p2 - d2)
if len(p[0]) > 1 and len(p[1]) > 1
]) # valid edges in target not leaves
if p2bis:
incompatible_target_branches = float(
len((p2 - d2) - p1))
target_found.append(1 -
(incompatible_target_branches /
(len(p2 - d2))))
# valid_target = p2-d2
# valid_ref = p1-d1
# ref_found.append(float(len(valid_target & valid_ref)) / reftree_edges)
# p2bis = set([p for p in (p2-d2) if len(p[0])>1 and len(p[1])>1])
# if p2bis-d2:
# incompatible_target_branches = float(len((p2-d2) - p1))
# target_found.append(1 - (incompatible_target_branches / (len(p2-d2))))
if all_rf:
# Treeko speciation distance
alld = [(all_rf[i] / float(all_max_rf[i]))
for i in xrange(len(all_rf))]
a = numpy.sum(
[alld[i] * tree_sizes[i] for i in xrange(len(all_rf))])
b = float(numpy.sum(tree_sizes))
treeko_d = a / b
total_rf = numpy.mean(all_rf)
norm_rf = numpy.mean([(all_rf[i] / float(all_max_rf[i]))
for i in xrange(len(all_rf))])
max_rf = numpy.max(all_max_rf)
ref_branches_in_target = numpy.mean(ref_found)
target_branches_in_ref = numpy.mean(
target_found) if target_found else -1
target_tree_len = numpy.mean(tree_sizes)
used_subtrees = len(all_rf)
else:
target_tree_len = len(tt)
ndups, ntrees, used_subtrees = 0, 1, 1
treeko_d = -1
total_rf, max_rf, common, p1, p2, d1, d2 = tt.robinson_foulds(
t,
expand_polytomies=args.polytomies,
unrooted_trees=args.unrooted)
common_names = len(common)
if max_rf:
norm_rf = total_rf / float(max_rf)
if p1 and p2:
sizes = [len(p) for p in p2 ^ p1]
if sizes:
avg_size = sum(sizes) / float(len(sizes))
max_size, min_size = max(sizes), min(sizes)
else:
max_size, min_size, avg_size = 0, 0, 0
ref_branches_in_target = float(len(p2 & p1)) / reftree_edges
#if p2-d2:
# incompatible_target_branches = float(len((p2-d2) - p1))
# target_found.append(1 - (incompatible_target_branches / (len(p2-d2))))
else:
ref_branches_in_target = 0.0
target_branches_in_ref = 0.0
max_size, min_size, avg_size = -1, -1, -1
if args.output:
print >> OUT, '\t'.join(
map(str, (fname, ndups, ntrees, used_subtrees, treeko_d,
total_rf, max_rf, norm_rf, ref_branches_in_target,
target_branches_in_ref, avg_size, min_size,
common_names, reftree_len, target_tree_len)))
else:
print_table([
map(istr,
(fname[-30:], ndups, ntrees, used_subtrees, treeko_d,
total_rf, max_rf, norm_rf,
'%0.4f' % ref_branches_in_target,
'%0.4f' % target_branches_in_ref, avg_size, min_size,
common_names, reftree_len, target_tree_len))
],
fix_col_width=COL_WIDTHS,
wrap_style='cut')
if args.output:
OUT.close()
def main(argv):
parser = argparse.ArgumentParser(description=__DESCRIPTION__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("target_trees", metavar='target_trees', type=str, nargs="*",
help='a list of target tree files')
parser.add_argument("--targets_file", dest="targets_file",
type=str,
help="""path to a file containing target trees, one per line""")
parser.add_argument("-o", dest="output",
type=str,
help="""Path to the tab delimited report file""")
parser.add_argument("-r", dest="reftree",
type=str, required=True,
help="""Reference tree""")
parser.add_argument("--outgroup", dest="outgroup",
nargs = "+",
help="""outgroup used to root reference and target trees before distance computation""")
parser.add_argument("--expand_polytomies", dest="polytomies",
action = "store_true",
help="""expand politomies if necessary""")
parser.add_argument("--unrooted", dest="unrooted",
action = "store_true",
help="""compare trees as unrooted""")
parser.add_argument("--min_support", dest="min_support",
type=float, default=0.0,
help=("min support value for branches to be counted in the distance computation (RF, treeko and refTree/targeGene compatibility)"))
parser.add_argument("--extract_species", dest="extract_species",
action = "store_true",
help="""When used, reference tree is assumed to contain species names, while target trees as expected to be gene trees. Species name will be extracted from gene tree nodes and treeko will be used if duplication events are found.""")
parser.add_argument("--spname_delimiter", dest="spname_delimiter",
type=str, default="_",
help=("species code delimiter in node names"))
parser.add_argument("--spname_field", dest="spname_field",
type=int, default=-1,
help=("position of the species code extracted from node names. -1 = last field"))
parser.add_argument("--collateral", dest="collateral",
action='store_true',
help=(""))
parser.add_argument("--ref_attr", dest="ref_attr",
type=str,
help=("attribute in ref tree used as leaf name"))
parser.add_argument("--target_attr", dest="target_attr",
type=str,
help=("attribute in target tree used as leaf name"))
args = parser.parse_args(argv)
print __DESCRIPTION__
reftree = args.reftree
if args.targets_file and args.target_trees:
print >>sys.stderr, 'The use of targets_file and targets at the same time is not supported.'
sys.exit(1)
if args.targets_file:
target_trees = tree_iterator(args.targets_file)
else:
target_trees = args.target_trees
t = Tree(reftree)
if args.ref_attr:
for lf in t.iter_leaves():
lf._origname = lf.name
if args.ref_attr not in lf.features:
print lf
lf.name = getattr(lf, args.ref_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = t.get_common_ancestor(args.outgroup)
else:
out = t.search_nodes(name=args.outgroup[0])[0]
t.set_outgroup(out)
ref_names = set(t.get_leaf_names())
reftree_len = len(t)
reftree_edges = (reftree_len*2)-2
ncollapsed_branches = len([n for n in t.traverse() if n.children and n.support < args.min_support])
#reftree_edges -= ncollapsed_branches
#if ncollapsed_branches:
# print '%d branches collapsed in reference tree' %ncollapsed_branches
HEADER = ("target tree", 'dups', 'subtrees', 'used trees', 'treeko', "RF", "maxRF", 'normRF', "%reftree", "%genetree", "avgSize", "minSize", "common tips", "refSize", "targetSize")
if args.output:
OUT = open(args.output, "w")
print >>OUT, '# ' + ctime()
print >>OUT, '# ' + ' '.join(sys.argv)
print >>OUT, '#'+'\t'.join(HEADER)
else:
print '# ' + ctime()
print '# ' + ' '.join(sys.argv)
COL_WIDTHS = [20, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]
print_table([HEADER], fix_col_width=COL_WIDTHS, wrap_style='wrap')
prev_tree = None
for counter, tfile in enumerate(target_trees):
if args.targets_file:
seedid, tfile = tfile
else:
seedid = None
if args.extract_species:
tt = PhyloTree(tfile, sp_naming_function = lambda name: name.split(args.spname_delimiter)[args.spname_field])
else:
tt = Tree(tfile)
if args.target_attr:
for lf in tt.iter_leaves():
lf._origname = lf.name
lf.name = getattr(lf, args.target_attr)
if args.outgroup:
if len(args.outgroup) > 1:
out = tt.get_common_ancestor(args.outgroup)
else:
out = tt.search_nodes(name=args.outgroup[0])[0]
tt.set_outgroup(out)
if args.target_trees:
fname = os.path.basename(tfile)
else:
fname = '%05d' %counter
max_size, min_size, avg_size, common = -1, -1, -1, -1
total_rf, max_rf, norm_rf = -1, -1, -1
treeko_d = -1
ref_branches_in_target, target_branches_in_ref = -1, -1
target_tree_len = -1
used_subtrees = -1
if args.extract_species:
orig_target_size = len(tt)
ntrees, ndups, sp_trees = tt.get_speciation_trees(autodetect_duplications=True, newick_only=True)
if ntrees < 1000:
all_rf = []
ref_found = []
target_found = []
tree_sizes = []
all_max_rf = []
common_names = 0
for subtree_nw in sp_trees:
if seedid and not args.collateral and (seedid not in subtree_nw):
continue
subtree = PhyloTree(subtree_nw, sp_naming_function = lambda name: name.split(args.spname_delimiter)[args.spname_field])
# only necessary if rf function is going to filter by support value. It slows downs the analysis, obviously
if args.min_support:
subtree_content = subtree.get_cached_content(store_attr='name')
for n in subtree.traverse():
if n.children:
n.support = tt.get_common_ancestor(subtree_content[n]).support
rf, maxr, common, p1, p2, d1, d2 = t.robinson_foulds(subtree, expand_polytomies=args.polytomies, unrooted_trees=args.unrooted,
attr_t2='species', min_support_t2=args.min_support)
if maxr > 0 and p1 and p2:
all_rf.append(rf)
tree_sizes.append(len(common))
all_max_rf.append(maxr)
common_names = max(common_names, len(common))
ref_found.append(float(len(p2 & p1)) / reftree_edges)
p2bis = set([p for p in (p2-d2) if len(p[0])>1 and len(p[1])>1]) # valid edges in target not leaves
if p2bis:
incompatible_target_branches = float(len((p2-d2) - p1))
target_found.append(1 - (incompatible_target_branches / (len(p2-d2))))
# valid_target = p2-d2
# valid_ref = p1-d1
# ref_found.append(float(len(valid_target & valid_ref)) / reftree_edges)
# p2bis = set([p for p in (p2-d2) if len(p[0])>1 and len(p[1])>1])
# if p2bis-d2:
# incompatible_target_branches = float(len((p2-d2) - p1))
# target_found.append(1 - (incompatible_target_branches / (len(p2-d2))))
if all_rf:
# Treeko speciation distance
alld = [(all_rf[i]/float(all_max_rf[i])) for i in xrange(len(all_rf))]
a = numpy.sum([alld[i] * tree_sizes[i] for i in xrange(len(all_rf))])
b = float(numpy.sum(tree_sizes))
treeko_d = a/b
total_rf = numpy.mean(all_rf)
norm_rf = numpy.mean([(all_rf[i]/float(all_max_rf[i])) for i in xrange(len(all_rf))])
max_rf = numpy.max(all_max_rf)
ref_branches_in_target = numpy.mean(ref_found)
target_branches_in_ref = numpy.mean(target_found) if target_found else -1
target_tree_len = numpy.mean(tree_sizes)
used_subtrees = len(all_rf)
else:
target_tree_len = len(tt)
ndups, ntrees, used_subtrees = 0, 1, 1
treeko_d = -1
total_rf, max_rf, common, p1, p2, d1, d2 = tt.robinson_foulds(t, expand_polytomies=args.polytomies, unrooted_trees=args.unrooted)
common_names = len(common)
if max_rf:
norm_rf = total_rf / float(max_rf)
if p1 and p2:
sizes = [len(p) for p in p2 ^ p1]
if sizes:
avg_size = sum(sizes) / float(len(sizes))
max_size, min_size = max(sizes), min(sizes)
else:
max_size, min_size, avg_size = 0, 0, 0
ref_branches_in_target = float(len(p2 & p1)) / reftree_edges
#if p2-d2:
# incompatible_target_branches = float(len((p2-d2) - p1))
# target_found.append(1 - (incompatible_target_branches / (len(p2-d2))))
else:
ref_branches_in_target = 0.0
target_branches_in_ref = 0.0
max_size, min_size, avg_size = -1, -1, -1
if args.output:
print >>OUT, '\t'.join(map(str, (fname, ndups, ntrees, used_subtrees, treeko_d, total_rf, max_rf, norm_rf, ref_branches_in_target, target_branches_in_ref,
avg_size, min_size, common_names, reftree_len, target_tree_len)))
else:
print_table([map(istr, (fname[-30:], ndups, ntrees, used_subtrees, treeko_d, total_rf, max_rf, norm_rf, '%0.4f' %ref_branches_in_target, '%0.4f' %target_branches_in_ref,
avg_size, min_size, common_names, reftree_len, target_tree_len))], fix_col_width = COL_WIDTHS, wrap_style='cut')
if args.output:
OUT.close()
