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min_threshold counts #115

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Merged
kdcurry merged 1 commit into from
Feb 26, 2026
Merged

min_threshold counts #115

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23 changes: 15 additions & 8 deletions emu
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ CIGAR_OPS_ALL = [0, 1, 2, 4]
TAXONOMY_RANKS = ['species', 'genus', 'family', 'order', 'class', 'phylum', 'clade', 'superkingdom']
RANKS_PRINTOUT = ['tax_id'] + TAXONOMY_RANKS + ['subspecies', 'species subgroup', 'species group']
RANKS_ORDER = ['tax_id'] + TAXONOMY_RANKS[:6] + TAXONOMY_RANKS[7:]
LOG_LIKELIHOOD_DIFF_TOLERANCE = 1e-9
LOG_LIKELIHOOD_DIFF_TOLERANCE = 1e-5


def open_maybe_gzip(path, mode="rt"):
Expand Down Expand Up @@ -465,12 +465,12 @@ def expectation_maximization_iterations(log_p_rgs, db_ids, lli_thresh, input_thr
freq = {k: v for k, v in freq.items() if v >= freq_thresh}
# remove tax id if less than the frequency threshold
freq_full, updated_log_likelihood, p_sgr = expectation_maximization(log_p_rgs, freq)
freq_set_thresh = None
freq_set_thresh, p_sgr_thresh = None, None
if freq_thresh < input_threshold:
freq = {k: v for k, v in freq_full.items() if v >= input_threshold}
freq_set_thresh, updated_log_likelihood, p_sgr = \
freq_set_thresh, updated_log_likelihood, p_sgr_thresh = \
expectation_maximization(log_p_rgs, freq)
return freq_full, freq_set_thresh, p_sgr
return freq_full, freq_set_thresh, p_sgr, p_sgr_thresh

#output current estimation
#freq_to_lineage_df(freq, f"{out_file}_{counter}", df_nodes, df_names)
Expand Down Expand Up @@ -838,7 +838,7 @@ def combine_outputs(dir_path, rank, split_files=False, count_table=False):
return df_combined_full

if __name__ == "__main__":
__version__ = "3.6.1"
__version__ = "3.6.2"
parser = argparse.ArgumentParser()
parser.add_argument('--version', '-v', action='version', version='%(prog)s v' + __version__)
subparsers = parser.add_subparsers(dest="subparser_name", help='sub-commands')
Expand Down Expand Up @@ -966,7 +966,7 @@ if __name__ == "__main__":
get_cigar_op_log_probabilities(SAM_FILE)
log_prob_rgs, set_unmapped, set_mapped, set_filtered = log_prob_rgs_dict(
SAM_FILE, log_prob_cigar_op, longest_align_dict, locs_p_cigar_zero, args)
f_full, f_set_thresh, read_dist = expectation_maximization_iterations(log_prob_rgs,
f_full, f_set_thresh, read_dist, read_dist_thresh = expectation_maximization_iterations(log_prob_rgs,
db_species_tids,
.01, args.min_abundance)
classified_reads = {read_id for inner_dict in read_dist.values() for read_id in inner_dict}
Expand All @@ -983,11 +983,15 @@ if __name__ == "__main__":

# convert and save frequency to a tsv
if f_set_thresh:
classified_reads_thresh = {read_id for inner_dict in read_dist_thresh.values() for read_id in inner_dict}
mapped_unclassified_thresh = set_mapped - classified_reads_thresh
stdout.write(f"Unclassified mapped read count for min abundance threshold {args.min_abundance}: "
f"{len(mapped_unclassified_thresh)}\n")
freq_to_lineage_df(
f_set_thresh,
"{}_rel-abundance-threshold-{}".format(out_file, args.min_abundance),
df_taxonomy, len(set_mapped), len(set_unmapped), len(mapped_unclassified),
args.keep_counts)
df_taxonomy, len(set_mapped), len(set_unmapped), len(mapped_unclassified_thresh),
len(set_filtered), args.keep_counts)

# gather input sequences that are unmapped according to minimap2
if args.output_unclassified:
Expand All @@ -998,6 +1002,9 @@ if __name__ == "__main__":
input_filetype, mapped_unclassified)
output_sequences(args.input_file[0], "{}_filtered_mapped".format(out_file),
input_filetype, set_filtered)
if f_set_thresh:
output_sequences(args.input_file[0], "{}_unclassified_mapped_threshold-{}".format(out_file, args.min_abundance),
input_filetype, mapped_unclassified_thresh)

# clean up extra file
if not args.keep_files:
Expand Down