def _normalize_sv_coverage_cnvkit(group_id, inputs, backgrounds, work_dir,
back_files, out_files):
"""Normalize CNV coverage depths by GC, repeats and background using CNVkit
- reference: calculates reference backgrounds from normals and pools
including GC and repeat information
- fix: Uses background to normalize coverage estimations
http://cnvkit.readthedocs.io/en/stable/pipeline.html#fix
"""
from bcbio.structural import cnvkit
cnns = reduce(operator.add, [[
tz.get_in(["depth", "bins", "target"], x),
tz.get_in(["depth", "bins", "antitarget"], x)
] for x in backgrounds], [])
for d in inputs:
if tz.get_in(["depth", "bins", "target"], d):
target_bed = tz.get_in(["depth", "bins", "target"], d)
antitarget_bed = tz.get_in(["depth", "bins", "antitarget"], d)
input_backs = set(
filter(lambda x: x is not None,
[dd.get_background_cnv_reference(d, "cnvkit") for d in inputs]))
if input_backs:
assert len(
input_backs
) == 1, "Multiple backgrounds in group: %s" % list(input_backs)
back_file = list(input_backs)[0]
else:
back_file = cnvkit.cnvkit_background(
cnns,
os.path.join(work_dir, "background-%s-cnvkit.cnn" % (group_id)),
backgrounds or inputs, target_bed, antitarget_bed)
fix_cmd_inputs = []
for data in inputs:
work_dir = utils.safe_makedir(
os.path.join(dd.get_work_dir(data), "structural",
dd.get_sample_name(data), "bins"))
if tz.get_in(["depth", "bins", "target"], data):
fix_file = os.path.join(
work_dir, "%s-normalized.cnr" % (dd.get_sample_name(data)))
fix_cmd_inputs.append((tz.get_in(["depth", "bins", "target"],
data),
tz.get_in(["depth", "bins", "antitarget"],
data), back_file, fix_file, data))
out_files[dd.get_sample_name(data)] = fix_file
back_files[dd.get_sample_name(data)] = back_file
parallel = {
"type": "local",
"cores": dd.get_cores(inputs[0]),
"progs": ["cnvkit"]
}
run_multicore(cnvkit.run_fix_parallel, fix_cmd_inputs, inputs[0]["config"],
parallel)
return back_files, out_files
def normalize_sv_coverage(*items):
"""Normalize CNV coverage depths by GC, repeats and background.
Provides normalized output based on CNVkit approaches, provides a
point for providing additional methods in the future:
- reference: calculates reference backgrounds from normals and pools
including GC and repeat information
- fix: Uses background to normalize coverage estimations
http://cnvkit.readthedocs.io/en/stable/pipeline.html#fix
"""
from bcbio.structural import cnvkit
from bcbio.structural import shared as sshared
orig_items = items
items = [utils.to_single_data(x) for x in cwlutils.handle_combined_input(items)]
if all(not cnvkit.use_general_sv_bins(x) for x in items):
return orig_items
out_files = {}
for group_id, gitems in itertools.groupby(items, lambda x: tz.get_in(["regions", "bins", "group"], x)):
inputs, backgrounds = sshared.find_case_control(list(gitems))
cnns = reduce(operator.add, [[tz.get_in(["depth", "bins", "target"], x),
tz.get_in(["depth", "bins", "antitarget"], x)] for x in backgrounds], [])
assert inputs, "Did not find inputs for sample batch: %s" % (" ".join(dd.get_sample_name(x) for x in items))
for d in inputs:
if tz.get_in(["depth", "bins", "target"], d):
target_bed = tz.get_in(["depth", "bins", "target"], d)
antitarget_bed = tz.get_in(["depth", "bins", "antitarget"], d)
work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(inputs[00]), "structural",
dd.get_sample_name(inputs[0]), "bins"))
back_file = cnvkit.cnvkit_background(cnns, os.path.join(work_dir, "background-%s-cnvkit.cnn" % (group_id)),
backgrounds or inputs, target_bed, antitarget_bed)
for data in inputs:
work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "structural",
dd.get_sample_name(data), "bins"))
if tz.get_in(["depth", "bins", "target"], data):
fix_file = cnvkit.run_fix(tz.get_in(["depth", "bins", "target"], data),
tz.get_in(["depth", "bins", "antitarget"], data),
back_file,
os.path.join(work_dir, "%s-normalized.cnr" % (dd.get_sample_name(data))),
data)
out_files[dd.get_sample_name(data)] = fix_file
out = []
for data in items:
if dd.get_sample_name(data) in out_files:
data["depth"]["bins"]["normalized"] = out_files[dd.get_sample_name(data)]
out.append([data])
return out
def _normalize_sv_coverage_cnvkit(group_id, inputs, backgrounds, work_dir, back_files, out_files):
"""Normalize CNV coverage depths by GC, repeats and background using CNVkit
- reference: calculates reference backgrounds from normals and pools
including GC and repeat information
- fix: Uses background to normalize coverage estimations
http://cnvkit.readthedocs.io/en/stable/pipeline.html#fix
"""
from bcbio.structural import cnvkit
cnns = reduce(operator.add, [[tz.get_in(["depth", "bins", "target"], x),
tz.get_in(["depth", "bins", "antitarget"], x)] for x in backgrounds], [])
for d in inputs:
if tz.get_in(["depth", "bins", "target"], d):
target_bed = tz.get_in(["depth", "bins", "target"], d)
antitarget_bed = tz.get_in(["depth", "bins", "antitarget"], d)
input_backs = set(filter(lambda x: x is not None,
[dd.get_background_cnv_reference(d, "cnvkit") for d in inputs]))
if input_backs:
assert len(input_backs) == 1, "Multiple backgrounds in group: %s" % list(input_backs)
back_file = list(input_backs)[0]
else:
back_file = cnvkit.cnvkit_background(cnns,
os.path.join(work_dir, "background-%s-cnvkit.cnn" % (group_id)),
backgrounds or inputs, target_bed, antitarget_bed)
fix_cmd_inputs = []
for data in inputs:
work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "structural",
dd.get_sample_name(data), "bins"))
if tz.get_in(["depth", "bins", "target"], data):
fix_file = os.path.join(work_dir, "%s-normalized.cnr" % (dd.get_sample_name(data)))
fix_cmd_inputs.append((tz.get_in(["depth", "bins", "target"], data),
tz.get_in(["depth", "bins", "antitarget"], data),
back_file, fix_file, data))
out_files[dd.get_sample_name(data)] = fix_file
back_files[dd.get_sample_name(data)] = back_file
parallel = {"type": "local", "cores": dd.get_cores(inputs[0]), "progs": ["cnvkit"]}
run_multicore(cnvkit.run_fix_parallel, fix_cmd_inputs, inputs[0]["config"], parallel)
return back_files, out_files
def normalize_sv_coverage(*items):
"""Normalize CNV coverage depths by GC, repeats and background.
Provides normalized output based on CNVkit approaches, provides a
point for providing additional methods in the future:
- reference: calculates reference backgrounds from normals and pools
including GC and repeat information
- fix: Uses background to normalize coverage estimations
http://cnvkit.readthedocs.io/en/stable/pipeline.html#fix
"""
from bcbio.structural import cnvkit
from bcbio.structural import shared as sshared
items = [utils.to_single_data(x) for x in cwlutils.handle_combined_input(items)]
if all(not cnvkit.use_general_sv_bins(x) for x in items):
return [[d] for d in items]
out_files = {}
back_files = {}
for group_id, gitems in itertools.groupby(items, lambda x: tz.get_in(["regions", "bins", "group"], x)):
# No CNVkit calling for this particular set of samples
if group_id is None:
continue
inputs, backgrounds = sshared.find_case_control(list(gitems))
cnns = reduce(operator.add, [[tz.get_in(["depth", "bins", "target"], x),
tz.get_in(["depth", "bins", "antitarget"], x)] for x in backgrounds], [])
assert inputs, "Did not find inputs for sample batch: %s" % (" ".join(dd.get_sample_name(x) for x in items))
for d in inputs:
if tz.get_in(["depth", "bins", "target"], d):
target_bed = tz.get_in(["depth", "bins", "target"], d)
antitarget_bed = tz.get_in(["depth", "bins", "antitarget"], d)
work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(inputs[0]), "structural",
dd.get_sample_name(inputs[0]), "bins"))
input_backs = set(filter(lambda x: x is not None,
[dd.get_background_cnv_reference(d) for d in inputs]))
if input_backs:
assert len(input_backs) == 1, "Multiple backgrounds in group: %s" % list(input_backs)
back_file = list(input_backs)[0]
else:
back_file = cnvkit.cnvkit_background(cnns,
os.path.join(work_dir, "background-%s-cnvkit.cnn" % (group_id)),
backgrounds or inputs, target_bed, antitarget_bed)
fix_cmd_inputs = []
for data in inputs:
work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "structural",
dd.get_sample_name(data), "bins"))
if tz.get_in(["depth", "bins", "target"], data):
fix_file = os.path.join(work_dir, "%s-normalized.cnr" % (dd.get_sample_name(data)))
fix_cmd_inputs.append((tz.get_in(["depth", "bins", "target"], data),
tz.get_in(["depth", "bins", "antitarget"], data),
back_file, fix_file, data))
out_files[dd.get_sample_name(data)] = fix_file
back_files[dd.get_sample_name(data)] = back_file
parallel = {"type": "local", "cores": dd.get_cores(inputs[0]), "progs": ["cnvkit"]}
run_multicore(cnvkit.run_fix_parallel, fix_cmd_inputs, inputs[0]["config"], parallel)
out = []
for data in items:
if dd.get_sample_name(data) in out_files:
data["depth"]["bins"]["background"] = back_files[dd.get_sample_name(data)]
data["depth"]["bins"]["normalized"] = out_files[dd.get_sample_name(data)]
out.append([data])
return out