def run(self, input_tree, output_prefix, plot_taxa_file, trusted_taxa_file,
min_children, min_support):
"""Determine distribution of taxa at each taxonomic rank.
Parameters
----------
input_tree : str
Name of input tree.
output_prefix : str
Desired prefix for generated files.
plot_taxa_file : str
File specifying taxa to plot. Set to None to consider all taxa.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
min_support : float
Only consider taxa with at least this level of support when inferring distribution.
"""
# read tree
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# read taxa to plot
taxa_to_plot = None
if plot_taxa_file:
taxa_to_plot = read_taxa_file(plot_taxa_file)
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(
tree, trusted_taxa, min_children, min_support)
# calculate relative distance to taxa
rd = RelativeDistance()
rel_dists = rd.rel_dist_to_named_clades(tree, taxa_to_plot)
# report number of taxa at each rank
print ''
print ' Number of taxa plotted at each taxonomic rank:'
for rank, taxa in rel_dists.iteritems():
print ' %s\t%d' % (Taxonomy.rank_labels[rank], len(taxa))
# create performance plots
rel_dist_thresholds = self._percent_correct_plot(
rel_dists, taxa_for_dist_inference, output_prefix)
# create distribution plot
distribution_table = output_prefix + '.tsv'
plot_file = output_prefix + '.png'
self._distribution_plot(rel_dists, rel_dist_thresholds,
taxa_for_dist_inference, distribution_table,
plot_file)
def run(self, input_tree, output_prefix, plot_taxa_file, trusted_taxa_file, min_children, min_support):
"""Determine distribution of taxa at each taxonomic rank.
Parameters
----------
input_tree : str
Name of input tree.
output_prefix : str
Desired prefix for generated files.
plot_taxa_file : str
File specifying taxa to plot. Set to None to consider all taxa.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
min_support : float
Only consider taxa with at least this level of support when inferring distribution.
"""
# read tree
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# read taxa to plot
taxa_to_plot = None
if plot_taxa_file:
taxa_to_plot = read_taxa_file(plot_taxa_file)
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(tree, trusted_taxa, min_children, min_support)
# calculate relative distance to taxa
rd = RelativeDistance()
rel_dists = rd.rel_dist_to_named_clades(tree, taxa_to_plot)
# report number of taxa at each rank
print ''
print ' Number of taxa plotted at each taxonomic rank:'
for rank, taxa in rel_dists.iteritems():
print ' %s\t%d' % (Taxonomy.rank_labels[rank], len(taxa))
# create performance plots
rel_dist_thresholds = self._percent_correct_plot(rel_dists, taxa_for_dist_inference, output_prefix)
# create distribution plot
distribution_table = output_prefix + '.tsv'
plot_file = output_prefix + '.png'
self._distribution_plot(rel_dists, rel_dist_thresholds, taxa_for_dist_inference, distribution_table, plot_file)
def _median_rank_rd(self,
tree,
placed_taxon,
taxonomy,
trusted_taxa_file,
min_children,
min_support):
"""Calculate median relative divergence to each node and thresholds for each taxonomic rank.
Parameters
----------
tree : Tree
Dendropy Tree.
placed_taxon : set
Taxon currently placed in tree which can be used for relative divergence inference.
taxonomy: d[taxon_id] -> taxonomy info
Taxonomic information for extant taxa.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
min_support : float
Only consider taxa with at least this level of support when inferring distribution.
Returns
-------
d[rank_index] -> float
Median relative divergence for each taxonomic rank.
"""
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(tree,
taxonomy,
trusted_taxa,
min_children,
min_support)
taxa_for_dist_inference.intersection_update(placed_taxon)
# infer distribution
outliers = Outliers()
phylum_rel_dists, rel_node_dists = outliers.median_rd_over_phyla(tree,
taxa_for_dist_inference,
taxonomy)
median_for_rank = outliers.rank_median_rd(phylum_rel_dists,
taxa_for_dist_inference)
# set edge lengths to median value over all rootings
tree.seed_node.rel_dist = 0.0
for n in tree.preorder_node_iter(lambda n: n != tree.seed_node):
n.rel_dist = np_median(rel_node_dists[n.id])
return median_for_rank
def _median_rank_rd(self,
tree,
placed_taxon,
taxonomy,
trusted_taxa_file,
min_children,
min_support):
"""Calculate median relative divergence to each node and thresholds for each taxonomic rank.
Parameters
----------
tree : Tree
Dendropy Tree.
placed_taxon : set
Taxon currently placed in tree which can be used for relative divergence inference.
taxonomy: d[taxon_id] -> taxonomy info
Taxonomic information for extant taxa.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
min_support : float
Only consider taxa with at least this level of support when inferring distribution.
Returns
-------
d[rank_index] -> float
Median relative divergence for each taxonomic rank.
"""
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(tree,
taxonomy,
trusted_taxa,
min_children,
min_support)
taxa_for_dist_inference.intersection_update(placed_taxon)
# infer distribution
outliers = Outliers()
phylum_rel_dists, rel_node_dists = outliers.median_rd_over_phyla(tree,
taxa_for_dist_inference,
taxonomy)
median_for_rank = outliers.rank_median_rd(phylum_rel_dists,
taxa_for_dist_inference)
# set edge lengths to median value over all rootings
tree.seed_node.rel_dist = 0.0
for n in tree.preorder_node_iter(lambda n: n != tree.seed_node):
n.rel_dist = np_median(rel_node_dists[n.id])
return median_for_rank
def run(self, input_tree, trusted_taxa_file, min_children, taxonomy_file,
output_dir):
"""Calculate distribution of branch lengths at each taxonomic rank.
Parameters
----------
input_tree : str
Name of input tree.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
taxonomy_file : str
File containing taxonomic information for leaf nodes (if NULL, read taxonomy from tree).
output_dir : str
Desired output directory.
"""
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
input_tree_name = os.path.splitext(os.path.basename(input_tree))[0]
# pull taxonomy from tree
if not taxonomy_file:
self.logger.info('Reading taxonomy from tree.')
taxonomy_file = os.path.join(output_dir,
'%s.taxonomy.tsv' % input_tree_name)
taxonomy = Taxonomy().read_from_tree(input_tree)
Taxonomy().write(taxonomy, taxonomy_file)
else:
self.logger.info('Reading taxonomy from file.')
taxonomy = Taxonomy().read(taxonomy_file)
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(
tree, taxonomy, set(), min_children, -1)
# determine branch lengths to leaves for named lineages
rank_bl_dist = defaultdict(list)
taxa_bl_dist = defaultdict(list)
taxa_at_rank = defaultdict(list)
for node in tree.postorder_node_iter():
if node.is_leaf() or not node.label:
continue
_support, taxon, _auxiliary_info = parse_label(node.label)
if not taxon:
continue
# get most specific rank in multi-rank taxa string
taxa = [t.strip() for t in taxon.split(';')]
taxon = taxa[-1]
most_specific_rank = taxon[0:3]
taxa_at_rank[Taxonomy.rank_index[most_specific_rank]].append(taxon)
for n in node.leaf_iter():
dist_to_node = self._dist_to_ancestor(n, node)
for t in taxa:
taxa_bl_dist[t].append(dist_to_node)
rank = Taxonomy.rank_labels[
Taxonomy.rank_index[most_specific_rank]]
if rank != 'species' or Taxonomy().validate_species_name(taxon):
if taxon in taxa_for_dist_inference:
rank_bl_dist[rank].append(np_mean(taxa_bl_dist[taxon]))
# report number of taxa at each rank
print('')
print('Rank\tTaxa\tTaxa for Inference')
for rank, taxa in taxa_at_rank.items():
taxa_for_inference = [
x for x in taxa if x in taxa_for_dist_inference
]
print('%s\t%d\t%d' % (Taxonomy.rank_labels[rank], len(taxa),
len(taxa_for_inference)))
print('')
# report results sorted by rank
sorted_taxon = []
for rank_prefix in Taxonomy.rank_prefixes:
taxa_at_rank = []
for taxon in taxa_bl_dist:
if taxon.startswith(rank_prefix):
taxa_at_rank.append(taxon)
sorted_taxon += sorted(taxa_at_rank)
# report results for each named group
taxa_file = os.path.join(output_dir,
'%s.taxa_bl_dist.tsv' % input_tree_name)
fout = open(taxa_file, 'w')
fout.write(
'Taxa\tUsed for Inference\tMean\tStd\t5th\t10th\t50th\t90th\t95th\n'
)
for taxon in sorted_taxon:
dist = taxa_bl_dist[taxon]
p = np_percentile(dist, [5, 10, 50, 90, 95])
fout.write(
'%s\t%s\t%g\t%g\t%g\t%g\t%g\t%g\t%g\n' %
(taxon, str(taxon in taxa_for_dist_inference), np_mean(dist),
np_std(dist), p[0], p[1], p[2], p[3], p[4]))
fout.close()
# report results for each taxonomic rank
rank_file = os.path.join(output_dir,
'%s.rank_bl_dist.tsv' % input_tree_name)
fout = open(rank_file, 'w')
fout.write('Rank\tMean\tStd\t5th\t10th\t50th\t90th\t95th\n')
for rank in Taxonomy.rank_labels:
dist = rank_bl_dist[rank]
p = np_percentile(dist, [5, 10, 50, 90, 95])
fout.write('%s\t%g\t%g\t%g\t%g\t%g\t%g\t%g\n' %
(rank, np_mean(dist), np_std(dist), p[0], p[1], p[2],
p[3], p[4]))
fout.close()
# report results for each node
output_bl_file = os.path.join(output_dir,
'%s.node_bl_dist.tsv' % input_tree_name)
self._write_bl_dist(tree, output_bl_file)
def run(self, input_tree,
taxonomy_file,
output_dir,
plot_taxa_file,
plot_dist_taxa_only,
plot_domain,
trusted_taxa_file,
fixed_root,
min_children,
min_support,
verbose_table):
"""Determine distribution of taxa at each taxonomic rank.
Parameters
----------
input_tree : str
Name of input tree.
taxonomy_file : str
File with taxonomy strings for each taxa.
output_dir : str
Desired output directory.
plot_taxa_file : str
File specifying taxa to plot. Set to None to consider all taxa.
plot_dist_taxa_only : boolean
Only plot the taxa used to infer distribution.
plot_domain : boolean
Plot domain rank.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
fixed_root : boolean
Usa a single fixed root to infer outliers.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
min_support : float
Only consider taxa with at least this level of support when inferring distribution.
verbose_table : boolean
Print additional columns in output table.
"""
# read tree
self.logger.info('Reading tree.')
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
input_tree_name = os.path.splitext(os.path.basename(input_tree))[0]
# pull taxonomy from tree
if not taxonomy_file:
self.logger.info('Reading taxonomy from tree.')
taxonomy_file = os.path.join(output_dir, '%s.taxonomy.tsv' % input_tree_name)
taxonomy = Taxonomy().read_from_tree(input_tree)
Taxonomy().write(taxonomy, taxonomy_file)
else:
self.logger.info('Reading taxonomy from file.')
taxonomy = Taxonomy().read(taxonomy_file)
gtdb_parent_ranks = Taxonomy().parents(taxonomy)
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(tree, taxonomy, trusted_taxa, min_children, min_support)
# limit plotted taxa
taxa_to_plot = None
if plot_dist_taxa_only:
taxa_to_plot = taxa_for_dist_inference
elif plot_taxa_file:
taxa_to_plot = read_taxa_file(plot_taxa_file)
# check if a single fixed root should be used
if fixed_root:
self.logger.info('Using single fixed rooting for inferring distributions.')
rel_dists = self.rd_fixed_root(tree, taxa_for_dist_inference)
# create fixed rooting style tables and plots
distribution_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
plot_file = os.path.join(output_dir, '%s.png' % input_tree_name)
self._distribution_plot(rel_dists, taxa_for_dist_inference, distribution_table, plot_file)
median_outlier_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
self._median_outlier_file(rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table)
else:
# calculate relative distance to taxa
rd = RelativeDistance()
rel_dists = rd.rel_dist_to_named_clades(tree)
# report number of taxa at each rank
print('')
print('Rank\tTaxa to Plot\tTaxa for Inference')
for rank, taxa in rel_dists.items():
taxa_for_inference = [x for x in taxa if x in taxa_for_dist_inference]
print('%s\t%d\t%d' % (Taxonomy.rank_labels[rank], len(taxa), len(taxa_for_inference)))
print('')
phylum_rel_dists, rel_node_dists = self.median_rd_over_phyla(tree,
taxa_for_dist_inference,
taxonomy)
# set edge lengths to median value over all rootings
tree.seed_node.rel_dist = 0.0
for n in tree.preorder_node_iter(lambda n: n != tree.seed_node):
n.rel_dist = np_median(rel_node_dists[n.id])
rd_to_parent = n.rel_dist - n.parent_node.rel_dist
if rd_to_parent < 0:
self.logger.warning('Not all branches are positive after scaling.')
n.edge_length = rd_to_parent
for phylum, rel_dists in phylum_rel_dists.items():
phylum_dir = os.path.join(output_dir, phylum)
if not os.path.exists(phylum_dir):
os.makedirs(phylum_dir)
# create distribution plot
distribution_table = os.path.join(phylum_dir, '%s.rank_distribution.tsv' % phylum)
plot_file = os.path.join(phylum_dir, '%s.rank_distribution.png' % phylum)
self._distribution_plot(rel_dists, taxa_for_dist_inference, distribution_table, plot_file)
median_outlier_table = os.path.join(phylum_dir, '%s.median_outlier.tsv' % phylum)
self._median_outlier_file(rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table)
plot_file = os.path.join(output_dir, '%s.png' % input_tree_name)
self._distribution_summary_plot(phylum_rel_dists, taxa_for_dist_inference, plot_file)
median_outlier_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
median_rank_file = os.path.join(output_dir, '%s.dict' % input_tree_name)
self._median_summary_outlier_file(phylum_rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table,
median_rank_file,
verbose_table)
output_rd_file = os.path.join(output_dir, '%s.node_rd.tsv' % input_tree_name)
self._write_rd(tree, output_rd_file)
output_tree = os.path.join(output_dir, '%s.scaled.tree' % input_tree_name)
tree.write_to_path(output_tree,
schema='newick',
suppress_rooting=True,
unquoted_underscores=True)
def run(self, input_tree,
taxonomy_file,
output_dir,
plot_taxa_file,
plot_dist_taxa_only,
plot_domain,
highlight_polyphyly,
highlight_taxa_file,
trusted_taxa_file,
fixed_root,
min_children,
min_support,
mblet,
fmeasure_table,
min_fmeasure,
fmeasure_mono,
verbose_table):
"""Determine distribution of taxa at each taxonomic rank.
Parameters
----------
input_tree : str
Name of input tree.
taxonomy_file : str
File with taxonomy strings for each taxa.
output_dir : str
Desired output directory.
plot_taxa_file : str
File specifying taxa to plot. Set to None to consider all taxa.
plot_dist_taxa_only : boolean
Only plot the taxa used to infer distribution.
plot_domain : boolean
Plot domain rank.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
fixed_root : boolean
Usa a single fixed root to infer outliers.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
min_support : float
Only consider taxa with at least this level of support when inferring distribution.
verbose_table : boolean
Print additional columns in output table.
"""
# read tree
self.logger.info('Reading tree.')
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
input_tree_name = os.path.splitext(os.path.basename(input_tree))[0]
# pull taxonomy from tree and file
self.logger.info('Reading taxonomy.')
taxonomy = Taxonomy().read(taxonomy_file)
tree_taxonomy = Taxonomy().read_from_tree(input_tree,
warnings=False)
gtdb_parent_ranks = Taxonomy().parents(tree_taxonomy)
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# read F-measure for taxa
fmeasure = None
if fmeasure_table:
fmeasure = self.read_fmeasure(fmeasure_table)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(tree,
taxonomy,
trusted_taxa,
min_children,
min_support,
fmeasure,
min_fmeasure)
# limit plotted taxa
taxa_to_plot = None
if plot_dist_taxa_only:
taxa_to_plot = taxa_for_dist_inference
elif plot_taxa_file:
taxa_to_plot = read_taxa_file(plot_taxa_file)
else:
# plot every taxon defined in tree
taxa_to_plot = set()
for node in tree.preorder_node_iter():
support, taxon, _auxiliary_info = parse_label(node.label)
if taxon:
taxon = taxon.split(';')[-1].strip() # get most specific taxon from compound names
# (e.g. p__Armatimonadetes; c__Chthonomonadetes)
taxa_to_plot.add(taxon)
if False:
# HACK FOR NCBI: only plot taxa with >= 2 taxa
taxa_to_plot = set()
for node in tree.preorder_node_iter():
if not node.label or node.is_leaf():
continue
support, taxon, _auxiliary_info = parse_label(node.label)
if not taxon:
continue
taxon = taxon.split(';')[-1].strip() # get most specific taxon from compound names
# (e.g. p__Armatimonadetes; c__Chthonomonadetes)
# count number of subordinate children
rank_prefix = taxon[0:3]
if min_children > 0 and rank_prefix != 's__':
child_rank_index = Taxonomy().rank_index[rank_prefix] + 1
child_rank_prefix = Taxonomy.rank_prefixes[child_rank_index]
subordinate_taxa = set()
for leaf in node.leaf_iter():
taxa = taxonomy.get(leaf.taxon.label, Taxonomy.rank_prefixes)
if len(taxa) > child_rank_index:
sub_taxon = taxa[child_rank_index]
if sub_taxon != Taxonomy.rank_prefixes[child_rank_index] and sub_taxon.startswith(child_rank_prefix):
subordinate_taxa.add(sub_taxon)
if len(subordinate_taxa) < min_children:
continue
taxa_to_plot.add(taxon)
# highlight taxa
highlight_taxa = set()
if highlight_taxa_file:
for line in open(highlight_taxa_file):
highlight_taxa.add(line.strip().split('\t')[0])
# check if a single fixed root should be used
if fixed_root or mblet:
self.logger.info('Using single fixed rooting for inferring distributions.')
if not mblet:
rel_dists = self.rd_fixed_root(tree, taxa_for_dist_inference)
else:
rel_dists = self.mblet(tree, taxa_for_dist_inference)
# create fixed rooting style tables and plots
distribution_table = os.path.join(output_dir, '%s.rank_distribution.tsv' % input_tree_name)
plot_file = os.path.join(output_dir, '%s.png' % input_tree_name)
self._distribution_plot(rel_dists,
taxa_for_dist_inference,
highlight_polyphyly,
highlight_taxa,
distribution_table,
fmeasure,
fmeasure_mono,
plot_file)
median_outlier_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
self._median_outlier_file(rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table)
else:
# calculate relative distance to taxa
rd = RelativeDistance()
rel_dists = rd.rel_dist_to_named_clades(tree)
# restrict to taxa of interest
if taxa_to_plot:
for r in rel_dists:
for k in set(rel_dists[r].keys()) - set(taxa_to_plot):
del rel_dists[r][k]
# report number of taxa at each rank
print ''
print 'Rank\tTaxa to Plot\tTaxa for Inference'
for rank, taxa in rel_dists.iteritems():
taxa_for_inference = [x for x in taxa if x in taxa_for_dist_inference]
print '%s\t%d\t%d' % (Taxonomy.rank_labels[rank], len(taxa), len(taxa_for_inference))
print ''
# *** determine phyla for inferring distribution
if True:
phylum_rel_dists, rel_node_dists = self.median_rd_over_phyla(tree,
taxa_for_dist_inference)
else:
phyla_for_inference = filter_taxa_for_dist_inference(tree,
taxonomy,
trusted_taxa,
2,
min_support,
fmeasure,
min_fmeasure)
phylum_rel_dists, rel_node_dists = self.median_rd_over_phyla(tree,
phyla_for_inference)
print ''
print 'Phyla for RED Inference:'
print ','.join(phylum_rel_dists)
phyla_file = os.path.join(output_dir, '%s.phyla.tsv' % input_tree_name)
fout = open(phyla_file, 'w')
for p in phylum_rel_dists:
fout.write(p + '\n')
fout.close()
# set edge lengths to median value over all rootings
tree.seed_node.rel_dist = 0.0
for n in tree.preorder_node_iter(lambda n: n != tree.seed_node):
n.rel_dist = np_median(rel_node_dists[n.id])
rd_to_parent = n.rel_dist - n.parent_node.rel_dist
if rd_to_parent < 0:
self.logger.warning('Not all branches are positive after scaling.')
n.edge_length = rd_to_parent
for phylum, rel_dists in phylum_rel_dists.iteritems():
phylum_dir = os.path.join(output_dir, phylum)
if not os.path.exists(phylum_dir):
os.makedirs(phylum_dir)
# restrict to taxa of interest
if taxa_to_plot:
for r in rel_dists:
for k in set(rel_dists[r].keys()) - set(taxa_to_plot):
del rel_dists[r][k]
# create distribution plot
distribution_table = os.path.join(phylum_dir, '%s.rank_distribution.tsv' % phylum)
plot_file = os.path.join(phylum_dir, '%s.rank_distribution.png' % phylum)
self._distribution_plot(rel_dists,
taxa_for_dist_inference,
highlight_polyphyly,
highlight_taxa,
distribution_table,
fmeasure,
fmeasure_mono,
plot_file)
median_outlier_table = os.path.join(phylum_dir, '%s.median_outlier.tsv' % phylum)
self._median_outlier_file(rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table)
plot_file = os.path.join(output_dir, '%s.png' % input_tree_name)
self._distribution_summary_plot(phylum_rel_dists,
taxa_for_dist_inference,
highlight_polyphyly,
highlight_taxa,
fmeasure,
fmeasure_mono,
plot_file)
median_outlier_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
median_rank_file = os.path.join(output_dir, '%s.dict' % input_tree_name)
self._median_summary_outlier_file(phylum_rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table,
median_rank_file,
verbose_table)
output_rd_file = os.path.join(output_dir, '%s.node_rd.tsv' % input_tree_name)
self._write_rd(tree, output_rd_file)
output_tree = os.path.join(output_dir, '%s.scaled.tree' % input_tree_name)
tree.write_to_path(output_tree,
schema='newick',
suppress_rooting=True,
unquoted_underscores=True)
def run(self, input_tree,
taxonomy_file,
output_dir,
plot_taxa_file,
plot_dist_taxa_only,
plot_domain,
trusted_taxa_file,
fixed_root,
min_children,
min_support,
verbose_table):
"""Determine distribution of taxa at each taxonomic rank.
Parameters
----------
input_tree : str
Name of input tree.
taxonomy_file : str
File with taxonomy strings for each taxa.
output_dir : str
Desired output directory.
plot_taxa_file : str
File specifying taxa to plot. Set to None to consider all taxa.
plot_dist_taxa_only : boolean
Only plot the taxa used to infer distribution.
plot_domain : boolean
Plot domain rank.
trusted_taxa_file : str
File specifying trusted taxa to consider when inferring distribution. Set to None to consider all taxa.
fixed_root : boolean
Usa a single fixed root to infer outliers.
min_children : int
Only consider taxa with at least the specified number of children taxa when inferring distribution.
min_support : float
Only consider taxa with at least this level of support when inferring distribution.
verbose_table : boolean
Print additional columns in output table.
"""
# read tree
self.logger.info('Reading tree.')
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
input_tree_name = os.path.splitext(os.path.basename(input_tree))[0]
# pull taxonomy from tree
if not taxonomy_file:
self.logger.info('Reading taxonomy from tree.')
taxonomy_file = os.path.join(output_dir, '%s.taxonomy.tsv' % input_tree_name)
taxonomy = Taxonomy().read_from_tree(input_tree)
Taxonomy().write(taxonomy, taxonomy_file)
else:
self.logger.info('Reading taxonomy from file.')
taxonomy = Taxonomy().read(taxonomy_file)
gtdb_parent_ranks = Taxonomy().parents(taxonomy)
# read trusted taxa
trusted_taxa = None
if trusted_taxa_file:
trusted_taxa = read_taxa_file(trusted_taxa_file)
# determine taxa to be used for inferring distribution
taxa_for_dist_inference = filter_taxa_for_dist_inference(tree, taxonomy, trusted_taxa, min_children, min_support)
# limit plotted taxa
taxa_to_plot = None
if plot_dist_taxa_only:
taxa_to_plot = taxa_for_dist_inference
elif plot_taxa_file:
taxa_to_plot = read_taxa_file(plot_taxa_file)
# check if a single fixed root should be used
if fixed_root:
self.logger.info('Using single fixed rooting for inferring distributions.')
rel_dists = self.rd_fixed_root(tree, taxa_for_dist_inference)
# create fixed rooting style tables and plots
distribution_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
plot_file = os.path.join(output_dir, '%s.png' % input_tree_name)
self._distribution_plot(rel_dists, taxa_for_dist_inference, distribution_table, plot_file)
median_outlier_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
self._median_outlier_file(rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table)
else:
# calculate relative distance to taxa
rd = RelativeDistance()
rel_dists = rd.rel_dist_to_named_clades(tree)
# report number of taxa at each rank
print ''
print 'Rank\tTaxa to Plot\tTaxa for Inference'
for rank, taxa in rel_dists.iteritems():
taxa_for_inference = [x for x in taxa if x in taxa_for_dist_inference]
print '%s\t%d\t%d' % (Taxonomy.rank_labels[rank], len(taxa), len(taxa_for_inference))
print ''
phylum_rel_dists, rel_node_dists = self.median_rd_over_phyla(tree,
taxa_for_dist_inference,
taxonomy)
# set edge lengths to median value over all rootings
tree.seed_node.rel_dist = 0.0
for n in tree.preorder_node_iter(lambda n: n != tree.seed_node):
n.rel_dist = np_median(rel_node_dists[n.id])
rd_to_parent = n.rel_dist - n.parent_node.rel_dist
if rd_to_parent < 0:
self.logger.warning('Not all branches are positive after scaling.')
n.edge_length = rd_to_parent
for phylum, rel_dists in phylum_rel_dists.iteritems():
phylum_dir = os.path.join(output_dir, phylum)
if not os.path.exists(phylum_dir):
os.makedirs(phylum_dir)
# create distribution plot
distribution_table = os.path.join(phylum_dir, '%s.rank_distribution.tsv' % phylum)
plot_file = os.path.join(phylum_dir, '%s.rank_distribution.png' % phylum)
self._distribution_plot(rel_dists, taxa_for_dist_inference, distribution_table, plot_file)
median_outlier_table = os.path.join(phylum_dir, '%s.median_outlier.tsv' % phylum)
self._median_outlier_file(rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table)
plot_file = os.path.join(output_dir, '%s.png' % input_tree_name)
self._distribution_summary_plot(phylum_rel_dists, taxa_for_dist_inference, plot_file)
median_outlier_table = os.path.join(output_dir, '%s.tsv' % input_tree_name)
median_rank_file = os.path.join(output_dir, '%s.dict' % input_tree_name)
self._median_summary_outlier_file(phylum_rel_dists,
taxa_for_dist_inference,
gtdb_parent_ranks,
median_outlier_table,
median_rank_file,
verbose_table)
output_tree = os.path.join(output_dir, '%s.scaled.tree' % input_tree_name)
tree.write_to_path(output_tree,
schema='newick',
suppress_rooting=True,
unquoted_underscores=True)
