def outliers(self, options):
"""Create information for identifying taxnomic outliers"""
check_file_exists(options.input_tree)
check_file_exists(options.taxonomy_file)
if options.plot_taxa_file:
check_file_exists(options.plot_taxa_file)
if options.trusted_taxa_file:
check_file_exists(options.trusted_taxa_file)
if not os.path.exists(options.output_dir):
os.makedirs(options.output_dir)
if options.highlight_polyphyly and not options.fmeasure_table:
self.logger.error(
"The '--highlight_polyphyly' flag must be used with the '--fmeasure_table' flag."
)
return
o = Outliers(options.skip_mpld3, options.dpi, options.output_dir)
o.run(options.input_tree, options.taxonomy_file, options.viral,
options.plot_taxa_file, options.plot_dist_taxa_only,
options.plot_domain, options.highlight_polyphyly,
options.highlight_taxa_file, options.trusted_taxa_file,
options.fixed_root, options.min_children, options.min_support,
options.mblet, options.fmeasure_table, options.min_fmeasure,
options.fmeasure_mono, options.verbose_table)
self.logger.info('Done.')
def outliers(self, options):
"""Create information for identifying taxnomic outliers"""
check_file_exists(options.input_tree)
if options.plot_taxa_file:
check_file_exists(options.plot_taxa_file)
if options.trusted_taxa_file:
check_file_exists(options.trusted_taxa_file)
if not os.path.exists(options.output_dir):
os.makedirs(options.output_dir)
o = Outliers(options.dpi)
o.run(options.input_tree,
options.taxonomy_file,
options.output_dir,
options.plot_taxa_file,
options.plot_dist_taxa_only,
options.plot_domain,
options.trusted_taxa_file,
options.fixed_root,
options.min_children,
options.min_support,
options.verbose_table)
self.logger.info('Done.')
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 outliers(self, options):
"""Create information for identifying taxnomic outliers"""
check_file_exists(options.input_tree)
if options.plot_taxa_file:
check_file_exists(options.plot_taxa_file)
if options.trusted_taxa_file:
check_file_exists(options.trusted_taxa_file)
if not os.path.exists(options.output_dir):
os.makedirs(options.output_dir)
o = Outliers(options.dpi)
o.run(options.input_tree, options.taxonomy_file, options.output_dir,
options.plot_taxa_file, options.plot_dist_taxa_only,
options.plot_domain, options.trusted_taxa_file,
options.fixed_root, options.min_children, options.min_support,
options.verbose_table)
self.logger.info('Done.')
