def test_unstrat_default_pipeline(self):
'''Test running default pipeline on unstratified input table.'''
with TemporaryDirectory() as temp_dir:
unstrat_path_abun_df, unstrat_path_cov_df, strat_abun, strat_cov = pathway_pipeline(in_metagenome_unstrat,
default_pathway_map,
proc=1,
out_dir=temp_dir,
run_minpath=True,
coverage=True,
regroup_mapfile=default_regroup_map,
gap_fill_on=True,
per_sequence_contrib=False,
print_cmds=False)
# Compare these predicted tables to expected tables.
exp_abun_unstrat = pd.read_csv(exp_abun_unstrat_file, sep="\t",
index_col="pathway")
exp_cov_unstrat = pd.read_csv(exp_cov_unstrat_file, sep="\t",
index_col="pathway")
pd.testing.assert_frame_equal(exp_abun_unstrat, unstrat_path_abun_df,
check_like=True, check_less_precise=True)
pd.testing.assert_frame_equal(exp_cov_unstrat, unstrat_path_cov_df,
check_like=True, check_less_precise=True)
def test_strat_default_pipeline(self):
'''Test running strat_minpath default pipeline. Make sure that
community wide stratified abundances are calculated correctly and
that unstratified abundances are right. Note that wide-format
stratified tables are tested for this test only.'''
with TemporaryDirectory() as temp_dir:
unstrat_pathabun, \
unstrat_pathcov, \
strat_pathabun, \
strat_pathcov, \
pathabun_by_seq, \
pathcov_by_seq, \
unstrat_pathabun_per_seq = pathway_pipeline(in_metagenome_strat,
default_pathway_map,
proc=1,
out_dir=temp_dir,
run_minpath=True,
coverage=True,
regroup_mapfile=default_regroup_map,
gap_fill_on=True,
per_sequence_contrib=False,
verbose=True,
wide_table=True)
# Compare these predicted tables to expected tables.
exp_abun_unstrat = pd.read_csv(exp_abun_unstrat_file,
sep="\t",
index_col="pathway")
exp_cov_unstrat = pd.read_csv(exp_cov_unstrat_file,
sep="\t",
index_col="pathway")
exp_abun_strat = pd.read_csv(exp_abun_strat_file, sep="\t")
# Sort stratified files (different versions can sort the output
# slightly differently).
strat_pathabun.sort_values(['pathway', 'sequence'], inplace=True)
exp_abun_strat.sort_values(['pathway', 'sequence'], inplace=True)
# Reset index labels.
exp_abun_strat.reset_index(drop=True, inplace=True)
strat_pathabun.reset_index(drop=True, inplace=True)
pd.testing.assert_frame_equal(exp_abun_unstrat,
unstrat_pathabun,
check_like=True,
check_less_precise=True)
pd.testing.assert_frame_equal(exp_cov_unstrat,
unstrat_pathcov,
check_like=True,
check_less_precise=True)
pd.testing.assert_frame_equal(exp_abun_strat,
strat_pathabun,
check_like=True,
check_less_precise=True)
def test_strat_per_genome_pipeline_strat_input(self):
'''Test running strat_minpath default pipeline. Make sure that
per genome contributions are correct (per_sequence_contrib set).
In this case the input is a stratified table.'''
with TemporaryDirectory() as temp_dir:
unstrat_pathabun, \
unstrat_pathcov, \
strat_pathabun, \
strat_pathcov, \
pathabun_by_seq, \
pathcov_by_seq, \
unstrat_pathabun_per_seq = pathway_pipeline(in_metagenome_strat_per_seq,
default_pathway_map,
proc=1,
out_dir=temp_dir,
run_minpath=True,
coverage=True,
regroup_mapfile=default_regroup_map,
gap_fill_on=True,
per_sequence_contrib=True,
per_sequence_abun=in_per_seq_abun,
per_sequence_function=in_per_seq_func,
verbose=True)
# Compare these predicted tables to expected tables.
exp_abun_unstrat = pd.read_csv(exp_abun_unstrat_per_genome_file,
sep="\t",
index_col="pathway")
exp_abun_unstrat_per_seq = pd.read_csv(
exp_abun_unstrat_per_genome_per_seq_file,
sep="\t",
index_col="pathway")
exp_cov_unstrat = pd.read_csv(exp_cov_unstrat_per_genome_file,
sep="\t",
index_col="pathway")
exp_abun_strat = pd.read_csv(exp_abun_strat_per_genome_file, sep="\t")
exp_cov_strat = pd.read_csv(exp_cov_strat_per_genome_file, sep="\t")
# Sort stratified files (different versions can sort the output
# slightly differently).
strat_pathabun.sort_values(['function', 'taxon'], inplace=True)
exp_abun_strat.sort_values(['function', 'taxon'], inplace=True)
strat_pathcov.sort_values(['function', 'taxon'], inplace=True)
exp_cov_strat.sort_values(['function', 'taxon'], inplace=True)
# Reset index labels.
exp_abun_strat.reset_index(drop=True, inplace=True)
strat_pathabun.reset_index(drop=True, inplace=True)
exp_cov_strat.reset_index(drop=True, inplace=True)
strat_pathcov.reset_index(drop=True, inplace=True)
pd.testing.assert_frame_equal(exp_abun_unstrat,
unstrat_pathabun,
check_like=True,
check_less_precise=True)
pd.testing.assert_frame_equal(exp_abun_unstrat_per_seq,
unstrat_pathabun_per_seq,
check_like=True,
check_less_precise=True)
pd.testing.assert_frame_equal(exp_cov_unstrat,
unstrat_pathcov,
check_like=True,
check_less_precise=True)
pd.testing.assert_frame_equal(exp_abun_strat,
strat_pathabun,
check_like=True,
check_less_precise=True)
pd.testing.assert_frame_equal(exp_cov_strat,
strat_pathcov,
check_like=True,
check_less_precise=True)
def main():
args = parser.parse_args()
# Check that input files exist.
check_files_exist([args.input, args.map])
gap_fill_opt = not args.no_gap_fill
run_minpath_opt = not args.skip_minpath
# If intermediate output directory set then create and output there.
# Otherwise make a temporary directory for the intermediate files.
if args.intermediate:
make_output_dir(args.intermediate)
unstrat_abun, unstrat_cov, strat_abun, strat_cov = pathway_pipeline(
inputfile=args.input,
mapfile=args.map,
regroup_mapfile=args.regroup_map,
proc=args.proc,
out_dir=args.intermediate,
run_minpath=run_minpath_opt,
coverage=args.coverage,
gap_fill_on=gap_fill_opt,
no_regroup=args.no_regroup,
per_sequence_contrib=args.per_sequence_contrib,
per_sequence_abun=args.per_sequence_abun,
per_sequence_function=args.per_sequence_function,
print_cmds=args.print_cmds)
else:
with TemporaryDirectory() as temp_dir:
unstrat_abun, unstrat_cov, strat_abun, strat_cov = pathway_pipeline(
inputfile=args.input,
mapfile=args.map,
regroup_mapfile=args.regroup_map,
proc=args.proc,
out_dir=temp_dir,
run_minpath=run_minpath_opt,
coverage=args.coverage,
gap_fill_on=gap_fill_opt,
no_regroup=args.no_regroup,
per_sequence_contrib=args.per_sequence_contrib,
per_sequence_abun=args.per_sequence_abun,
per_sequence_function=args.per_sequence_function,
print_cmds=args.print_cmds)
make_output_dir(args.out_dir)
# Write output files.
unstrat_abun_outfile = path.join(args.out_dir, "path_abun_unstrat.tsv.gz")
unstrat_abun.to_csv(path_or_buf=unstrat_abun_outfile, sep="\t",
index_label="pathway", compression="gzip")
if args.coverage:
unstrat_cov_outfile = path.join(args.out_dir, "path_cov_unstrat.tsv.gz")
unstrat_cov.to_csv(path_or_buf=unstrat_cov_outfile, sep="\t",
index_label="pathway", compression="gzip")
# Write stratified output only if something besides None was returned.
if strat_abun is not None:
strat_abun_outfile = path.join(args.out_dir, "path_abun_strat.tsv.gz")
strat_abun.to_csv(path_or_buf=strat_abun_outfile, sep="\t",
index=False, compression="gzip")
if args.coverage and strat_cov is not None:
strat_cov_outfile = path.join(args.out_dir, "path_cov_strat.tsv.gz")
strat_cov.to_csv(path_or_buf=strat_cov_outfile, sep="\t",
index=False, compression="gzip")
def main():
args = parser.parse_args()
# Check that input files exist.
check_files_exist([args.input, args.map])
gap_fill_opt = not args.no_gap_fill
run_minpath_opt = not args.skip_minpath
# If intermediate output directory set then create and output there.
# Otherwise make a temporary directory for the intermediate files.
if args.intermediate:
make_output_dir(args.intermediate)
unstrat_abun, \
unstrat_cov, \
strat_abun, \
strat_cov, \
path_abun_by_seq, \
path_cov_by_seq, \
unstrat_abun_per_seq = pathway_pipeline(
inputfile=args.input,
mapfile=args.map,
regroup_mapfile=args.regroup_map,
proc=args.processes,
out_dir=args.intermediate,
run_minpath=run_minpath_opt,
coverage=args.coverage,
gap_fill_on=gap_fill_opt,
no_regroup=args.no_regroup,
per_sequence_contrib=args.per_sequence_contrib,
per_sequence_abun=args.per_sequence_abun,
per_sequence_function=args.per_sequence_function,
wide_table=args.wide_table,
print_cmds=args.print_cmds)
else:
with TemporaryDirectory() as temp_dir:
unstrat_abun, \
unstrat_cov, \
strat_abun, \
strat_cov, \
path_abun_by_seq, \
path_cov_by_seq, \
unstrat_abun_per_seq = pathway_pipeline(
inputfile=args.input,
mapfile=args.map,
regroup_mapfile=args.regroup_map,
proc=args.processes,
out_dir=temp_dir,
run_minpath=run_minpath_opt,
coverage=args.coverage,
gap_fill_on=gap_fill_opt,
no_regroup=args.no_regroup,
per_sequence_contrib=args.per_sequence_contrib,
per_sequence_abun=args.per_sequence_abun,
per_sequence_function=args.per_sequence_function,
wide_table=args.wide_table,
print_cmds=args.print_cmds)
make_output_dir(args.out_dir)
# Write output files. The unstratified abundance table will always be
# written, but the other files will only be written if applicable.
unstrat_abun_outfile = path.join(args.out_dir, "path_abun_unstrat.tsv.gz")
unstrat_abun.to_csv(path_or_buf=unstrat_abun_outfile,
sep="\t",
index_label="pathway",
compression="gzip")
if args.coverage:
unstrat_cov_outfile = path.join(args.out_dir,
"path_cov_unstrat.tsv.gz")
unstrat_cov.to_csv(path_or_buf=unstrat_cov_outfile,
sep="\t",
index_label="pathway",
compression="gzip")
if strat_abun is not None:
if args.wide_table:
strat_abun_outfile = path.join(args.out_dir,
"path_abun_strat.tsv.gz")
else:
strat_abun_outfile = path.join(args.out_dir,
"path_abun_contrib.tsv.gz")
strat_abun.to_csv(path_or_buf=strat_abun_outfile,
sep="\t",
index=False,
compression="gzip")
if args.coverage and strat_cov is not None:
if args.wide_table:
strat_cov_outfile = path.join(args.out_dir,
"path_cov_strat.tsv.gz")
else:
strat_cov_outfile = path.join(args.out_dir,
"path_cov_contrib.tsv.gz")
strat_cov.to_csv(path_or_buf=strat_cov_outfile,
sep="\t",
index=False,
compression="gzip")
if path_abun_by_seq is not None:
genome_path_abun_outfile = path.join(args.out_dir,
"path_abun_predictions.tsv.gz")
path_abun_by_seq.to_csv(path_or_buf=genome_path_abun_outfile,
sep="\t",
index=True,
compression="gzip",
index_label="sequence")
if args.coverage and path_cov_by_seq is not None:
genome_path_cov_outfile = path.join(args.out_dir,
"path_cov_predictions.tsv.gz")
path_cov_by_seq.to_csv(path_or_buf=genome_path_cov_outfile,
sep="\t",
index=True,
compression="gzip",
index_label="sequence")
if unstrat_abun_per_seq is not None:
unstrat_abun_per_seq_outfile = path.join(
args.out_dir, "path_abun_unstrat_per_seq.tsv.gz")
unstrat_abun_per_seq.to_csv(path_or_buf=unstrat_abun_per_seq_outfile,
sep="\t",
index_label="pathway",
compression="gzip")
def test_strat_default_pipeline(self):
'''Test running strat_minpath default pipeline. Make sure that
community wide stratified abundances are calculated correctly and
that unstratified abundances are right.'''
with TemporaryDirectory() as temp_dir:
unstrat_path_abun_df, unstrat_path_cov_df, strat_path_abun_df, strat_cov = pathway_pipeline(in_metagenome_strat,
default_pathway_map,
proc=1,
out_dir=temp_dir,
run_minpath=True,
coverage=True,
regroup_mapfile=default_regroup_map,
gap_fill_on=True,
per_sequence_contrib=False,
print_cmds=False)
# Compare these predicted tables to expected tables.
exp_abun_unstrat = pd.read_csv(exp_abun_unstrat_file, sep="\t",
index_col="pathway")
exp_cov_unstrat = pd.read_csv(exp_cov_unstrat_file, sep="\t",
index_col="pathway")
exp_abun_strat = pd.read_csv(exp_abun_strat_file, sep="\t")
# Sort stratified files (different versions can sort the output
# slightly differently).
strat_path_abun_df.sort_values(['pathway', 'sequence'], inplace=True)
exp_abun_strat.sort_values(['pathway', 'sequence'], inplace=True)
# Reset index labels.
exp_abun_strat.reset_index(drop=True, inplace=True)
strat_path_abun_df.reset_index(drop=True, inplace=True)
pd.testing.assert_frame_equal(exp_abun_unstrat, unstrat_path_abun_df,
check_like=True, check_less_precise=True)
pd.testing.assert_frame_equal(exp_cov_unstrat, unstrat_path_cov_df,
check_like=True, check_less_precise=True)
# Check with less precision here since the HUMAnN2 output that is used
# as expected abundances are not rounded.
pd.testing.assert_frame_equal(exp_abun_strat, strat_path_abun_df,
check_like=True, check_less_precise=True)