Skip to content

Commit

Permalink
Adding database name output
Browse files Browse the repository at this point in the history
  • Loading branch information
@akrinos
akrinos committed Dec 5, 2023
1 parent 6d4b71b commit 9ecd10b
Showing 1 changed file with 25 additions and 11 deletions.
36 changes: 25 additions & 11 deletions src/EUKulele/tax_placement.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -134,7 +134,7 @@ def match_maker(dd, consensus_cutoff, consensus_proportion, tax_dict, use_counts
if len(transcript_name) > 1:
print("More than 1 transcript name included in the group.", flush = True)
transcript_name = list(transcript_name)[0]
ds = list(set(dd[dd.bitscore>=md]['ssqid_TAXID']))
ds = [curr.replace(".","N") for curr in list(set(dd[dd.bitscore>=md]['ssqid_TAXID']))]
counts = list(set(dd[dd.bitscore>=md]['counts']))
maxpident = max(list(set(dd[dd.bitscore>=md]['pident'])))

Expand All @@ -144,36 +144,49 @@ def match_maker(dd, consensus_cutoff, consensus_proportion, tax_dict, use_counts
chosen_count = 0
assignment, level = tax_placement(maxpident, tax_cutoffs) #tax_placement(md, tax_cutoffs)
# most specific taxonomic level assigned
database_match=''
database_dict=dict()
if len(ds)==1:
database_match=ds[0]
if ds[0] not in tax_dict:
return pd.DataFrame([[transcript_name, assignment,\
"MissingFromTaxDict", "MissingFromTaxDict", md,\
";".join(["MissingFromTaxDict"]*level), "MissingFromTaxDict", md,\
chosen_count, ambiguous]],
columns=['transcript_name','classification_level',
'full_classification','classification',
'max_pid','counts','ambiguous'])
full_classification = str(tax_dict[ds[0]]).split(";")[0:level]
best_classification = full_classification[len(full_classification) - 1]
# the most specific taxonomic level we can classify by
full_classification = '; '.join(full_classification)
full_classification = ';'.join(full_classification)
# the actual assignments based on that
else:
classification_0 = []
full_classification_0 = []
for d in ds:
if d not in tax_dict:
classification_0.append("MissingFromTaxDict")
full_classification_0.append("MissingFromTaxDict")
full_classification_0.append(";".join(["MissingFromTaxDict"]*level))
if ";".join(["MissingFromTaxDict"]*level) not in database_dict:
database_dict[";".join(["MissingFromTaxDict"]*level)] = str(d)
else:
database_dict[";".join(["MissingFromTaxDict"]*level)] = database_dict[";".join(["MissingFromTaxDict"]*level)] + ";" + str(d)
continue
#return(pd.DataFrame(columns=['transcript_name','classification_level',
# 'full_classification','classification',
# 'max_pid','counts','ambiguous']))
d_full_class = str(tax_dict[str(d)]).split(";")[0:level]
classification_0.append(d_full_class[len(d_full_class) - 1])
# the most specific taxonomic level we can classify by
full_classification_0.append('; '.join(d_full_class))
full_classification_0.append(';'.join(d_full_class))
# the actual assignments based on that
if ';'.join(d_full_class) not in database_dict:
database_dict[';'.join(d_full_class)] = str(d)
else:
database_dict[';'.join(d_full_class)] = database_dict[';'.join(d_full_class)] + ";" + str(d)
entries = list(set(full_classification_0))
if len(entries) == 1:
database_match=database_dict[full_classification_0[0]]
best_classification = classification_0[0]
full_classification = full_classification_0[0]
else:
Expand All @@ -185,6 +198,7 @@ def match_maker(dd, consensus_cutoff, consensus_proportion, tax_dict, use_counts
curr_frac = len(np.where(full_classification_0 == e)) / \
len(full_classification_0)
if (isinstance(curr_frac, float)) & (curr_frac > best_frac):
database_match=database_dict[e]
best_frac = curr_frac
best_full_class = e
best_one_class = str(e.split(";")[len(e.split(";")) - 1]).strip()
Expand All @@ -198,14 +212,14 @@ def match_maker(dd, consensus_cutoff, consensus_proportion, tax_dict, use_counts
if use_counts == 1:
return pd.DataFrame([[transcript_name, assignment,\
full_classification, best_classification, md,\
chosen_count, ambiguous]],\
chosen_count, ambiguous, database_match]],\
columns=['transcript_name','classification_level', 'full_classification',
'classification', 'max_pid', 'counts', 'ambiguous'])
'classification', 'max_pid', 'counts', 'ambiguous', 'database'])
else:
return pd.DataFrame([[transcript_name, assignment, full_classification,\
best_classification, md, ambiguous]],\
best_classification, md, ambiguous, database_match]],\
columns=['transcript_name', 'classification_level', 'full_classification',
'classification', 'max_pid', 'ambiguous'])
'classification', 'max_pid', 'ambiguous', 'database'])

def apply_parallel(grouped_data, match_maker, consensus_cutoff, consensus_proportion, tax_dict, use_counts, tax_cutoffs, classes):
resultdf = Parallel(n_jobs=multiprocessing.cpu_count(),
Expand All @@ -232,11 +246,11 @@ def classify_taxonomy_parallel(df, tax_dict, namestoreads, pdict,
if namestoreads != 0:
return pd.DataFrame(columns=['transcript_name','classification_level',
'full_classification',
'classification', 'max_pid', 'counts', 'ambiguous'])
'classification', 'max_pid', 'counts', 'ambiguous', 'database'])
else:
return pd.DataFrame(columns=['transcript_name','classification_level',\
'full_classification',
'classification', 'max_pid', 'ambiguous'])
'classification', 'max_pid', 'ambiguous', 'database'])

for chunk in pd.read_csv(str(df), sep = '\t', header = None, chunksize=chunksize):
chunk.columns = ['qseqid', 'sseqid', 'pident', 'length', 'mismatch',
Expand Down

0 comments on commit 9ecd10b

Please sign in to comment.