def get_combined_counts(table, positions):
bases = 'ACGT'
if type(positions) == str:
counts = reduced_one_position(table, positions)
mut_counts = counts['M']
unmut_counts = counts['R']
positions = [positions]
states = bases
header = ['mut', 'base', 'count']
else:
counts = reduced_multiple_positions(table, *positions)
mut_counts = counts['M']
unmut_counts = counts['R']
states = product(*list([bases] * len(positions)))
header = ['mut'] + ['base%d' % (i + 1)
for i in range(len(positions))] + ['count']
combined = []
for state in states:
combined.append(['R'] + list(state) + [unmut_counts[state]])
combined.append(['M'] + list(state) + [mut_counts[state]])
counts_table = LoadTable(header=header, rows=combined)
counts_table = counts_table.sorted(columns=header[:-1])
return counts_table
def summary_stat_table(table, factors):
'''returns summary statistics for classifier, feature set combination'''
fscore_cols = [c for c in table.header if c.startswith('fscore')]
distinct = table.distinct_values(factors)
rows = []
for comb in tqdm(distinct, ncols=80):
subtable = table.filtered(lambda x: tuple(x) == tuple(comb),
columns=factors)
aurocs = numpy.array(subtable.tolist('auc'))
mean_prec = numpy.array(subtable.tolist('mean_precision'))
accuracy = numpy.array(subtable.tolist('balanced_accuracy'))
row = list(comb) + [
aurocs.mean(),
aurocs.std(ddof=1),
mean_prec.mean(),
mean_prec.std(ddof=1),
accuracy.mean(),
accuracy.std(ddof=1)
]
for col in fscore_cols:
data = numpy.array(subtable.tolist(col))
row.append(data.mean())
row.append(data.std(ddof=1))
rows.append(row)
header = list(factors) + [
'mean_auc', 'std_auc', 'mean_ap', 'std_ap', 'mean_balanced_accuracy',
'std_balanced_accuracy'
]
for col in fscore_cols:
header.extend([f'mean_{col}', f'std_{col}'])
table = LoadTable(header=header, rows=rows)
table = table.sorted(reverse='mean_auc')
return table
def make_strand_symmetric_table(table):
'''takes a combined counts table and returns a table with reverse
complemented seqs
Uses MUTATION_COMPLEMENTS'''
new_data = []
direction_index = [
i for i in range(len(table.header)) if table.header[i] == 'direction'
][0]
for plus, minus in list(MUTATION_COMPLEMENTS.items()):
plus_table = table.filtered('direction=="%s"' % plus)
plus_data = add_strand_column(plus_table.tolist(), '+')
new_data.extend(plus_data)
minus_table = table.filtered('direction=="%s"' % minus)
if minus_table.shape[0] == 0:
continue
minus_table = _reverse_complement(minus_table)
minus_data = minus_table.tolist()
for row in minus_data:
row[direction_index] = plus
minus_data = add_strand_column(minus_data, '-')
new_data.extend(minus_data)
return LoadTable(header=table.header[:] + ['strand'], rows=new_data)
def spectra_table(table, group_label):
"""returns a table with columns without position information"""
assert 'direction' in table.header
if 'mut' in table.header:
# remove redundant category (counts of M == R)
table = table.filtered("mut=='M'")
columns = ['count', 'direction', group_label]
table = table.get_columns(columns)
# so we have a table with counts per direction
results = []
group_categories = table.distinct_values(group_label)
filter_template = "direction=='%(direction)s' and "\
"%(label)s=='%(category)s'"
for direction in table.distinct_values('direction'):
start = direction[0]
for group_category in group_categories:
condition = dict(direction=direction,
label=group_label,
category=group_category)
sub_table = table.filtered(filter_template % condition)
total = sub_table.summed('count')
results.append([total, start, direction, group_category])
result = LoadTable(header=['count', 'start', 'direction', group_label],
rows=results)
return result
def test_deserialise_tabular_table(self):
"""correctly deserialises Table"""
from cogent3 import LoadTable
table = LoadTable(
header=["id", "foo", "bar"],
rows=[
[1, "abc", 11],
[2, "bca", 22],
[3, "cab", 33],
[4, "abc", 44],
[5, "bca", 55],
],
)
json = table.to_json()
got = deserialise_object(json)
self.assertEqual(got.to_dict(), table.to_dict())
def get_grouped_combined_counts(table, position, group_label):
"""wraps motif_count.get_combined_counts for groups"""
group_cats = table.distinct_values(group_label)
all_data = []
header = None
for category in group_cats:
subtable = table.filtered(lambda x: x == category, columns=group_label)
counts = motif_count.get_combined_counts(subtable, position)
if header is None:
header = [group_label] + list(counts.header)
counts = counts.with_new_column(group_label, lambda x: category,
columns=counts.header[0])
all_data.extend(counts.tolist(header))
counts = LoadTable(header=header, rows=all_data)
counts.sorted(columns=[group_label, 'mut'])
return counts
def test_reverse_complement(self):
table = LoadTable(header=self.header, rows=self.data)
ex = [[1670, 'A', 'A', 'A', 'A', 'M', 'AtoC'],
[557, 'G', 'T', 'T', 'C', 'M', 'AtoC'],
[1479, 'T', 'T', 'C', 'T', 'M', 'AtoC'],
[925, 'C', 'T', 'T', 'C', 'M', 'AtoC'],
[1919, 'T', 'T', 'G', 'T', 'M', 'AtoC'],
[442, 'T', 'G', 'T', 'C', 'M', 'AtoC']]
got = _reverse_complement(table)
raw_got = got.tolist()
self.assertEqual(raw_got, ex)
def load_table_from_delimited_file(path, sep='\t'):
'''returns a Table object after a quicker loading'''
with open_(path, 'rt') as infile:
header = infile.readline().strip().split(sep)
count_index = header.index('count')
records = []
for line in infile:
line = line.strip().split(sep)
line[count_index] = int(line[count_index])
records.append(line)
table = LoadTable(header=header, rows=records)
return table
def missing_species_names(names):
"""returns a Table of missing species names, or None"""
missing = []
for name in names:
n = Species.get_species_name(name)
if n == "None":
missing.append([name])
if missing:
result = LoadTable(header=["MISSING SPECIES"], rows=missing)
else:
result = None
return result
def display_available_dbs(account, release=None):
"""displays the available Ensembl databases at the nominated host"""
db_list = get_db_name(account=account, db_type="core", release=release)
db_list += get_db_name(account=account, db_type="compara", release=release)
rows = []
for db_name in db_list:
species_name = db_name.species
if species_name:
common_name = Species.get_common_name(db_name.species,
level="ignore")
if "compara" in db_name.name:
species_name = common_name = "-"
rows.append([db_name.release, db_name.name, species_name, common_name])
table = LoadTable(header=["Release", "Db Name", "Species", "Common Name"],
rows=rows,
space=2)
table = table.sorted(["Release", "Db Name"])
table.legend = (
"Values of 'None' indicate cogent does not have a value for that database name."
)
return table
def test_all_counts(self):
"""exercising all_acounts"""
if os.path.exists(self.dirname):
shutil.rmtree(self.dirname)
runner = CliRunner()
# should fail, as data files not in this directory
r = runner.invoke(
all_count_main, ["-cdata/*.txt", "-o%s" % self.dirname])
self.assertNotEqual(r.exit_code, 0)
r = runner.invoke(
all_count_main, ["-cdata/directions/*.txt", "-o%s" % self.dirname])
# should produce directory containing two files
dirlist = os.listdir(self.dirname)
self.assertEqual(set(dirlist),
set(["combined_counts.txt", "combined_counts.log"]))
# check the contents of combined_counts
counts = LoadTable(os.path.join(
self.dirname, "combined_counts.txt"), sep="\t")
# 4**4 nbrs x 12 mutations x 2 (M/R groups) = 6144
counts = LoadTable(os.path.join(
self.dirname, "combined_counts.txt"), sep="\t")
self.assertEqual(counts.shape[0], 6144)
shutil.rmtree(self.dirname)
def get_ms_supp_labels(float_type,
texdir="../ENU-ms-genetics-v2",
verbose=False):
"""returns ordered dicts of labels from the manuscript for
supplementary and main manuscript body"""
# assert float_type in ('fig', 'tab')
# hardcoding these, in the manuscript order of the sections
texfns = [
os.path.join(texdir, tfn) for tfn in (
"MS-introduction.tex",
"MS-results.tex",
"MS-discussion.tex",
"MS-methods.tex",
)
]
alllabels = None
for tfn in texfns:
tags = get_tags(tfn, "label")
if alllabels is None:
alllabels = tags
else:
alllabels.update(tags)
print("\n\nWorking on labels")
alllabels = filtertags(float_type, alllabels)
allrefs = None
for tfn in texfns:
tags = get_tags(tfn, "ref")
if allrefs is None:
allrefs = tags
else:
allrefs.update(tags)
print("\n\nWorking on refs")
allrefs = filtertags(float_type, allrefs)
mainrefs = filtertags(lambda x: not x.startswith("sup"), allrefs)
suprefs = filtertags(lambda x: x.startswith("sup"), allrefs)
missing = set(alllabels) - set(mainrefs)
rows = [(missed, alllabels[missed]) for missed in missing]
table = LoadTable(header=["label missing", "referenced in"], rows=rows)
if verbose:
print(table)
return mainrefs, suprefs
def _reverse_complement(table):
'''returns a table with sequences reverse complemented'''
pos_indices = [
i for i, c in enumerate(table.header) if c.startswith('pos')
]
rows = table.tolist()
for row in rows:
# we use the cogent3 DnaSeq object to do reverse complementing
seq = DNA.make_seq(''.join(row[i] for i in pos_indices))
seq = list(seq.rc())
for i, index in enumerate(pos_indices):
row[index] = seq[i]
if rows:
new = LoadTable(header=table.header, rows=rows)
else:
new = None
return new
def test_aln_to_counts(self):
"""exercising aln_to_counts"""
if os.path.exists(self.dirname):
shutil.rmtree(self.dirname)
makedirs(self.dirname)
runner = CliRunner()
# should fail, as data files not in this directory
r = runner.invoke(aln_to_counts_main, ["-adata/sample_AtoC.fasta", "-o%s" % self.dirname,
"-f1", "--direction=AtoC", "-S111", "-F"])
dirlist = os.listdir(self.dirname)
self.assertEqual(r.exit_code, 0)
self.assertEqual(set(dirlist),
set(["sample_AtoC.txt", "sample_AtoC.log"]))
counts = LoadTable(os.path.join(
self.dirname, "sample_AtoC.txt"), sep="\t")
# two columns with pos, two groups giving shape=2*16
self.assertEqual(counts.shape[0], 32)
shutil.rmtree(self.dirname)
def status(configpath):
"""checks download/install status using checkpoint files and config"""
release, remote_path, local_path, species_dbs = read_config(configpath)
content = os.listdir(local_path)
dbnames = reduce_dirnames(content, species_dbs)
rows = []
for db in dbnames:
row = [
db.name,
is_downloaded(local_path, db.name),
is_installed(local_path, db.name),
]
rows.append(row)
table = LoadTable(
header=["dbname", "Downloaded", "Installed"],
rows=rows,
title="Status of download and install",
legend="config=%s; local_path=%s" % (configpath.name, local_path),
)
print(table)
def get_count_table(observed, control, k=None):
"""return table of motif counts
Each motif position is a separate column. All possible DNA motifs of length
k are included.
Arguments:
- observed: the observed counts as {seq: count}
- control: the control counts as {seq: count}
- k: size of the motif"""
rows = []
lengths = set(
list(map(len, list(observed.keys()))) +
list(map(len, list(control.keys()))))
if len(lengths) != 1:
raise ValueError("Motifs not all same length: %s" % str(lengths))
length = list(lengths)[0]
if k and length != k:
raise ValueError("k[%d] doesn't match motif length [%d]" % (k, length))
elif k is None:
k = length
states = list(set(observed.keys()) | set(control.keys()))
states.sort()
for state in states:
state = ''.join(state)
control_counts = control[state]
observed_counts = observed[state]
if control_counts == observed_counts == 0:
# we skip unobserved states
continue
rows.append([control_counts] + list(state) + ['R'])
rows.append([observed_counts] + list(state) + ['M'])
header = ['count'] + ["pos%d" % i for i in range(k)] + ['mut']
table = LoadTable(header=header, rows=rows)
return table
def _parse_db_display(output, columns):
"""finds the table display and accumulates the content"""
result = output.splitlines()
header = []
for index, line in enumerate(result):
if not header and columns[0] in line:
header = columns
break
if header:
rows = []
for i in range(index + 2, len(result)):
line = result[i].strip()
if line.startswith("----------"):
break
line = line.split()
rows.append(line[:len(columns)])
table = LoadTable(header=header, rows=rows)
else:
table = None
return table
def test_strandsym_table(self):
"""makes strand symmetric table"""
data = [[1, 'T', 'T', 'T', 'T', 'M', 'TtoG'],
[1, 'G', 'A', 'A', 'C', 'M', 'TtoG'],
[1, 'A', 'G', 'A', 'A', 'M', 'TtoG'],
[1, 'G', 'A', 'A', 'G', 'M', 'TtoG'],
[1, 'A', 'C', 'A', 'A', 'M', 'TtoG'],
[1, 'G', 'A', 'C', 'A', 'M', 'TtoG']]
exp = []
for row in self.data:
n = row[:]
n.append('+')
exp.append(n)
for row in data:
seq = list(map(DNA.complement, row[1:-2]))
seq.reverse()
n = [row[0]] + seq + ['M', 'AtoC']
n.append('-')
exp.append(n)
table = LoadTable(header=self.header, rows=self.data + data)
r = make_strand_symmetric_table(table)
self.assertEqual(r.tolist(), exp)
def single_group(counts_table, outpath, group_label, group_ref, positions,
plot_config, first_order, dry_run):
# Collect statistical analysis results
summary = []
max_results = {}
# Single position analysis
print("Doing single position analysis")
single_results = single_position_effects(counts_table, positions,
group_label=group_label)
summary += make_summary(single_results)
max_results[1] = max(single_results[p]['rel_entropy']
for p in single_results)
if not dry_run:
outfilename = os.path.join(outpath, "1.json")
util.dump_loglin_stats(single_results, outfilename)
LOGGER.output_file(outfilename, label="analysis1")
fig = get_single_position_fig(
single_results, positions,
plot_config.get('1-way plot', 'figsize'),
group_label=group_label,
group_ref=group_ref,
figwidth=plot_config.get('1-way plot', 'figwidth'),
xlabel_fontsize=plot_config.get('1-way plot',
'xlabel_fontsize'),
ylabel_fontsize=plot_config.get('1-way plot',
'ylabel_fontsize'),
xtick_fontsize=plot_config.get('1-way plot',
'xtick_fontsize'),
ytick_fontsize=plot_config.get('1-way plot',
'ytick_fontsize'))
format_offset(fig, int(plot_config.get('1-way plot',
'ytick_fontsize') * .8))
if not dry_run:
outfilename = os.path.join(outpath, "1.pdf")
fig.savefig(outfilename, bbox_inches='tight')
print("Wrote", outfilename)
fig.clf() # refresh for next section
if first_order:
msg = "Done! Check %s for your results" % outpath
summary = LoadTable(header=['Position', 'RE', 'Deviance', 'df',
'prob', 'formula'],
rows=summary, digits=2, space=2)
if not dry_run:
outfilename = os.path.join(outpath, "summary.txt")
summary.write(outfilename, sep='\t')
LOGGER.output_file(outfilename, label="summary")
return msg
print("Doing two positions analysis")
results = get_two_position_effects(counts_table, positions,
group_label=group_label)
summary += make_summary(results)
max_results[2] = max(results[p]['rel_entropy'] for p in results)
if not dry_run:
outfilename = os.path.join(outpath, "2.json")
util.dump_loglin_stats(results, outfilename)
LOGGER.output_file(outfilename, label="analysis2")
fig = get_two_position_fig(results, positions,
plot_config.get('2-way plot', 'figsize'),
group_label=group_label, group_ref=group_ref,
xtick_fontsize=plot_config.get(
'2-way plot', 'xtick_fontsize'),
ytick_fontsize=plot_config.get('2-way plot', 'ytick_fontsize'))
fig.set_figwidth(plot_config.get('2-way plot', 'figwidth'))
x_fsz = plot_config.get('2-way plot', 'xlabel_fontsize')
y_fsz = plot_config.get('2-way plot', 'ylabel_fontsize')
fig.text(0.5, plot_config.get('2-way plot', 'xlabel_pad'), 'Position',
ha='center', va='center', fontsize=x_fsz)
fig.text(plot_config.get('2-way plot', 'ylabel_pad'), 0.5, 'RE',
ha='center', va='center', rotation='vertical', fontsize=y_fsz)
format_offset(fig, int(plot_config.get('2-way plot',
'ytick_fontsize') * .8))
if not dry_run:
outfilename = os.path.join(outpath, "2.pdf")
fig.savefig(outfilename, bbox_inches='tight')
print("Wrote", outfilename)
fig.clf() # refresh for next section
print("Doing three positions analysis")
results = get_three_position_effects(counts_table, positions,
group_label=group_label)
summary += make_summary(results)
max_results[3] = max(results[p]['rel_entropy'] for p in results)
if not dry_run:
outfilename = os.path.join(outpath, "3.json")
util.dump_loglin_stats(results, outfilename)
LOGGER.output_file(outfilename, label="analysis3")
fig = get_three_position_fig(results, positions,
plot_config.get('3-way plot', 'figsize'),
group_label=group_label, group_ref=group_ref,
xtick_fontsize=plot_config.get(
'3-way plot', 'xtick_fontsize'),
ytick_fontsize=plot_config.get('3-way plot', 'ytick_fontsize'))
fig.set_figwidth(plot_config.get('3-way plot', 'figwidth'))
x_fsz = plot_config.get('3-way plot', 'xlabel_fontsize')
y_fsz = plot_config.get('3-way plot', 'ylabel_fontsize')
fig.text(0.5, plot_config.get('3-way plot', 'xlabel_pad'),
'Position', ha='center', va='center', fontsize=x_fsz)
fig.text(plot_config.get('3-way plot', 'ylabel_pad'), 0.5, 'RE',
ha='center', va='center', rotation='vertical', fontsize=y_fsz)
format_offset(fig,
int(plot_config.get('3-way plot', 'ytick_fontsize') * .8))
if not dry_run:
outfilename = os.path.join(outpath, "3.pdf")
fig.savefig(outfilename, bbox_inches='tight')
print("Wrote", outfilename)
fig.clf() # refresh for next section
print("Doing four positions analysis")
results = get_four_position_effects(counts_table, positions,
group_label=group_label)
summary += make_summary(results)
max_results[4] = max(results[p]['rel_entropy'] for p in results)
if not dry_run:
outfilename = os.path.join(outpath, "4.json")
util.dump_loglin_stats(results, outfilename)
LOGGER.output_file(outfilename, label="analysis4")
fig = get_four_position_fig(results, positions,
plot_config.get('4-way plot', 'figsize'),
group_label=group_label, group_ref=group_ref)
fig.set_figwidth(plot_config.get('4-way plot', 'figwidth'))
ax = fig.gca()
x_fsz = plot_config.get('4-way plot', 'xlabel_fontsize')
y_fsz = plot_config.get('4-way plot', 'ylabel_fontsize')
ax.set_xlabel('Position', fontsize=x_fsz)
ax.set_ylabel('RE', fontsize=y_fsz)
format_offset(fig, int(plot_config.get('4-way plot',
'ytick_fontsize') * .8))
if not dry_run:
outfilename = os.path.join(outpath, "4.pdf")
fig.savefig(outfilename, bbox_inches='tight')
print("Wrote", outfilename)
fig.clf() # refresh for next section
# now generate summary plot
bar_width = 0.5
index = numpy.arange(4)
y_lim = max(max_results.values())
y_fmt = util.FixedOrderFormatter(numpy.floor(numpy.log10(y_lim)))
fig = pyplot.figure(figsize=plot_config.get('summary plot', 'figsize'))
ax = fig.gca()
ax.yaxis.set_major_formatter(y_fmt)
bar = pyplot.bar(index, [max_results[i] for i in range(1, 5)], bar_width)
pyplot.xticks(index + (bar_width / 2.), list(range(1, 5)),
fontsize=plot_config.get('summary plot', 'xtick_fontsize'))
x_sz = plot_config.get('summary plot', 'xlabel_fontsize')
y_sz = plot_config.get('summary plot', 'ylabel_fontsize')
ax.set_xlabel("Effect Order", fontsize=x_sz)
ax.set_ylabel("RE$_{max}$", fontsize=y_sz)
x_sz = plot_config.get('summary plot', 'xtick_fontsize')
y_sz = plot_config.get('summary plot', 'ytick_fontsize')
ax.tick_params(axis='x', labelsize=x_sz, pad=x_sz // 2, length=0)
ax.tick_params(axis='y', labelsize=y_sz, pad=y_sz // 2)
format_offset(fig, int(plot_config.get('summary plot',
'ytick_fontsize') * .8))
if not dry_run:
outfilename = os.path.join(outpath, "summary.pdf")
pyplot.savefig(outfilename, bbox_inches='tight')
print("Wrote", outfilename)
summary = LoadTable(header=['Position', 'RE', 'Deviance', 'df',
'prob', 'formula'],
rows=summary, digits=2, space=2)
if not dry_run:
outfilename = os.path.join(outpath, "summary.txt")
summary.write(outfilename, sep='\t')
LOGGER.output_file(outfilename, label="summary")
print(summary)
pyplot.close('all')
msg = "Done! Check %s for your results" % outpath
return msg
def main(countsfile, outpath, countsfile2, strand_symmetry, force_overwrite,
dry_run, verbose):
args = locals()
table = LoadTable(countsfile, sep='\t')
if not dry_run:
log_file_path = os.path.join(util.abspath(outpath),
'spectra_analysis.log')
LOGGER.log_file_path = log_file_path
LOGGER.log_message(str(args), label='vars')
LOGGER.input_file(countsfile)
# if there's a strand symmetry argument then we don't need a second file
if strand_symmetry:
group_label = 'strand'
counts_table = util.spectra_table(table, group_label)
if not strand_symmetry:
group_label = 'group'
# be sure there's two files
counts_table2 = LoadTable(countsfile2, sep='\t')
LOGGER.input_file(countsfile2)
counts_table2 = counts_table2.with_new_column('group',
lambda x: '2', columns=counts_table2.header[0])
counts_table1 = table.with_new_column('group',
lambda x: '1', columns=table.header[0])
counts_table1 = util.spectra_table(counts_table1, group_label)
counts_table2 = util.spectra_table(counts_table2, group_label)
# now combine
header = ['group'] + counts_table2.header[:-1]
raw1 = counts_table1.tolist(header)
raw2 = counts_table2.tolist(header)
counts_table = LoadTable(header=header, rows=raw1 + raw2)
if verbose:
print(counts_table)
# spectra table has [count, start, end, group] order
# we reduce comparisons to a start base
results = []
saveable = {}
for start_base in counts_table.distinct_values('start'):
subtable = counts_table.filtered('start == "%s"' % start_base)
columns = [c for c in counts_table.header if c != 'start']
subtable = subtable.get_columns(columns)
total_re, dev, df, collated, formula = log_lin.spectra_difference(
subtable, group_label)
r = [list(x) for x in collated.to_records(index=False)]
if not strand_symmetry:
grp_labels = {'1': countsfile,
'2': countsfile2}
grp_index = list(collated.columns).index('group')
for row in r:
row[grp_index] = grp_labels[row[grp_index]]
p = chisqprob(dev, df)
if p < 1e-6:
prob = "%.2e" % p
else:
prob = "%.6f" % p
for row in r:
row.insert(0, start_base)
row.append(prob)
results += r
significance = ["RE=%.6f" % total_re, "Dev=%.2f" % dev, "df=%d" % df,
"p=%s" % p]
stats = " : ".join(significance)
print("Start base=%s %s" % (start_base, stats))
saveable[start_base] = dict(rel_entropy=total_re, deviance=dev,
df=df, prob=p,
formula=formula, stats=collated.to_json())
table = LoadTable(header=['start_base'] + list(collated.columns) +
['prob'],
rows=results, digits=5).sorted(columns='ret')
json_path = None
outpath = util.abspath(outpath)
if not dry_run:
util.makedirs(outpath)
json_path = os.path.join(outpath, 'spectra_analysis.json')
dump_json(saveable, json_path)
LOGGER.output_file(json_path)
table_path = os.path.join(outpath, 'spectra_summary.txt')
table.write(table_path, sep='\t')
LOGGER.output_file(table_path)
LOGGER.log_message(str(significance), label="significance")
def main(counts_pattern, output_path, strand_symmetric, split_dir, dry_run,
force_overwrite):
"""export tab delimited combined counts table by appending the 12 mutation
direction tables, adding a new column ``direction``."""
args = locals()
output_path = abspath(output_path)
if strand_symmetric and split_dir:
split_dir = abspath(split_dir)
else:
split_dir = None
# check we the glob pattern produces the correct number of files
counts_files = glob.glob(counts_pattern)
check_found_filenames(counts_files)
counts_filename = os.path.join(output_path, 'combined_counts.txt')
runlog_path = os.path.join(output_path, "combined_counts.log")
if not dry_run:
if not force_overwrite and (os.path.exists(counts_filename)
or os.path.exists(runlog_path)):
msg = "Either %s or %s already exist. Force overwrite of "\
"existing files with -F."
raise ValueError(msg % (counts_filename, runlog_path))
makedirs(output_path)
if split_dir:
makedirs(split_dir)
LOGGER.log_file_path = runlog_path
LOGGER.log_message(str(args), label='vars')
for fn in counts_files:
LOGGER.input_file(fn, label="count_file")
start_time = time.time()
# run the program
all_counts = []
header = None
num_rows = 0
basenames = []
for fn in counts_files:
basenames.append(os.path.basename(fn))
mutation = direction.findall(fn)[0]
table = LoadTable(fn, sep='\t')
if header is None:
header = list(table.header)
header.append('direction')
num_rows = table.shape[0]
data = table.tolist()
new = []
for row in data:
row.append(mutation)
new.append(row)
all_counts += new
table = LoadTable(header=header, rows=all_counts)
if strand_symmetric:
table = make_strand_symmetric_table(table)
if split_dir:
group_subtables = get_subtables(table, group_label='direction')
if not dry_run:
table.write(counts_filename, sep='\t')
LOGGER.output_file(counts_filename)
if split_dir:
for group, subtable in group_subtables:
# we first assume that group is part of the filenames!
fn = [bn for bn in basenames if group in bn]
if len(fn) == 1:
fn = fn[0]
else:
fn = "%s.txt" % group
counts_filename = os.path.join(split_dir, fn)
subtable.write(counts_filename, sep='\t')
LOGGER.output_file(counts_filename)
# determine runtime
duration = time.time() - start_time
if not dry_run:
LOGGER.log_message("%.2f" % (duration / 60.),
label="run duration (minutes)")
print("Done!")
def nbr(countsfile, outpath, countsfile2, first_order, strand_symmetry,
group_label, group_ref, plot_cfg, no_type3, format, verbose, dry_run):
'''log-linear analysis of neighbouring base influence on point mutation
Writes estimated statistics, figures and a run log to the specified
directory outpath.
See documentation for count table format requirements.
'''
if no_type3:
util.exclude_type3_fonts()
args = locals()
outpath = util.abspath(outpath)
if not dry_run:
util.makedirs(outpath)
runlog_path = os.path.join(outpath, "analysis.log")
LOGGER.log_file_path = runlog_path
LOGGER.log_message(str(args), label='vars')
counts_filename = util.abspath(countsfile)
counts_table = util.load_table_from_delimited_file(counts_filename,
sep='\t')
LOGGER.input_file(counts_filename, label="countsfile1_path")
positions = [c for c in counts_table.header if c.startswith('pos')]
if not first_order and len(positions) != 4:
raise ValueError("Requires four positions for analysis")
group_label = group_label or None
group_ref = group_ref or None
if strand_symmetry:
group_label = 'strand'
group_ref = group_ref or '+'
if group_label not in counts_table.header:
print("ERROR: no column named 'strand', exiting.")
exit(-1)
if countsfile2:
print("Performing 2 group analysis")
group_label = group_label or 'group'
group_ref = group_ref or '1'
counts_table1 = counts_table.with_new_column(group_label,
lambda x: '1',
columns=counts_table.header[0])
fn2 = util.abspath(countsfile2)
counts_table2 = util.load_table_from_delimited_file(fn2, sep='\t')
LOGGER.input_file(fn2, label="countsfile2_path")
counts_table2 = counts_table2.with_new_column(group_label,
lambda x: '2',
columns=counts_table2.header[0])
# now combine
header = [group_label] + counts_table2.header[:-1]
raw1 = counts_table1.tolist(header)
raw2 = counts_table2.tolist(header)
counts_table = LoadTable(header=header, rows=raw1 + raw2)
if not dry_run:
outfile = os.path.join(outpath, 'group_counts_table.txt')
counts_table.write(outfile, sep='\t')
LOGGER.output_file(outfile, label="group_counts")
if dry_run or verbose:
print()
print(counts_table)
print()
plot_config = util.get_plot_configs(cfg_path=plot_cfg)
msg = single_group(counts_table, outpath, group_label, group_ref,
positions, plot_config, first_order,
dry_run)
print(msg)
def collate(base_path, output_path, exclude_paths, overwrite):
"""collates all classifier performance stats and writes
to a single tsv file"""
LOGGER.log_args()
outpath = os.path.join(output_path, "collated.tsv.gz")
logfile_path = os.path.join(output_path, "collated.log")
if os.path.exists(outpath) and not overwrite:
click.secho(f"Skipping. {outpath} exists. "
"Use overwrite to force.",
fg='green')
exit(0)
stat_fns = exec_command(f'find {base_path} -name' ' "*performance.json*"')
stat_fns = stat_fns.splitlines()
if not stat_fns:
msg = f'No files matching "*performance.json*" in {base_path}'
click.secho(msg, fg='red')
return
LOGGER.log_file_path = logfile_path
records = []
keys = set()
exclude_paths = [] if exclude_paths is None else exclude_paths.split(',')
num_skipped = 0
for fn in tqdm(stat_fns, ncols=80):
if skip_path(exclude_paths, fn):
num_skipped += 1
LOGGER.log_message(fn, label="SKIPPED FILE")
continue
LOGGER.input_file(fn)
data = load_json(fn)
labels = data['classification_report']['labels']
fscores = data['classification_report']['f-score']
row = {
"stat_path": fn,
"classifier_path": data["classifier_path"],
"auc": data["auc"],
"algorithm": data["classifier_label"],
"mean_precision": data["mean_precision"],
f"fscore({labels[0]})": fscores[0],
f"fscore({labels[1]})": fscores[1],
'balanced_accuracy': data['balanced_accuracy']
}
row.update(data["feature_params"])
keys.update(row.keys())
records.append(row)
columns = sorted(keys)
rows = list(map(lambda r: [r.get(c, None) for c in columns], records))
table = LoadTable(header=columns, rows=rows)
table = table.sorted(reverse="auc")
table = table.with_new_column(
"name",
lambda x: model_name_from_features(*x),
columns=["flank_size", "feature_dim", "usegc", "proximal"])
table = table.with_new_column("size",
sample_size_from_path,
columns="classifier_path")
table.write(outpath)
LOGGER.output_file(outpath)
# make summary statistics via grouping by factors
factors = [
"algorithm", "name", "flank_size", "feature_dim", "proximal", "usegc",
"size"
]
summary = summary_stat_table(table, factors=factors)
outpath = os.path.join(output_path, "summary_statistics.tsv.gz")
summary.write(outpath)
LOGGER.output_file(outpath)
if num_skipped:
click.secho("Skipped %d files that matched exclude_paths" %
num_skipped,
fg='red')