def calculate_robinson_foulds(self, species_tree, gene_tree, weighted):
"""
Calculates the Robinson Foulds distances for weighted and unweighted
trees.
Input:
species_tree -- newick file or newick string containing the species tree
gene_tree -- newick file or newick string containing the tree to
be compared to the species tree
weighted -- boolean parameter for whether the files have weights
Returns:
The weighted and/or unweighted Robinson Foulds distance of the species
tree and input tree.
"""
# taxon names
tns = dendropy.TaxonNamespace()
# Create dendropy tree from species tree input file
if os.path.isfile(species_tree):
species_tree = Tree.get_from_path(species_tree,
'newick',
taxon_namespace=tns)
# Create dendropy tree from species tree input newick string
else:
species_tree = Tree.get_from_string(species_tree,
'newick',
taxon_namespace=tns)
# Create dendropy tree from gene tree input file
if os.path.isfile(gene_tree):
gene_tree = Tree.get_from_path(gene_tree,
'newick',
taxon_namespace=tns)
# Create dendropy tree from gene tree input newick string
else:
gene_tree = Tree.get_from_string(gene_tree,
'newick',
taxon_namespace=tns)
# both weighted and unweighted foulds distance
if weighted:
return treecompare.weighted_robinson_foulds_distance(species_tree, gene_tree), \
treecompare.unweighted_robinson_foulds_distance(species_tree, gene_tree)
# only unweighted foulds distance
else:
return treecompare.unweighted_robinson_foulds_distance(
species_tree, gene_tree)
def collapse_short_analyses(dirstub, num):
inf_dict = {'Euclidean': [], 'RF': []}
for i in range(num):
i += 1
diri = "{}{}".format(dirstub, i)
tns = dendropy.TaxonNamespace()
inputtree = dendropy.Tree.get_from_path(
"{}/scaledtree.tre".format(diri),
schema="newick",
taxon_namespace=tns)
for edge in inputtree.postorder_edge_iter():
if edge.length < 0.0000000001:
edge.collapse()
inferred = dendropy.Tree.get_from_string(trestr,
schema="newick",
taxon_namespace=tns)
for edge in inferredtree.postorder_edge_iter():
if edge.length < 0.0000000001:
edge.collapse()
inputtree.encode_bipartitions()
inferred.encode_bipartitions()
inf_dict['Euclidean'].append(
treecompare.euclidean_distance(inputtree, inferred))
inf_dict['RF'].append(
treecompare.unweighted_robinson_foulds_distance(
inputtree, inferred))
for key in inf_dict:
mean = sum(inf_dict[key]) / len(inf_dict[key])
print(key)
print(mean)
return (inf_dict)
#perform_sims(dirstub = "validation/short_fix/run", refloc = "example/short_ref.fasta")
#perform_analyses("validation/short_fix/run")
def compute_distance(trees, true_tree):
"""
Computes Robinson-Foulds distance between input trees and "true" tree
:param trees: dict of dendropy tree top be compared to the "true" tree
:param true_tree: dentropy tree of the "true" tree
:return: key:value dict where key is filename of tree
"""
distance_dict = {
file: tc.unweighted_robinson_foulds_distance(tree, true_tree)
for file, tree in trees.items()
}
return distance_dict
def calc_rfd_distribution(src_path):
tns = dendropy.TaxonNamespace()
trees = dendropy.TreeList.get(
path=src_path,
schema="nexus")
rf_dists = []
for idx1, t1 in enumerate(trees[:-1]):
for idx2, t2 in enumerate(trees[idx1+1:]):
rfd = treecompare.unweighted_robinson_foulds_distance(t1, t2)
rf_dists.append(rfd)
mean, var = statistics.mean_and_sample_variance(rf_dists)
print("mean = {}, var = {}, 5/95% quantile = {}".format(
mean,
var,
statistics.quantile_5_95(rf_dists)))
def test_special_case1(self):
original_tree_str = """\
[&R] ((((e1:4.25978504749,a0:4.25978504749):9.75100657322,(e5:11.2557415909,c9:11.2557415909):2.75505002977):5.25672273638,(c5:17.0225375511,e6:17.0225375511):2.24497680601):20.9755404109,(((c7:0.0433876754663,e4:0.0433876754663):16.2031718648,(b1:14.1628944123,d7:14.1628944123):2.08366512802):14.3825543479,((((d1:13.4235384066,(d4:7.64533761739,c3:7.64533761739):5.77820078917):2.00948796838,((d8:3.10025757397,b5:3.10025757397):5.07496414931,a4:8.17522172328):7.25780465166):4.52823355379,((((((a7:8.94718577977,(((a1:2.04048640276,c2:2.04048640276):1.45629935083,(e0:0.408302025932,b6:0.408302025932):3.08848372766):3.77714533326,(((c6:2.1238494561,(e8:2.03255428077,d6:2.03255428077):0.0912951753249):2.91822700988,a5:5.04207646598):1.92173681425,((a2:3.43218264885,(b8:0.515232535857,a9:0.515232535857):2.91695011299):1.6832785054,b4:5.11546115425):1.84835212598):0.310117806629):1.67325469292):0.613875266884,(d9:8.93428444448,(c1:5.91732320427,c8:5.91732320427):3.0169612402):0.626776602178):3.65721021136,((c0:3.99662328128,d2:3.99662328128):1.90572648225,(e9:1.84550535315,(b9:0.803660457957,e3:0.803660457957):1.0418448952):4.05684441038):7.31592149449):1.63573163655,d5:14.8540028946):2.25255068893,d3:17.1065535835):2.27795405888,(((a3:4.85356559967,(c4:3.08209866724,d0:3.08209866724):1.77146693244):2.74425153816,e7:7.59781713783):4.22596432824,(b2:2.86856170856,e2:2.86856170856):8.9552197575):7.56072617631):0.576752286338):1.05878660087,((b0:1.6464852541,b7:1.6464852541):10.0630186678,(a8:7.31781944487,(a6:7.13495568605,b3:7.13495568605):0.182863758824):4.39168447703):9.31054260769):9.60906735859):9.61394087983):6.65318140005;
"""
expected_tree_strs = """\
[&R] (a0:40.243054768,((a4:19.9612599287,((a7:8.94718577977,(a1:7.27393108685,(a5:6.96381328023,(a2:3.43218264885,a9:3.43218264885):3.53163063138):0.310117806629):1.67325469292):10.4373218626,a3:19.3845076424):0.576752286338):1.05878660087,(a8:7.31781944487,a6:7.31781944487):13.7022270847):19.2230082384):6.65318140005;
[&R] (b1:30.6291138882,((b5:19.9612599287,(((b6:7.27393108685,(b8:5.11546115425,b4:5.11546115425):2.15846993261):5.94434017116,b9:13.218271258):6.16623638436,b2:19.3845076424):0.576752286338):1.05878660087,((b0:1.6464852541,b7:1.6464852541):10.0630186678,b3:11.7095039219):9.31054260769):9.60906735859):16.2671222799;
[&R] ((c9:19.2675143571,c5:19.2675143571):20.9755404109,(c7:30.6291138882,(c3:19.9612599287,((((c2:7.27393108685,c6:7.27393108685):2.2871299598,(c1:5.91732320427,c8:5.91732320427):3.64373784238):3.65721021136,c0:13.218271258):6.16623638436,c4:19.3845076424):0.576752286338):10.6678539595):9.61394087983):6.65318140005;
[&R] (d7:30.6291138882,(((d1:13.4235384066,d4:13.4235384066):2.00948796838,d8:15.4330263749):4.52823355379,(((((d6:9.56106104665,d9:9.56106104665):3.65721021136,d2:13.218271258):1.63573163655,d5:14.8540028946):2.25255068893,d3:17.1065535835):2.27795405888,d0:19.3845076424):0.576752286338):10.6678539595):16.2671222799;
[&R] (((e1:14.0107916207,e5:14.0107916207):5.25672273638,e6:19.2675143571):20.9755404109,(e4:30.6291138882,(((e0:7.27393108685,e8:7.27393108685):5.94434017116,(e9:1.84550535315,e3:1.84550535315):11.3727659049):6.16623638436,(e7:11.8237814661,e2:11.8237814661):7.56072617631):11.2446062458):9.61394087983):6.65318140005;
"""
tns = dendropy.TaxonNamespace()
source_tree1 = dendropy.Tree.get(
data=original_tree_str,
schema="newick",
taxon_namespace=tns)
source_tree2 = dendropy.Tree.get(
data=original_tree_str,
schema="newick",
taxon_namespace=tns)
self.assertEqual(treecompare.weighted_robinson_foulds_distance(source_tree1, source_tree2), 0.0)
group_ids = ("a", "b", "c", "d", "e")
expected_induced_trees = dendropy.TreeList.get(
data=expected_tree_strs,
schema="newick",
taxon_namespace=tns)
assert len(expected_induced_trees) == len(group_ids)
for group_id, expected_induced_tree in zip(group_ids, expected_induced_trees):
extracted_tree = source_tree1.extract_tree(
node_filter_fn=lambda node: node.taxon.label.startswith(group_id),
is_apply_filter_to_leaf_nodes=True,
is_apply_filter_to_internal_nodes=False)
for leaf_nd in extracted_tree.leaf_node_iter():
self.assertTrue(leaf_nd.taxon.label.startswith(group_id))
for leaf_nd in expected_induced_tree.leaf_node_iter():
assert leaf_nd.taxon.label.startswith(group_id)
# self.assertEqual(treecompare.weighted_robinson_foulds_distance(source_tree1, source_tree2), 0.0)
self.assertEqual(treecompare.unweighted_robinson_foulds_distance(extracted_tree, expected_induced_tree), 0)
self.assertAlmostEqual(treecompare.weighted_robinson_foulds_distance(extracted_tree, expected_induced_tree), 0.0)
def test_special_case2(self):
original_tree_str = """\
[&R] ((a1,(a2,(a3,a4)a0)),(b1,(b2,(b3,b4))));
"""
source_tree1 = dendropy.Tree.get(
data=original_tree_str,
schema="newick",
)
expected_tree_str = """\
[&R] ((a3,a4)a0);
"""
extracted_tree = source_tree1.extract_tree(
# node_filter_fn=lambda node: node.taxon is not None and node.taxon.label.startswith("a"),
node_filter_fn=lambda node: node.taxon.label in set(["a3", "a4"]),
is_apply_filter_to_leaf_nodes=True,
is_apply_filter_to_internal_nodes=False,
)
expected_tree = dendropy.Tree.get(
data=expected_tree_str,
schema="newick",
taxon_namespace=source_tree1.taxon_namespace)
self.assertEqual(treecompare.unweighted_robinson_foulds_distance(extracted_tree, expected_tree), 0.0)
import dendropy
from dendropy.calculate import treecompare
from dendropy import Tree
import os
protein_dir_set = []
for i in os.listdir('output'):
if "nex" in i.split("."):
protein_dir_set.append(str(i))
tns = dendropy.TaxonNamespace()
for i in range(0, len(protein_dir_set)):
for j in range(i + 1, len(protein_dir_set)):
tree1 = Tree.get_from_path("output/" + protein_dir_set[i],
"nexus",
taxon_namespace=tns)
tree2 = Tree.get_from_path("output/" + protein_dir_set[j],
"nexus",
taxon_namespace=tns)
tree1.encode_bipartitions()
tree2.encode_bipartitions()
print(protein_dir_set[i], protein_dir_set[j],
treecompare.unweighted_robinson_foulds_distance(tree1, tree2))
def topology_counter(self, rooted=False, outgroup=None):
"""
Counts the number of times that each topology appears as outputted by
running RAxML.
Output:
topologies_to_counts --- a dictionary mapping topologies to the number of times they appear
"""
# Initialize a dictionary mapping newick strings to unique topologies
unique_topologies_to_newicks = {}
# taxon names
tns = dendropy.TaxonNamespace()
# Create a set of unique topologies
unique_topologies = set([])
# Get the topology files from the "Topologies" folder
input_directory = "Topologies"
# Initialize topology_count to a defaultdict
topologies_to_counts = defaultdict(int)
# Iterate over each file in the given directory
for filename in os.listdir(input_directory):
# Create a boolean flag for determining the uniqueness of tree
new_tree_is_unique = True
# If file is the file with the best tree newick string
if os.path.splitext(filename)[0] == "Topology_bestTree":
input_file = os.path.join(input_directory, filename)
new_tree = Tree.get_from_path(input_file,
'newick',
taxon_namespace=tns)
if rooted:
outgroup_node = new_tree.find_node_with_taxon_label(
outgroup)
new_tree.to_outgroup_position(outgroup_node,
update_bipartitions=False)
# Iterate over each topology in unique_topologies
for unique_topology in unique_topologies:
# Create a tree for each of the unique topologies calculate RF distance compared to new_tree
unique_tree = Tree.get_from_string(unique_topology,
'newick',
taxon_namespace=tns)
rf_distance = treecompare.unweighted_robinson_foulds_distance(
unique_tree, new_tree)
# If the RF distance is 0 then the new tree is the same as one of the unique topologies
if rf_distance == 0:
topologies_to_counts[unique_topology] += 1
new_tree_is_unique = False
new_tree = new_tree.as_string("newick").replace(
"\n", "")
unique_topologies_to_newicks[unique_topology].add(
new_tree)
break
# If the new tree is a unique tree add it to the set of unique topologies
if new_tree_is_unique:
new_tree = new_tree.as_string("newick").replace("\n", "")
unique_topologies.add(new_tree)
topologies_to_counts[new_tree] += 1
unique_topologies_to_newicks[new_tree] = set([new_tree])
return topologies_to_counts, unique_topologies_to_newicks
def assert_equal_trees(self, t0, t1):
self.assertEqual(
treecompare.unweighted_robinson_foulds_distance(t0, t1), 0)
self.assertAlmostEqual(
treecompare.weighted_robinson_foulds_distance(t0, t1), 0, 8)
def assert_equal_trees(self, t0, t1):
self.assertEqual(treecompare.unweighted_robinson_foulds_distance(t0, t1), 0)
self.assertAlmostEqual(treecompare.weighted_robinson_foulds_distance(t0, t1), 0, 8)
def get_figure(software1, software2, output_path):
"""
This function generates a comparison figure of trees generated from software 1 and software 2
:param software1: name of the software
:param software2: name of the software
:param output_path: path to where the output figure will be saved
:return: NA
"""
map = {
1: "PB2",
2: "PB1",
3: "PA",
4: "HA",
5: "NP",
6: "NA",
7: "MP",
8: "NS",
9: "concatenated"
}
wRF = []
uwRF = []
eD = []
for num1 in range(1, 10):
w = []
u = []
e = []
for num2 in range(1, 10):
segment1 = map[num1]
segment2 = map[num2]
groundTruthFile = get_file(software1, segment1)
estimationFile = get_file(software2, segment2)
tns = dendropy.TaxonNamespace()
gtTree = dendropy.Tree.get(file=open(groundTruthFile, 'r'),
schema='newick',
taxon_namespace=tns)
estimateTree = dendropy.Tree.get(file=open(estimationFile, 'r'),
schema='newick',
taxon_namespace=tns)
# metrics, weighted RF is unsymmetric, unweighted RF is symmetric distance
weightedRF = treecompare.weighted_robinson_foulds_distance(
gtTree, estimateTree)
unweightedRF = treecompare.unweighted_robinson_foulds_distance(
gtTree, estimateTree)
euclideanDist = treecompare.euclidean_distance(
gtTree, estimateTree)
w.append(weightedRF)
u.append(unweightedRF)
e.append(euclideanDist)
wRF.append(w)
uwRF.append(u)
eD.append(e)
wRF = np.array(wRF)
uwRF = np.array(uwRF)
eD = np.array(eD)
metric_map = {
"Weighted Robinson Foulds": wRF,
"Unweighted Robinson Foulds": uwRF,
"Euclidean Distances": eD
}
for metric in [
"Weighted Robinson Foulds", "Unweighted Robinson Foulds",
"Euclidean Distances"
]:
fig, ax = plt.subplots()
im, cbar = heatmap(metric_map[metric],
software1,
software2,
ax=ax,
cmap="YlGn",
cbarlabel="Distance")
texts = annotate_heatmap(im, valfmt="{x:.2f}")
title = "%s on %s and %s Tree" % (metric, software1.capitalize(),
software2.capitalize())
ax.set_title(title, pad=-330)
fig.tight_layout()
# save figure to output path
plt.savefig(output_path)
import dendropy
from dendropy.calculate.treecompare import \
unweighted_robinson_foulds_distance, euclidean_distance, false_positives_and_negatives
if __name__ == "__main__":
usage = "Compute Robinson-Foulds distance between two trees in NEWICK format.\n\
Usage: python bin/compute_tree_dist.sh [First tree in newick format] [Second tree in newick format]\n\
Example: python bin/compute_tree_dist.sh 1.tree 2.tree"
if len(sys.argv) != 3 or sys.argv[1] == "-h" or sys.argv[1] == "--help":
print(usage, file=sys.stderr)
sys.exit(1)
# Parse arguments
tree_files = [os.path.abspath(x) for x in sys.argv[1:]]
# Read trees
tns = dendropy.TaxonNamespace()
trees = tuple([
dendropy.Tree.get(path=x, schema="newick", taxon_namespace=tns)
for x in tree_files
])
print(unweighted_robinson_foulds_distance(*trees))
#print(euclidean_distance(*trees))
# d = false_positives_and_negatives(*trees)
# print(np.sqrt(np.float(d[0])*np.float(d[1])))
sys.exit(0)
def perform_analyses(dirstub, num):
inf_dict = {
'Euclidean': [],
'RF': [],
'MutsSim': [],
'MutsCalled': [],
'freqA': [],
'freqC': [],
'freqG': [],
'freqT': [],
'ac': [],
'ag': [],
'at': [],
'cg': [],
'ct': [],
'gt': []
}
for i in range(num):
i += 1
diri = "{}{}".format(dirstub, i)
#diri = "{}{}/altref".format(dirstub,i)
cwd = os.getcwd()
os.chdir(diri)
os.system(
"raxmlHPC -m ASC_GTRGAMMA --asc-corr=lewis -s snpma.fasta -p 1 -n val"
)
# os.system("raxmlHPC -m GTRGAMMA -s snpma.fasta -p 1 -n val_noasc")
os.chdir(cwd)
print diri
trestr = open("{}/RAxML_bestTree.val".format(diri)).readline().replace(
'sim_', '')
# trestr =open("{}/RAxML_bestTree.val_noasc".format(diri)).readline().replace('sim_','')
tns = dendropy.TaxonNamespace()
inputtree = dendropy.Tree.get_from_path(
"{}/scaledtree.tre".format(diri),
schema="newick",
taxon_namespace=tns)
inferred = dendropy.Tree.get_from_string(trestr,
schema="newick",
taxon_namespace=tns)
inputtree.encode_bipartitions()
inferred.encode_bipartitions()
inf_dict['Euclidean'].append(
treecompare.euclidean_distance(inputtree, inferred))
inf_dict['RF'].append(
treecompare.unweighted_robinson_foulds_distance(
inputtree, inferred))
freqs = subprocess.check_output(
["grep", "Base frequencies:",
"{}/RAxML_info.val".format(diri)]).split()
# freqs = subprocess.check_output(["grep", "Base frequencies:","{}/RAxML_info.val_noasc".format(diri)]).split()
print(freqs)
freqs = [float(val) for val in freqs[2:]]
inf_dict['freqA'].append(freqs[0])
inf_dict['freqC'].append(freqs[1])
inf_dict['freqG'].append(freqs[2])
inf_dict['freqT'].append(freqs[3])
trans = subprocess.check_output(
["grep", "ac ag at cg ct gt",
"{}/RAxML_info.val".format(diri)]).split()
# trans = subprocess.check_output(["grep", "ac ag at cg ct gt", "{}/RAxML_info.val_noasc".format(diri)]).split()
trans = [float(val) for val in trans[9:]]
inf_dict['ac'].append(trans[0])
inf_dict['ag'].append(trans[1])
inf_dict['at'].append(trans[2])
inf_dict['cg'].append(trans[3])
inf_dict['ct'].append(trans[4])
inf_dict['gt'].append(trans[5])
inf_dict['MutsCalled'].append(
float(
subprocess.check_output(["wc", "{}/snplist.txt".format(diri)
]).split()[0]))
inf_dict['MutsSim'].append(
float(
subprocess.check_output(["wc", "{}/mutsites.txt".format(diri)
]).split()[0]))
for key in inf_dict:
mean = sum(inf_dict[key]) / len(inf_dict[key])
print(key)
print(mean)
return (inf_dict)