def main(argv):
parser = argparse.ArgumentParser(
description=__doc__,
prog='svtest sample-list',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('test_sample_list', type=str)
parser.add_argument(
'--valid-sample-list',
type=str,
default=None,
help='Sample ids not found in this list will cause an error')
parser.add_argument('--prefix',
type=str,
default=None,
help='Prefix to add to metric names')
# Print help if no arguments specified
if len(argv) == 0:
parser.print_help()
sys.exit(1)
args = parser.parse_args(argv)
samples = iou.read_samples_list(args.test_sample_list, fail_on_empty=False)
if args.valid_sample_list is not None:
valid_samples = iou.read_samples_list(args.valid_sample_list)
else:
valid_samples = None
# Get metrics
metrics = get_metrics(samples, valid_samples, args.prefix)
# Write metrics
write_metrics(metrics)
def get_metrics(file, sample_list):
samples = iou.read_samples_list(sample_list)
samples_set = set(samples)
data = [0, 0, 0, 0] # ++, --, +-, -+
for line in file:
tokens = line.decode().strip().split('\t')
test_record(tokens, samples_set)
first = tokens[2]
second = tokens[5]
val = first + second
if val == '++':
data[0] += 1
elif val == '--':
data[1] += 1
elif val == '+-':
data[2] += 1
elif val == '-+':
data[3] += 1
else:
raise ValueError("Unrecognized orientation: %s / %s" %
(first, second))
if len(samples) == 1:
metric_suffix = "_" + samples[0]
else:
metric_suffix = "_merged"
return {
PLUS_PLUS_KEY + metric_suffix: data[0],
MINUS_MINUS_KEY + metric_suffix: data[1],
PLUS_MINUS_KEY + metric_suffix: data[2],
MINUS_PLUS_KEY + metric_suffix: data[3]
}
def main(argv):
parser = argparse.ArgumentParser(
description=__doc__,
prog='svtest ped-file',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('test_ped_file', type=str)
parser.add_argument(
'--sample-list',
type=str,
default=None,
help='Sample ids not found in this list will cause an error')
parser.add_argument('--prefix',
type=str,
default=None,
help='Prefix to add to metric names')
# Print help if no arguments specified
if len(argv) == 0:
parser.print_help()
sys.exit(1)
args = parser.parse_args(argv)
if args.sample_list is not None:
samples = iou.read_samples_list(args.sample_list)
else:
samples = None
# Get metrics
df = pd.read_csv(args.test_ped_file, sep='\t', names=range(6))
metrics = get_metrics(df, valid_samples=samples, metric_prefix=args.prefix)
# Write metrics
write_metrics(metrics)
def main(argv):
parser = argparse.ArgumentParser(
description=__doc__,
prog='svtest medcov',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('test_file', type=str)
parser.add_argument('sample_list', type=str)
parser.add_argument('--baseline-file', type=str, default=None)
# Print help if no arguments specified
if len(argv) == 0:
parser.print_help()
sys.exit(1)
args = parser.parse_args(argv)
samples = iou.read_samples_list(args.sample_list)
# Read file
with open(args.test_file, mode='r') as ftest:
if args.baseline_file is None:
metrics = get_metrics(ftest, None, samples)
else:
with open(args.baseline_file, mode='r') as fbase:
metrics = get_metrics(ftest, fbase, samples)
# Write metrics
write_metrics(metrics)
def get_metrics(matrix_file, sample_list, low_mem_mode):
samples = iou.read_samples_list(sample_list)
samples_set = set(samples)
header = matrix_file.readline().decode().strip().split('\t')
header_samples_set = set(header[3:])
tu.test_sets_equal(header_samples_set,
samples_set,
item_str="sample",
name_a="header",
name_b="samples list")
data = []
interval_size = None
num_records = 0
for line in matrix_file:
num_records += 1
tokens = line.decode().strip().split('\t')
tu.test_is_int(tokens, 1)
tu.test_is_int(tokens, 2)
if interval_size is None:
interval_size = int(tokens[2]) - int(tokens[1])
else:
if interval_size != int(tokens[2]) - int(tokens[1]):
raise ValueError(
"Interval not of size {:d}: {:s}:{:d}-{:d}".format(
interval_size, tokens[0], int(tokens[1]),
int(tokens[2])))
counts = tokens[3:]
test_record(counts, len(samples_set))
if not low_mem_mode:
data.append([int(x) for x in counts])
if not low_mem_mode:
arr = np.asarray(data)
quantiles = np.quantile(arr, [0.25, 0.50, 0.75])
max_over_samples = arr.max(axis=1)
num_zero_in_all = len(max_over_samples[max_over_samples == 0])
min_over_samples = arr.min(axis=1)
num_zero_in_one = len(min_over_samples[min_over_samples == 0])
metrics = {
Q25_KEY: quantiles[0],
Q50_KEY: quantiles[1],
Q75_KEY: quantiles[2],
INTERVALS_KEY: num_records,
ALL_ZERO_KEY: num_zero_in_all,
ONE_ZERO_KEY: num_zero_in_one
}
column_means = arr.mean(axis=0)
col = 0
for sample in header[3:]:
metrics[SAMPLE_MEAN_KEY + "_" + sample] = column_means[col]
col += 1
else:
metrics = {INTERVALS_KEY: num_records}
return metrics
def main(argv):
parser = argparse.ArgumentParser(
description=__doc__,
prog='svtest plot-metrics',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('metrics_a', type=str)
parser.add_argument('metrics_b', type=str)
parser.add_argument('pdf_out', type=str)
parser.add_argument('--sample-list', type=str, default=None)
parser.add_argument('--changes-only', action='store_true',
help='Only plot values that are different')
parser.add_argument('--metrics-out', type=str,
help='Write plotted metrics to tsv', default=None)
# Print help if no arguments specified
if len(argv) == 0:
parser.print_help()
sys.exit(1)
args = parser.parse_args(argv)
# Read metric tables and join
df_a = get_metrics(args.metrics_a)
df_b = get_metrics(args.metrics_b)
df = df_a.join(df_b, how='outer', lsuffix='_a', rsuffix='_b', sort=True)
# If sample ids are provided, consolidate sample-specific metrics
if args.sample_list is not None:
samples = iou.read_samples_list(args.sample_list)
df = consolidate_sample_metrics(df, samples)
# Only plot changed metrics
if args.changes_only:
df = df[df["value_a"] != df["value_b"]]
# Write raw data to file
if args.metrics_out is not None:
df.to_csv(args.metrics_out, sep='\t')
# Plot
plot_data(df, args.pdf_out)
def get_metrics(baf_file, sample_list):
samples = iou.read_samples_list(sample_list)
samples_set = set(samples)
data = []
for line in baf_file:
tokens = line.decode().strip().split('\t')
test_record(tokens, samples_set)
baf = float(tokens[2])
data.append(baf)
arr = np.asarray(data)
quantiles = np.quantile(arr, [0.25, 0.50, 0.75])
if len(samples) == 1:
metric_suffix = "_" + samples[0]
else:
metric_suffix = "_merged"
return {
Q25_KEY + metric_suffix: quantiles[0],
Q50_KEY + metric_suffix: quantiles[1],
Q75_KEY + metric_suffix: quantiles[2],
COUNT_KEY + metric_suffix: len(arr)
}
def get_metrics(sr_file, sample_list):
samples = iou.read_samples_list(sample_list)
samples_set = set(samples)
side_metrics = [0, 0]
for line in sr_file:
tokens = line.decode().strip().split('\t')
test_record(tokens, samples_set)
side = tokens[2]
if side == 'left':
side_metrics[0] += 1
elif side == 'right':
side_metrics[1] += 1
else:
raise ValueError("Unrecognized orientation: %s" % side)
if len(samples) == 1:
metric_suffix = "_" + samples[0]
else:
metric_suffix = "_merged"
return {
LEFT_KEY + metric_suffix: side_metrics[0],
RIGHT_KEY + metric_suffix: side_metrics[1]
}
def main(argv):
parser = argparse.ArgumentParser(
description=__doc__,
prog='svtest vcf',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('test_vcf', type=str)
parser.add_argument('contig_list', type=str)
parser.add_argument('sample_list', type=str)
parser.add_argument(
'types',
type=str,
help='Comma-delimited list of variant types (case-sensitive)')
parser.add_argument('metric_prefix', type=str)
parser.add_argument(
'--baseline-vcf',
type=str,
help='Baseline vcf to provide evaluation metrics against')
parser.add_argument(
'--baseline-bed',
type=str,
help=
'Baseline bed file to provide evaluation metrics against. Must have header beginning with "'
+ BED_FILE_HEADER_CHAR + '" and the following columns: "' +
'", "'.join([
BED_FILE_CHROM_COL, BED_FILE_START_COL, BED_FILE_END_COL,
BED_FILE_SVTYPE_COL
]) + '"')
parser.add_argument(
'--min-reciprocal-overlap',
type=float,
default=0.5,
help='Minimum reciprocal overlap for validation metrics [0.5]')
parser.add_argument('--padding',
type=int,
default=50,
help='Interval padding for validation metrics [50]')
parser.add_argument(
'--max-warnings',
type=int,
default=50,
help='Maximum number of records to print warnings for [50]')
parser.add_argument('--fp-file',
type=str,
default=None,
help='Write false positives to file')
parser.add_argument('--fn-file',
type=str,
default=None,
help='Write false negatives to file')
parser.add_argument('--fp-pass-file',
type=str,
default=None,
help='Write PASS false positives to file')
parser.add_argument('--fn-pass-file',
type=str,
default=None,
help='Write PASS false negatives to file')
# Print help if no arguments specified
if len(argv) == 0:
parser.print_help()
sys.exit(1)
args = parser.parse_args(argv)
if (args.baseline_vcf is None
and args.baseline_bed is None) and (args.fp_file is not None
or args.fn_file is not None):
raise ValueError(
"FP and FN files cannot be generated if --baseline-vcf and --baseline-bed aren't specified"
)
if args.baseline_vcf is not None and args.baseline_bed is not None:
raise ValueError(
"Cannot specify both --baseline-vcf and --baseline-bed")
types_list = args.types.split(',')
contigs = iou.read_contig_list(args.contig_list)
samples = iou.read_samples_list(args.sample_list)
metrics, fp_intervals, fn_intervals, fp_intervals_pass, fn_intervals_pass = get_metrics(
args.test_vcf, args.baseline_vcf, args.baseline_bed, contigs,
types_list, args.min_reciprocal_overlap, args.padding, samples,
args.metric_prefix, args.max_warnings)
# Write metrics
write_metrics(metrics)
if args.fp_file is not None and fp_intervals is not None:
write_intervals(args.fp_file, fp_intervals)
if args.fn_file is not None and fn_intervals is not None:
write_intervals(args.fn_file, fn_intervals)
if args.fp_pass_file is not None and fp_intervals_pass is not None:
write_intervals(args.fp_pass_file, fp_intervals_pass)
if args.fn_pass_file is not None and fn_intervals_pass is not None:
write_intervals(args.fn_pass_file, fn_intervals_pass)
def main(argv):
parser = argparse.ArgumentParser(
description=__doc__,
prog='svtest vcf',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('test_vcf', type=str)
parser.add_argument('contig_list', type=str)
parser.add_argument('sample_list', type=str)
parser.add_argument(
'types',
type=str,
help='Comma-delimited list of variant types (case-sensitive)')
parser.add_argument('metric_prefix', type=str)
parser.add_argument(
'--baseline-vcf',
type=str,
help='Baseline vcf to provide evaluation metrics against')
parser.add_argument(
'--min-reciprocal-overlap',
type=float,
default=0.5,
help='Minimum reciprocal overlap for validation metrics [0.5]')
parser.add_argument('--padding',
type=int,
default=50,
help='Interval padding for validation metrics [50]')
parser.add_argument(
'--max-warnings',
type=int,
default=50,
help='Maximum number of records to print warnings for [50]')
parser.add_argument('--fp-file',
type=str,
default=None,
help='Write false positives to file')
parser.add_argument('--fn-file',
type=str,
default=None,
help='Write false negatives to file')
# Print help if no arguments specified
if len(argv) == 0:
parser.print_help()
sys.exit(1)
args = parser.parse_args(argv)
if args.baseline_vcf is None and (args.fp_file is not None
or args.fn_file is not None):
raise ValueError(
"FP and FN files cannot be generated if --baseline-vcf isn't specified"
)
types_list = args.types.split(',')
contigs = iou.read_contig_list(args.contig_list)
samples = iou.read_samples_list(args.sample_list)
metrics, fp_intervals, fn_intervals = get_metrics(
args.test_vcf, args.baseline_vcf, contigs, types_list,
args.min_reciprocal_overlap, args.padding, samples, args.metric_prefix,
args.max_warnings)
# Write metrics
write_metrics(metrics)
if args.fp_file is not None and fp_intervals is not None:
write_intervals(args.fp_file, fp_intervals)
if args.fn_file is not None and fn_intervals is not None:
write_intervals(args.fn_file, fn_intervals)