def lca_star(self,
taxonomy_list,
min_tree_depth=3,
majority_threshold=0.51):
"""Find the LCA within a list of taxonomies after filtering those taxonomies by tree depth.
One can also vary what constitutes a majority consensus for the counts, with the default
being 51%.
Args:
taxonomy_list (list): list of taxonomy names or IDs
min_tree_depth (int): the mininum allowable tree depth of taxon to be considered within
the taxonomy list; those found sooner in the tree will be filtered out of consideration
majority_threshold (float): 0-1; the fraction of taxonomy counts which constitutes a
majority; a lower fraction will classify with less confidence deeper in the tree
while a higher threshold will classify with more confidence higher in the tree
Returns:
dict of 'taxonomy' and 'pvalue'
Example:
>>> tree = Tree("ref/ncbi_taxonomy_tree.txt")
>>> taxonomy_list = ['gamma subgroup', 'RNA similarity group I',
'purple photosynthetic bacteria and relatives',
'not Bacteria Haeckel 1894',
'purple photosynthetic bacteria and relatives', 'gamma subgroup',
'gamma subgroup', 'purple photosynthetic bacteria and relatives',
'purple photosynthetic bacteria and relatives']
>>> tree.lca_star(taxonomy_list)
{'pvalue': 0.012791848981090311, 'taxonomy': '1224'}
"""
# tree depth based filter
taxonomy_list = self.filter_taxonomy_list(taxonomy_list,
min_tree_depth)
# all have been filtered
if not taxonomy_list:
majority = "1"
p = 1.
else:
taxonomy_counts = Counter(taxonomy_list)
majority_cutoff = len(taxonomy_list) * majority_threshold
# majority based on existing taxonomy counts alone
if taxonomy_counts.most_common()[0][1] > majority_cutoff:
majority = taxonomy_counts.most_common()[0][0]
p = nettleton_pvalue(taxonomy_list, majority)
# create majority from lca
else:
majority, lineages = self.lca_majority(taxonomy_list,
majority_cutoff)
aggregate_counts = self.counts_to_majority_list(
taxonomy_counts, lineages, majority)
p = nettleton_pvalue(aggregate_counts, majority)
return {"taxonomy": majority, "pvalue": p}
def process_orfs_with_tree(orf_assignments, tree, output, aggregation_method, majority_threshold=0.51, table_name="refseq"):
"""Processing the already classified ORFs through secondary contig classification.
Args:
orf_assignments (dict): dict of dict for per ORF tax assignment per contig
tree (Tree): taxonomic tree object
output (filehandle): output file handle
aggregation_method (str): lca, lca-majority, or majority
majority_threshold (float): constitutes a majority fraction at tree node for 'lca-majority' ORF aggregation method
"""
print("contig", "orf", "taxonomy", "erfc", "orf_taxonomy", "%s_product" % table_name,
"%s_evalue" % table_name, "%s_bitscore" % table_name, sep="\t", file=output)
for contig, orfs in orf_assignments.items():
taxonomies = [x[1] for x in orfs.values()]
if aggregation_method == "lca-majority":
res = tree.lca_star(taxonomies, majority_threshold=majority_threshold)
contig_taxonomy = res["taxonomy"]
error_function = res["pvalue"]
elif aggregation_method == "lca":
# TODO incorporate threshold into LCAs?
contig_taxonomy = tree.lca(taxonomies)
error_function = nettleton_pvalue(taxonomies, contig_taxonomy)
# simple majority
else:
contig_taxonomy = BlastHits(taxonomies).majority()
error_function = nettleton_pvalue(taxonomies, contig_taxonomy)
lineage = {}
for item in tree.taxonomic_lineage(contig_taxonomy):
node = tree.tree[item]
if node.tax_level in TAX_LEVELS:
# does not account for "no rank" and some other cases of "unclassified"
lineage["k" if node.tax_level == "superkingdom" else node.tax_level[0]] = node.taxonomy
lineage = validate_lineage(lineage)
for idx in sorted(orfs.keys()):
orf_function, orf_tax_id, bitscore, evalue = orfs[idx]
orf_taxonomy = tree.tree[orf_tax_id].taxonomy
print(contig, "%s_%s" % (contig, idx), lineage, error_function, orf_taxonomy,
orf_function, evalue, bitscore, sep="\t", file=output)
