def main():
parser = argparse.ArgumentParser("Somatic Comparison")
parser.add_argument("truth", help="Truth VCF file")
parser.add_argument("query", help="Query VCF file")
parser.add_argument("-o", "--output", dest="output", required=True,
help="Output file prefix for statistics and feature table (when selected)")
parser.add_argument("-l", "--location", dest="location", default="",
help="Location for bcftools view (e.g. chr1)")
parser.add_argument("-R", "--restrict-regions", dest="regions_bedfile",
default=None, type=str,
help="Restrict analysis to given (sparse) regions (using -R in bcftools).")
parser.add_argument("-T", "--target-regions", dest="targets_bedfile",
default=None, type=str,
help="Restrict analysis to given (dense) regions (using -T in bcftools).")
parser.add_argument("-f", "--false-positives", dest="FP",
help="False-positive region bed file to distinguish UNK from FP")
parser.add_argument("-a", "--ambiguous", dest="ambi", action='append',
help="Ambiguous region bed file(s) to distinguish from FP (e.g. variant only observed "
"in some replicates)")
parser.add_argument("--ambi-fp", dest="ambi_fp", action='store_true', default=False,
help="Use FP calls from ambiguous region files also.")
parser.add_argument("--no-ambi-fp", dest="ambi_fp", action='store_false',
help="Do not use FP calls from ambiguous region files also.")
parser.add_argument("--count-unk", dest="count_unk", action='store_true', default=False,
help="Assume the truth set covers the whole genome and only count FPs in regions "
"specified by the truth VCF or ambiguous/false-positive regions.")
parser.add_argument("--no-count-unk", dest="count_unk", action='store_false',
help="Do not use FP calls from ambiguous region files also.")
parser.add_argument("-e", "--explain_ambiguous", dest="explain_ambiguous", required=False,
default=False, action="store_true",
help="print a table giving the number of ambiguous events per category")
parser.add_argument("-r", "--reference", dest="ref", default=Tools.defaultReference(),
help="Specify a reference file.")
parser.add_argument("--scratch-prefix", dest="scratch_prefix",
default=None,
help="Filename prefix for scratch report output.")
parser.add_argument("--keep-scratch", dest="delete_scratch",
default=True, action="store_false",
help="Filename prefix for scratch report output.")
parser.add_argument("--continue", dest="cont", default=False, action="store_true",
help="Continue from scratch space (i.e. use VCFs in there if they already exist).")
parser.add_argument("-P", "--include-nonpass", dest="inc_nonpass", action="store_true", default=False,
help="Use to include failing variants in comparison.")
parser.add_argument("--feature-table", dest="features", default=False, choices=Somatic.FeatureSet.sets.keys(),
help="Select a feature table to output.")
parser.add_argument("--bam", dest="bams", default=[], action="append",
help="pass one or more BAM files for feature table extraction")
parser.add_argument("--normalize-truth", dest="normalize_truth", default=False, action="store_true",
help="Enable running of bcftools norm on the truth file.")
parser.add_argument("--normalize-query", dest="normalize_query", default=False, action="store_true",
help="Enable running of bcftools norm on the query file.")
parser.add_argument("-N", "--normalize-all", dest="normalize_all", default=False, action="store_true",
help="Enable running of bcftools norm on both truth and query file.")
parser.add_argument("--fixchr-truth", dest="fixchr_truth", action="store_true", default=True,
help="Add chr prefix to truth file (default: true).")
parser.add_argument("--fixchr-query", dest="fixchr_query", action="store_true", default=True,
help="Add chr prefix to query file (default: true).")
parser.add_argument("--fix-chr-truth", dest="fixchr_truth", action="store_true", default=None,
help="Same as --fixchr-truth.")
parser.add_argument("--fix-chr-query", dest="fixchr_query", action="store_true", default=None,
help="Same as --fixchr-query.")
parser.add_argument("--no-fixchr-truth", dest="fixchr_truth", action="store_false", default=False,
help="Disable chr replacement for truth (default: false).")
parser.add_argument("--no-fixchr-query", dest="fixchr_query", action="store_false", default=False,
help="Add chr prefix to query file (default: false).")
parser.add_argument("--no-order-check", dest="disable_order_check", default=False, action="store_true",
help="Disable checking the order of TP features (dev feature).")
parser.add_argument("--roc", dest="roc", default=None, choices=ROC.list(),
help="Create a ROC-style table. This is caller specific "
" - this will override the --feature-table switch!")
parser.add_argument("--bin-afs", dest="af_strat", default=None, action="store_true",
help="Stratify into different AF buckets. This needs to have features available"
"for getting the AF both in truth and query variants.")
parser.add_argument("--af-binsize", dest="af_strat_binsize", default=0.2,
help="Bin size for AF binning (should be < 1). Multiple bin sizes can be specified using a comma, "
"e.g. 0.1,0.2,0.5,0.2 will split at 0.1, 0.3, 0.8 and 1.0.")
parser.add_argument("--af-truth", dest="af_strat_truth", default="I.T_ALT_RATE",
help="Feature name to use for retrieving AF for truth variants (TP and FN)")
parser.add_argument("--af-query", dest="af_strat_query", default="T_AF",
help="Feature name to use for retrieving AF for query variants (FP/UNK/AMBI)")
parser.add_argument("-FN", "--count-filtered-fn", dest="count_filtered_fn", action="store_true",
help="Count filtered vs. absent FN numbers. This requires the -P switch (to use all "
"variants) and either the --feature-table or --roc switch.")
parser.add_argument("--fp-region-size", dest="fpr_size",
help="How to obtain the normalisation constant for FP rate. By default, this will use the FP region bed file size when using"
" --count-unk and the size of all reference contigs that overlap with the location specified in -l otherwise."
" This can be overridden with: 1) a number of nucleotides, or 2) \"auto\" to use the lengths of all contigs that have calls."
" The resulting value is used as fp.region.size.")
parser.add_argument("--ci-level", dest="ci_level", default=0.95, type = float,
help="Confidence level for precision/recall confidence intervals (default: 0.95)")
parser.add_argument("--logfile", dest="logfile", default=None,
help="Write logging information into file rather than to stderr")
verbosity_options = parser.add_mutually_exclusive_group(required=False)
verbosity_options.add_argument("--verbose", dest="verbose", default=False, action="store_true",
help="Raise logging level from warning to info.")
verbosity_options.add_argument("--quiet", dest="quiet", default=False, action="store_true",
help="Set logging level to output errors only.")
args = parser.parse_args()
if args.verbose:
loglevel = logging.INFO
elif args.quiet:
loglevel = logging.ERROR
else:
loglevel = logging.WARNING
try:
if type(args.af_strat_binsize) is str:
args.af_strat_binsize = map(float, args.af_strat_binsize.split(","))
else:
args.af_strat_binsize = map(float, [args.af_strat_binsize])
if not args.af_strat_binsize:
raise Exception("Bin size list is empty")
except:
logging.error("Failed to parse stratification bin size: %s" % str(args.af_strat_binsize))
exit(1)
# reinitialize logging
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
logging.basicConfig(filename=args.logfile,
format='%(asctime)s %(levelname)-8s %(message)s',
level=loglevel)
if args.normalize_all:
args.normalize_truth = True
args.normalize_query = True
if args.roc:
args.roc = ROC.make(args.roc)
args.features = args.roc.ftname
if not args.inc_nonpass:
logging.warn("When creating ROCs without the -P switch, the ROC data points will only "
"include filtered variants (i.e. they will normally end at the caller's "
"quality threshold).")
if not (args.ci_level > 0.0 and args.ci_level < 1.0):
raise Exception("Confidence interval level must be > 0.0 and < 1.0.")
if args.af_strat and not args.features:
raise Exception("To stratify by AFs, a feature table must be selected -- use this switch together "
"with --feature-table or --roc")
if args.count_filtered_fn and (not args.inc_nonpass or not args.features):
raise Exception("Counting filtered / unfiltered FNs only works when a feature table is selected, "
"and when using unfiltered variants. Specify -P --feature-table <...> or use "
"--roc to select a ROC type.")
if args.scratch_prefix:
scratch = os.path.abspath(args.scratch_prefix)
args.delete_scratch = False
Tools.mkdir_p(scratch)
else:
scratch = tempfile.mkdtemp()
logging.info("Scratch path is %s" % scratch)
try:
bams = []
md = None
for x in args.bams:
bams.append(bamStats(x))
if bams:
bres = pandas.concat(bams).groupby("CHROM").mean()
md = {}
for x in bres.index:
logging.info("Mean coverage on %s is %f" % (x, bres.loc[x]["COVERAGE"]))
md[x] = float(bres.loc[x]["COVERAGE"]) * 3.0
logging.info("Normalizing/reading inputs")
ntpath = os.path.join(scratch, "normalized_truth.vcf.gz")
if not (args.cont and os.path.exists(ntpath)):
preprocessVCF(args.truth, ntpath, args.location,
True, # pass_only
args.fixchr_truth, # chrprefix
args.normalize_truth, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref)
else:
logging.info("Continuing from %s" % ntpath)
if not (args.cont and os.path.exists(ntpath + ".csi")):
runBcftools("index", ntpath)
nqpath = os.path.join(scratch, "normalized_query.vcf.gz")
if not (args.cont and os.path.exists(nqpath)):
preprocessVCF(args.query, nqpath, args.location,
not args.inc_nonpass, # pass_only
args.fixchr_query, # chrprefix
args.normalize_query, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref)
else:
logging.info("Continuing from %s" % nqpath)
if not (args.cont and os.path.exists(nqpath + ".csi")):
runBcftools("index", nqpath)
logging.info("Intersecting")
tpfn_files = all([os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz"))])
tpfn_r_files = all([os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz"))])
if not (args.cont and tpfn_files):
runBcftools("isec", ntpath, nqpath, "-p", os.path.join(scratch, "tpfn"), "-O", "z")
else:
logging.info("Continuing from %s" % os.path.join(scratch, "tpfn"))
if args.features and not (args.cont and tpfn_r_files):
# only need to do this for getting the feature table
runBcftools("isec", nqpath, ntpath, "-p", os.path.join(scratch, "tpfn_r"), "-O", "z")
logging.info("Getting FPs / Ambi / Unk")
fppath = os.path.join(scratch, "fp.vcf.gz")
unkpath = os.path.join(scratch, "unk.vcf.gz")
ambipath = os.path.join(scratch, "ambi.vcf.gz")
# get header to print to unk and ambi VCFs
rununiquepath = os.path.join(scratch, "tpfn", "0001.vcf.gz")
header = runBcftools("view", rununiquepath, "--header-only")
fp = Tools.BGZipFile(fppath, True)
fp.write(header)
unk = Tools.BGZipFile(unkpath, True)
unk.write(header)
ambi = Tools.BGZipFile(ambipath, True)
ambi.write(header)
ambiClasses = Counter()
ambiReasons = Counter()
fpclasses = BedIntervalTree()
if args.ambi:
# can have multiple ambiguous BED files
for aBED in args.ambi:
# auto-label from first value after chr start end
# new ambi files have the label in position 4
# old ones will look weird here.
fpclasses.addFromBed(aBED, lambda xe: xe[4], args.fixchr_truth)
if args.FP:
fpclasses.addFromBed(args.FP, "FP", args.fixchr_truth)
# split VCF into FP, UNK and AMBI
toProcess = gzip.open(rununiquepath, "rb")
for entry in toProcess:
if entry[0] == '#':
continue
fields = entry.strip().split("\t")
chrom = fields[0]
start = int(fields[1])
stop = int(fields[1]) + len(fields[3])
overlap = fpclasses.intersect(chrom, start, stop)
is_fp = False
is_ambi = False
classes_this_pos = set()
for o in overlap:
reason = o.value[0]
if reason == "fp" and args.ambi_fp:
reason = "FP"
elif reason == "fp":
reason = "ambi-fp"
elif reason == "unk":
reason = "ambi-unk"
classes_this_pos.add(reason)
try:
ambiReasons["%s: rep. count %s" % (reason, o.value[1])] += 1
except IndexError:
ambiReasons["%s: rep. count *" % reason] += 1
for x in o.value[3:]:
ambiReasons["%s: %s" % (reason, x)] += 1
if reason == "FP":
is_fp = True
else:
is_ambi = True
for reason in classes_this_pos:
ambiClasses[reason] += 1
if is_fp:
fp.write(entry)
elif is_ambi:
ambi.write(entry)
elif not args.count_unk:
# when we don't have FP regions, unk stuff becomes FP
fp.write(entry)
else:
unk.write(entry)
toProcess.close()
# since 0001.vcf.gz should already be sorted, we can just convert to bgzipped vcf
# and create index
fp.close()
ambi.close()
unk.close()
runBcftools("index", "--tbi", fppath)
runBcftools("index", "--tbi", unkpath)
runBcftools("index", "--tbi", ambipath)
logging.info("Counting variants...")
truthcounts = parseStats(runBcftools("stats", ntpath), "total.truth")
querycounts = parseStats(runBcftools("stats", nqpath), "total.query")
tpcounts = parseStats(runBcftools("stats", os.path.join(scratch, "tpfn", "0002.vcf.gz")), "tp")
fncounts = parseStats(runBcftools("stats", os.path.join(scratch, "tpfn", "0000.vcf.gz")), "fn")
fpcounts = parseStats(runBcftools("stats", fppath), "fp")
ambicounts = parseStats(runBcftools("stats", ambipath), "ambi")
unkcounts = parseStats(runBcftools("stats", unkpath), "unk")
res = pandas.merge(truthcounts, querycounts, on="type")
res = pandas.merge(res, tpcounts, on="type")
res = pandas.merge(res, fpcounts, on="type")
res = pandas.merge(res, fncounts, on="type")
res = pandas.merge(res, unkcounts, on="type")
res = pandas.merge(res, ambicounts, on="type")
# no explicit guarantee that total.query is equal to unk + ambi + fp + tp
# testSum = res["fp"] + res["tp"] + res["unk"] + res["ambi"]
# filter and relabel
res = res[res["type"] != "samples"]
res = res[res["type"] != "multiallelic SNP sites"]
res = res[res["type"] != "multiallelic sites"]
res.loc[res["type"] == "SNPs", "type"] = "SNVs"
metrics_output = makeMetricsObject("som.py.comparison")
if args.ambi and args.explain_ambiguous:
ac = list(ambiClasses.iteritems())
if ac:
ambie = pandas.DataFrame(ac, columns=["class", "count"])
ambie.sort(["class"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info("FP/ambiguity classes with info (multiple classes can "
"overlap):\n" + ambie.to_string(index=False))
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "FP/ambiguity classes with info (multiple classes can " \
"overlap):\n" + ambie.to_string(index=False)
ambie.to_csv(args.output + ".ambiclasses.csv")
metrics_output["metrics"].append(dataframeToMetricsTable("ambiclasses", ambie))
else:
logging.info("No ambiguous variants.")
ar = list(ambiReasons.iteritems())
if ar:
ambie = pandas.DataFrame(ar, columns=["reason", "count"])
ambie.sort(["reason"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info("Reasons for defining as ambiguous (multiple reasons can overlap):\n" + ambie.to_string(
formatters={'reason': '{{:<{}s}}'.format(ambie['reason'].str.len().max()).format}, index=False))
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "Reasons for defining as ambiguous (multiple reasons can overlap):\n" + ambie.to_string(
formatters={'reason': '{{:<{}s}}'.format(ambie['reason'].str.len().max()).format}, index=False)
ambie.to_csv(args.output + ".ambireasons.csv")
metrics_output["metrics"].append(dataframeToMetricsTable("ambireasons", ambie))
else:
logging.info("No ambiguous variants.")
if args.features:
logging.info("Extracting features...")
fset = Somatic.FeatureSet.make(args.features)
fset.setChrDepths(md)
logging.info("Collecting TP info (1)...")
tps = fset.collect(os.path.join(scratch, "tpfn", "0002.vcf.gz"), "TP")
# TP_r is a hint for fset, they are both TPs
logging.info("Collecting TP info (2)...")
tps2 = fset.collect(os.path.join(scratch, "tpfn_r", "0002.vcf.gz"), "TP_r")
# this is slow because it tries to sort
# ... which we don't need to do since tps1 and tps2 have the same ordering
logging.info("Sorting...")
tps.sort(["CHROM", "POS"], inplace=True)
tps2.sort(["CHROM", "POS"], inplace=True)
tps = tps.reset_index(drop=True)
tps2 = tps2.reset_index(drop=True)
logging.info("Merging TP info...")
columns_tps = list(tps)
columns_tps2 = list(tps2)
len1 = tps.shape[0]
len2 = tps2.shape[0]
if len1 != len2:
raise Exception("Cannot read TP features, lists have different lengths : %i != %i" % (len1, len2))
if not args.disable_order_check:
logging.info("Checking order %i / %i" % (len1, len2))
for x in xrange(0, len1):
for a in ["CHROM", "POS"]:
if tps.loc[x][a] != tps2.loc[x][a]:
raise Exception("Cannot merge TP features, inputs are out of order at %s / %s" % (
str(tps[x:x + 1]), str(tps2[x:x + 1])))
logging.info("Merging...")
cdata = {
"CHROM": tps["CHROM"],
"POS": tps["POS"],
"tag": tps["tag"]
}
tpc = pandas.DataFrame(cdata, columns=["CHROM", "POS", "tag"])
all_columns = list(set(columns_tps + columns_tps2))
for a in all_columns:
if a in columns_tps and a not in columns_tps2:
tpc[a] = tps[a]
elif a not in columns_tps and a in columns_tps2:
tpc[a] = tps2[a]
elif a not in ["CHROM", "POS", "tag"]:
tpc[a] = tps2[a]
tpc[a + ".truth"] = tps[a]
logging.info("Collecting FP info...")
fps = fset.collect(fppath, "FP")
ambs = fset.collect(ambipath, "AMBI")
logging.info("Collecting FN info...")
fns = fset.collect(os.path.join(scratch, "tpfn", "0000.vcf.gz"), "FN")
renamed = {}
tp_cols = list(tpc)
for col in list(fns):
if col + ".truth" in tp_cols:
renamed[col] = col + ".truth"
fns.rename(columns=renamed, inplace=True)
featurelist = [tpc, fps, fns, ambs]
if unkpath is not None:
logging.info("Collecting UNK info...")
unk = fset.collect(unkpath, "UNK")
featurelist.append(unk)
logging.info("Making feature table...")
featuretable = pandas.concat(featurelist)
# reorder to make more legible
first_columns = ["CHROM", "POS", "tag"]
# noinspection PyTypeChecker
all_columns = list(featuretable)
if "REF" in all_columns:
first_columns.append("REF")
if "REF.truth" in all_columns:
first_columns.append("REF.truth")
if "ALT" in all_columns:
first_columns.append("ALT")
if "ALT.truth" in all_columns:
first_columns.append("ALT.truth")
ordered_columns = first_columns + sorted([x for x in all_columns if x not in first_columns])
featuretable = featuretable[ordered_columns]
# make sure positions are integers
featuretable["POS"] = featuretable["POS"].astype(int)
logging.info("Saving feature table...")
featuretable.to_csv(args.output + ".features.csv", float_format='%.8f')
if args.roc is not None:
roc_table = args.roc.from_table(featuretable)
roc_table.to_csv(args.output + ".roc.csv", float_format='%.8f')
featuretable["FILTER"].fillna("", inplace=True)
featuretable.ix[featuretable["REF"].str.len() < 1, "absent"] = True
featuretable.ix[featuretable["tag"] == "FN", "REF"] = featuretable.ix[featuretable["tag"] == "FN",
"REF.truth"]
featuretable.ix[featuretable["tag"] == "FN", "ALT"] = featuretable.ix[featuretable["tag"] == "FN",
"ALT.truth"]
af_t_feature = args.af_strat_truth
af_q_feature = args.af_strat_query
for vtype in ["records", "SNVs", "indels"]:
if vtype == "SNVs":
featuretable_this_type = featuretable[(featuretable["REF"].str.len() > 0) &
(featuretable["ALT"].str.len() ==
featuretable["REF"].str.len())]
elif vtype == "indels":
featuretable_this_type = featuretable[(featuretable["REF"].str.len() != 1) |
(featuretable["ALT"].str.len() != 1)]
else:
featuretable_this_type = featuretable
if args.count_filtered_fn:
res.ix[res["type"] == vtype, "fp.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "FP") & (featuretable_this_type["FILTER"] != "")].shape[0]
res.ix[res["type"] == vtype, "tp.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "TP") & (featuretable_this_type["FILTER"] != "")].shape[0]
res.ix[res["type"] == vtype, "unk.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "UNK") & (featuretable_this_type["FILTER"] != "")].shape[0]
res.ix[res["type"] == vtype, "ambi.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "AMBI") & (featuretable_this_type["FILTER"] != "")].shape[0]
if args.af_strat:
start = 0.0
current_binsize = args.af_strat_binsize[0]
next_binsize = 0
while start < 1.0:
# include 1 in last interval
end = min(1.000000001, start + current_binsize)
n_tp = featuretable_this_type[(featuretable_this_type["tag"] == "TP") &
(featuretable_this_type[af_t_feature] >= start) &
(featuretable_this_type[af_t_feature] < end)]
n_fn = featuretable_this_type[(featuretable_this_type["tag"] == "FN") &
(featuretable_this_type[af_t_feature] >= start) &
(featuretable_this_type[af_t_feature] < end)]
n_fp = featuretable_this_type[(featuretable_this_type["tag"] == "FP") &
(featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
n_ambi = featuretable_this_type[(featuretable_this_type["tag"] == "AMBI") &
(featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
n_unk = featuretable_this_type[(featuretable_this_type["tag"] == "UNK") &
(featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
r = {"type": "%s.%f-%f" % (vtype, start, end),
"total.truth": n_tp.shape[0] + n_fn.shape[0],
"total.query": n_tp.shape[0] + n_fp.shape[0] + n_ambi.shape[0] + n_unk.shape[0],
"tp": n_tp.shape[0],
"fp": n_fp.shape[0],
"fn": n_fn.shape[0],
"unk": n_unk.shape[0],
"ambi": n_ambi.shape[0], }
if args.count_filtered_fn:
r["fp.filtered"] = n_fp[n_fp["FILTER"] != ""].shape[0]
r["tp.filtered"] = n_tp[n_tp["FILTER"] != ""].shape[0]
r["unk.filtered"] = n_unk[n_unk["FILTER"] != ""].shape[0]
r["ambi.filtered"] = n_ambi[n_ambi["FILTER"] != ""].shape[0]
res = pandas.concat([res, pandas.DataFrame([r])])
if args.roc is not None and (n_tp.shape[0] + n_fn.shape[0] + n_fp.shape[0]) > 0:
roc_table_strat = args.roc.from_table(pandas.concat([n_tp, n_fp, n_fn]))
rtname = "%s.%s.%f-%f.roc.csv" % (args.output, vtype, start, end)
roc_table_strat.to_csv(rtname, float_format='%.8f')
start += current_binsize
next_binsize += 1
if next_binsize >= len(args.af_strat_binsize):
next_binsize = 0
current_binsize = args.af_strat_binsize[next_binsize]
# remove things where we haven't seen any variants in truth and query
res = res[(res["total.truth"] > 0) & (res["total.query"] > 0)]
# summary metrics with confidence intervals
ci_alpha = 1.0 - args.ci_level
recall = binomialCI(res["tp"], res["tp"]+res["fn"], ci_alpha)
precision = binomialCI(res["tp"], res["tp"]+res["fp"], ci_alpha)
res["recall"], res["recall_lower"], res["recall_upper"] = recall
res["recall2"] = res["tp"] / (res["total.truth"])
res["precision"], res["precision_lower"], res["precision_upper"] = precision
res["na"] = res["unk"] / (res["total.query"])
res["ambiguous"] = res["ambi"] / res["total.query"]
any_fp = fpclasses.countbases(label="FP")
fp_region_count = 0
auto_size = True
if args.fpr_size:
try:
fp_region_count = int(args.fpr_size)
auto_size = False
except:
pass
if auto_size:
if any_fp:
if args.location:
chrom, _, rest = args.location.partition(":")
if rest:
start, _, end = rest.partition("_")
if start:
start = int(start)
if end:
end = int(end)
else:
fp_region_count += fpclasses.countbases(chrom, label="FP")
else:
fp_region_count = any_fp
else:
cs = fastaContigLengths(args.ref)
if args.location:
fp_region_count = calculateLength(cs, args.location)
else:
# use all locations we saw calls on
h1 = Tools.vcfextract.extractHeadersJSON(ntpath)
h1_chrs = h1["tabix"]["chromosomes"]
if not h1_chrs:
logging.warn("ntpath is empty")
h1_chrs = []
h2 = Tools.vcfextract.extractHeadersJSON(nqpath)
h2_chrs = h2["tabix"]["chromosomes"]
if not h2_chrs:
logging.warn("nqpath is empty")
h2_chrs = []
combined_chrs = list(set(h1_chrs + h2_chrs))
if len(combined_chrs) > 0:
qlocations = " ".join(combined_chrs)
fp_region_count = calculateLength(cs, qlocations)
else:
fp_region_count = 0
res["fp.region.size"] = fp_region_count
res["fp.rate"] = 1e6 * res["fp"] / res["fp.region.size"]
if args.count_filtered_fn:
res["recall.filtered"] = (res["tp"] - res["tp.filtered"]) / (res["tp"] + res["fn"])
res["precision.filtered"] = (res["tp"] - res["tp.filtered"]) / (res["tp"] - res["tp.filtered"] +
res["fp"] - res["fp.filtered"])
res["fp.rate.filtered"] = 1e6 * (res["fp"] - res["fp.filtered"]) / res["fp.region.size"]
res["na.filtered"] = (res["unk"] - res["unk.filtered"]) / (res["total.query"])
res["ambiguous.filtered"] = (res["ambi"] - res["ambi.filtered"]) / res["total.query"]
# HAP-162 remove inf values
res.replace([np.inf, -np.inf], 0)
metrics_output["metrics"].append(dataframeToMetricsTable("result", res))
vstring = "som.py-%s" % Tools.version
logging.info("\n" + res.to_string())
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "\n" + res.to_string()
res["sompyversion"] = vstring
vstring = " ".join(sys.argv)
res["sompycmd"] = vstring
res.to_csv(args.output + ".stats.csv")
with open(args.output + ".metrics.json", "w") as fp:
json.dump(metrics_output, fp)
finally:
if args.delete_scratch:
shutil.rmtree(scratch)
else:
logging.info("Scratch kept at %s" % scratch)
def main():
parser = argparse.ArgumentParser("Somatic Comparison")
parser.add_argument("truth", help="Truth VCF file")
parser.add_argument("query", help="Query VCF file")
parser.add_argument("-o", "--output", dest="output", required=True,
help="Output file prefix for statistics and feature table (when selected)")
parser.add_argument("-l", "--location", dest="location", default="",
help="Location for bcftools view (e.g. chr1)")
parser.add_argument("-R", "--restrict-regions", dest="regions_bedfile",
default=None, type=str,
help="Restrict analysis to given (sparse) regions (using -R in bcftools).")
parser.add_argument("-T", "--target-regions", dest="targets_bedfile",
default=None, type=str,
help="Restrict analysis to given (dense) regions (using -T in bcftools).")
parser.add_argument("-f", "--false-positives", dest="FP",
help="False-positive region bed file to distinguish UNK from FP")
parser.add_argument("-a", "--ambiguous", dest="ambi", action='append',
help="Ambiguous region bed file(s) to distinguish from FP (e.g. variant only observed "
"in some replicates)")
parser.add_argument("--ambi-fp", dest="ambi_fp", action='store_true', default=False,
help="Use FP calls from ambiguous region files also.")
parser.add_argument("--no-ambi-fp", dest="ambi_fp", action='store_false',
help="Do not use FP calls from ambiguous region files also.")
parser.add_argument("--count-unk", dest="count_unk", action='store_true', default=False,
help="Assume the truth set covers the whole genome and only count FPs in regions "
"specified by the truth VCF or ambiguous/false-positive regions.")
parser.add_argument("--no-count-unk", dest="count_unk", action='store_false',
help="Do not use FP calls from ambiguous region files also.")
parser.add_argument("-e", "--explain_ambiguous", dest="explain_ambiguous", required=False,
default=False, action="store_true",
help="print a table giving the number of ambiguous events per category")
parser.add_argument("-r", "--reference", dest="ref", default=Tools.defaultReference(),
help="Specify a reference file.")
parser.add_argument("--scratch-prefix", dest="scratch_prefix",
default=None,
help="Filename prefix for scratch report output.")
parser.add_argument("--keep-scratch", dest="delete_scratch",
default=True, action="store_false",
help="Filename prefix for scratch report output.")
parser.add_argument("--continue", dest="cont", default=False, action="store_true",
help="Continue from scratch space (i.e. use VCFs in there if they already exist).")
parser.add_argument("-P", "--include-nonpass", dest="inc_nonpass", action="store_true", default=False,
help="Use to include failing variants in comparison.")
parser.add_argument("--feature-table", dest="features", default=False, choices=Somatic.FeatureSet.sets.keys(),
help="Select a feature table to output.")
parser.add_argument("--bam", dest="bams", default=[], action="append",
help="pass one or more BAM files for feature table extraction")
parser.add_argument("--normalize-truth", dest="normalize_truth", default=False, action="store_true",
help="Enable running of bcftools norm on the truth file.")
parser.add_argument("--normalize-query", dest="normalize_query", default=False, action="store_true",
help="Enable running of bcftools norm on the query file.")
parser.add_argument("-N", "--normalize-all", dest="normalize_all", default=False, action="store_true",
help="Enable running of bcftools norm on both truth and query file.")
parser.add_argument("--fixchr-truth", dest="fixchr_truth", action="store_true", default=True,
help="Add chr prefix to truth file (default: true).")
parser.add_argument("--fixchr-query", dest="fixchr_query", action="store_true", default=True,
help="Add chr prefix to query file (default: true).")
parser.add_argument("--fix-chr-truth", dest="fixchr_truth", action="store_true", default=None,
help="Same as --fixchr-truth.")
parser.add_argument("--fix-chr-query", dest="fixchr_query", action="store_true", default=None,
help="Same as --fixchr-query.")
parser.add_argument("--no-fixchr-truth", dest="fixchr_truth", action="store_false", default=False,
help="Disable chr replacement for truth (default: false).")
parser.add_argument("--no-fixchr-query", dest="fixchr_query", action="store_false", default=False,
help="Add chr prefix to query file (default: false).")
parser.add_argument("--no-order-check", dest="disable_order_check", default=False, action="store_true",
help="Disable checking the order of TP features (dev feature).")
parser.add_argument("--roc", dest="roc", default=None, choices=ROC.list(),
help="Create a ROC-style table. This is caller specific "
" - this will override the --feature-table switch!")
parser.add_argument("--bin-afs", dest="af_strat", default=None, action="store_true",
help="Stratify into different AF buckets. This needs to have features available"
"for getting the AF both in truth and query variants.")
parser.add_argument("--af-binsize", dest="af_strat_binsize", default=0.2,
help="Bin size for AF binning (should be < 1). Multiple bin sizes can be specified using a comma, "
"e.g. 0.1,0.2,0.5,0.2 will split at 0.1, 0.3, 0.8 and 1.0.")
parser.add_argument("--af-truth", dest="af_strat_truth", default="I.T_ALT_RATE",
help="Feature name to use for retrieving AF for truth variants (TP and FN)")
parser.add_argument("--af-query", dest="af_strat_query", default="T_AF",
help="Feature name to use for retrieving AF for query variants (FP/UNK/AMBI)")
parser.add_argument("-FN", "--count-filtered-fn", dest="count_filtered_fn", action="store_true",
help="Count filtered vs. absent FN numbers. This requires the -P switch (to use all "
"variants) and either the --feature-table or --roc switch.")
parser.add_argument("--fp-region-size", dest="fpr_size",
help="How to obtain the normalisation constant for FP rate. By default, this will use the FP region bed file size when using"
" --count-unk and the size of all reference contigs that overlap with the location specified in -l otherwise."
" This can be overridden with: 1) a number of nucleotides, or 2) \"auto\" to use the lengths of all contigs that have calls."
" The resulting value is used as fp.region.size.")
parser.add_argument("--logfile", dest="logfile", default=None,
help="Write logging information into file rather than to stderr")
verbosity_options = parser.add_mutually_exclusive_group(required=False)
verbosity_options.add_argument("--verbose", dest="verbose", default=False, action="store_true",
help="Raise logging level from warning to info.")
verbosity_options.add_argument("--quiet", dest="quiet", default=False, action="store_true",
help="Set logging level to output errors only.")
args = parser.parse_args()
if args.verbose:
loglevel = logging.INFO
elif args.quiet:
loglevel = logging.ERROR
else:
loglevel = logging.WARNING
try:
if type(args.af_strat_binsize) is str:
args.af_strat_binsize = map(float, args.af_strat_binsize.split(","))
else:
args.af_strat_binsize = map(float, [args.af_strat_binsize])
if not args.af_strat_binsize:
raise Exception("Bin size list is empty")
except:
logging.error("Failed to parse stratification bin size: %s" % str(args.af_strat_binsize))
exit(1)
# reinitialize logging
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
logging.basicConfig(filename=args.logfile,
format='%(asctime)s %(levelname)-8s %(message)s',
level=loglevel)
if args.normalize_all:
args.normalize_truth = True
args.normalize_query = True
if args.roc:
args.roc = ROC.make(args.roc)
args.features = args.roc.ftname
if not args.inc_nonpass:
logging.warn("When creating ROCs without the -P switch, the ROC data points will only "
"include filtered variants (i.e. they will normally end at the caller's "
"quality threshold).")
if args.af_strat and not args.features:
raise Exception("To stratify by AFs, a feature table must be selected -- use this switch together "
"with --feature-table or --roc")
if args.count_filtered_fn and (not args.inc_nonpass or not args.features):
raise Exception("Counting filtered / unfiltered FNs only works when a feature table is selected, "
"and when using unfiltered variants. Specify -P --feature-table <...> or use "
"--roc to select a ROC type.")
if args.scratch_prefix:
scratch = os.path.abspath(args.scratch_prefix)
args.delete_scratch = False
Tools.mkdir_p(scratch)
else:
scratch = tempfile.mkdtemp()
logging.info("Scratch path is %s" % scratch)
try:
bams = []
md = None
for x in args.bams:
bams.append(bamStats(x))
if bams:
bres = pandas.concat(bams).groupby("CHROM").mean()
md = {}
for x in bres.index:
logging.info("Mean coverage on %s is %f" % (x, bres.loc[x]["COVERAGE"]))
md[x] = float(bres.loc[x]["COVERAGE"]) * 3.0
logging.info("Normalizing/reading inputs")
ntpath = os.path.join(scratch, "normalized_truth.vcf.gz")
if not (args.cont and os.path.exists(ntpath)):
preprocessVCF(args.truth, ntpath, args.location,
True, # pass_only
args.fixchr_truth, # chrprefix
args.normalize_truth, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref)
else:
logging.info("Continuing from %s" % ntpath)
if not (args.cont and os.path.exists(ntpath + ".csi")):
runBcftools("index", ntpath)
nqpath = os.path.join(scratch, "normalized_query.vcf.gz")
if not (args.cont and os.path.exists(nqpath)):
preprocessVCF(args.query, nqpath, args.location,
not args.inc_nonpass, # pass_only
args.fixchr_query, # chrprefix
args.normalize_query, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref)
else:
logging.info("Continuing from %s" % nqpath)
if not (args.cont and os.path.exists(nqpath + ".csi")):
runBcftools("index", nqpath)
logging.info("Intersecting")
tpfn_files = all([os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz"))])
tpfn_r_files = all([os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz"))])
if not (args.cont and tpfn_files):
runBcftools("isec", ntpath, nqpath, "-p", os.path.join(scratch, "tpfn"), "-O", "z")
else:
logging.info("Continuing from %s" % os.path.join(scratch, "tpfn"))
if args.features and not (args.cont and tpfn_r_files):
# only need to do this for getting the feature table
runBcftools("isec", nqpath, ntpath, "-p", os.path.join(scratch, "tpfn_r"), "-O", "z")
logging.info("Getting FPs / Ambi / Unk")
fppath = os.path.join(scratch, "fp.vcf.gz")
unkpath = os.path.join(scratch, "unk.vcf.gz")
ambipath = os.path.join(scratch, "ambi.vcf.gz")
# get header to print to unk and ambi VCFs
rununiquepath = os.path.join(scratch, "tpfn", "0001.vcf.gz")
header = runBcftools("view", rununiquepath, "--header-only")
fp = Tools.BGZipFile(fppath, True)
fp.write(header)
unk = Tools.BGZipFile(unkpath, True)
unk.write(header)
ambi = Tools.BGZipFile(ambipath, True)
ambi.write(header)
ambiClasses = Counter()
ambiReasons = Counter()
fpclasses = BedIntervalTree()
if args.ambi:
# can have multiple ambiguous BED files
for aBED in args.ambi:
# auto-label from first value after chr start end
# new ambi files have the label in position 4
# old ones will look weird here.
fpclasses.addFromBed(aBED, lambda xe: xe[4], args.fixchr_truth)
if args.FP:
fpclasses.addFromBed(args.FP, "FP", args.fixchr_truth)
# split VCF into FP, UNK and AMBI
toProcess = gzip.open(rununiquepath, "rb")
for entry in toProcess:
if entry[0] == '#':
continue
fields = entry.strip().split("\t")
chrom = fields[0]
start = int(fields[1])
stop = int(fields[1]) + len(fields[3])
overlap = fpclasses.intersect(chrom, start, stop)
is_fp = False
is_ambi = False
classes_this_pos = set()
for o in overlap:
reason = o.value[0]
if reason == "fp" and args.ambi_fp:
reason = "FP"
elif reason == "fp":
reason = "ambi-fp"
elif reason == "unk":
reason = "ambi-unk"
classes_this_pos.add(reason)
try:
ambiReasons["%s: rep. count %s" % (reason, o.value[1])] += 1
except IndexError:
ambiReasons["%s: rep. count *" % reason] += 1
for x in o.value[3:]:
ambiReasons["%s: %s" % (reason, x)] += 1
if reason == "FP":
is_fp = True
else:
is_ambi = True
for reason in classes_this_pos:
ambiClasses[reason] += 1
if is_fp:
fp.write(entry)
elif is_ambi:
ambi.write(entry)
elif not args.count_unk:
# when we don't have FP regions, unk stuff becomes FP
fp.write(entry)
else:
unk.write(entry)
toProcess.close()
# since 0001.vcf.gz should already be sorted, we can just convert to bgzipped vcf
# and create index
fp.close()
ambi.close()
unk.close()
runBcftools("index", "--tbi", fppath)
runBcftools("index", "--tbi", unkpath)
runBcftools("index", "--tbi", ambipath)
logging.info("Counting variants...")
truthcounts = parseStats(runBcftools("stats", ntpath), "total.truth")
querycounts = parseStats(runBcftools("stats", nqpath), "total.query")
tpcounts = parseStats(runBcftools("stats", os.path.join(scratch, "tpfn", "0002.vcf.gz")), "tp")
fncounts = parseStats(runBcftools("stats", os.path.join(scratch, "tpfn", "0000.vcf.gz")), "fn")
fpcounts = parseStats(runBcftools("stats", fppath), "fp")
ambicounts = parseStats(runBcftools("stats", ambipath), "ambi")
unkcounts = parseStats(runBcftools("stats", unkpath), "unk")
res = pandas.merge(truthcounts, querycounts, on="type")
res = pandas.merge(res, tpcounts, on="type")
res = pandas.merge(res, fpcounts, on="type")
res = pandas.merge(res, fncounts, on="type")
res = pandas.merge(res, unkcounts, on="type")
res = pandas.merge(res, ambicounts, on="type")
# no explicit guarantee that total.query is equal to unk + ambi + fp + tp
# testSum = res["fp"] + res["tp"] + res["unk"] + res["ambi"]
# filter and relabel
res = res[res["type"] != "samples"]
res = res[res["type"] != "multiallelic SNP sites"]
res = res[res["type"] != "multiallelic sites"]
res.loc[res["type"] == "SNPs", "type"] = "SNVs"
metrics_output = makeMetricsObject("som.py.comparison")
if args.ambi and args.explain_ambiguous:
ac = list(ambiClasses.iteritems())
if ac:
ambie = pandas.DataFrame(ac, columns=["class", "count"])
ambie.sort(["class"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info("FP/ambiguity classes with info (multiple classes can "
"overlap):\n" + ambie.to_string(index=False))
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "FP/ambiguity classes with info (multiple classes can " \
"overlap):\n" + ambie.to_string(index=False)
ambie.to_csv(args.output + ".ambiclasses.csv")
metrics_output["metrics"].append(dataframeToMetricsTable("ambiclasses", ambie))
else:
logging.info("No ambiguous variants.")
ar = list(ambiReasons.iteritems())
if ar:
ambie = pandas.DataFrame(ar, columns=["reason", "count"])
ambie.sort(["reason"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info("Reasons for defining as ambiguous (multiple reasons can overlap):\n" + ambie.to_string(
formatters={'reason': '{{:<{}s}}'.format(ambie['reason'].str.len().max()).format}, index=False))
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "Reasons for defining as ambiguous (multiple reasons can overlap):\n" + ambie.to_string(
formatters={'reason': '{{:<{}s}}'.format(ambie['reason'].str.len().max()).format}, index=False)
ambie.to_csv(args.output + ".ambireasons.csv")
metrics_output["metrics"].append(dataframeToMetricsTable("ambireasons", ambie))
else:
logging.info("No ambiguous variants.")
if args.features:
logging.info("Extracting features...")
fset = Somatic.FeatureSet.make(args.features)
fset.setChrDepths(md)
logging.info("Collecting TP info (1)...")
tps = fset.collect(os.path.join(scratch, "tpfn", "0002.vcf.gz"), "TP")
# TP_r is a hint for fset, they are both TPs
logging.info("Collecting TP info (2)...")
tps2 = fset.collect(os.path.join(scratch, "tpfn_r", "0002.vcf.gz"), "TP_r")
# this is slow because it tries to sort
# ... which we don't need to do since tps1 and tps2 have the same ordering
logging.info("Sorting...")
tps.sort(["CHROM", "POS"], inplace=True)
tps2.sort(["CHROM", "POS"], inplace=True)
tps = tps.reset_index(drop=True)
tps2 = tps2.reset_index(drop=True)
logging.info("Merging TP info...")
columns_tps = list(tps)
columns_tps2 = list(tps2)
len1 = tps.shape[0]
len2 = tps2.shape[0]
if len1 != len2:
raise Exception("Cannot read TP features, lists have different lengths : %i != %i" % (len1, len2))
if not args.disable_order_check:
logging.info("Checking order %i / %i" % (len1, len2))
for x in xrange(0, len1):
for a in ["CHROM", "POS"]:
if tps.loc[x][a] != tps2.loc[x][a]:
raise Exception("Cannot merge TP features, inputs are out of order at %s / %s" % (
str(tps[x:x + 1]), str(tps2[x:x + 1])))
logging.info("Merging...")
cdata = {
"CHROM": tps["CHROM"],
"POS": tps["POS"],
"tag": tps["tag"]
}
tpc = pandas.DataFrame(cdata, columns=["CHROM", "POS", "tag"])
all_columns = list(set(columns_tps + columns_tps2))
for a in all_columns:
if a in columns_tps and a not in columns_tps2:
tpc[a] = tps[a]
elif a not in columns_tps and a in columns_tps2:
tpc[a] = tps2[a]
elif a not in ["CHROM", "POS", "tag"]:
tpc[a] = tps2[a]
tpc[a + ".truth"] = tps[a]
logging.info("Collecting FP info...")
fps = fset.collect(fppath, "FP")
ambs = fset.collect(ambipath, "AMBI")
logging.info("Collecting FN info...")
fns = fset.collect(os.path.join(scratch, "tpfn", "0000.vcf.gz"), "FN")
renamed = {}
tp_cols = list(tpc)
for col in list(fns):
if col + ".truth" in tp_cols:
renamed[col] = col + ".truth"
fns.rename(columns=renamed, inplace=True)
featurelist = [tpc, fps, fns, ambs]
if unkpath is not None:
logging.info("Collecting UNK info...")
unk = fset.collect(unkpath, "UNK")
featurelist.append(unk)
logging.info("Making feature table...")
featuretable = pandas.concat(featurelist)
# reorder to make more legible
first_columns = ["CHROM", "POS", "tag"]
# noinspection PyTypeChecker
all_columns = list(featuretable)
if "REF" in all_columns:
first_columns.append("REF")
if "REF.truth" in all_columns:
first_columns.append("REF.truth")
if "ALT" in all_columns:
first_columns.append("ALT")
if "ALT.truth" in all_columns:
first_columns.append("ALT.truth")
ordered_columns = first_columns + sorted([x for x in all_columns if x not in first_columns])
featuretable = featuretable[ordered_columns]
# make sure positions are integers
featuretable["POS"] = featuretable["POS"].astype(int)
logging.info("Saving feature table...")
featuretable.to_csv(args.output + ".features.csv", float_format='%.8f')
if args.roc is not None:
roc_table = args.roc.from_table(featuretable)
roc_table.to_csv(args.output + ".roc.csv", float_format='%.8f')
featuretable["FILTER"].fillna("", inplace=True)
featuretable.ix[featuretable["REF"].str.len() < 1, "absent"] = True
featuretable.ix[featuretable["tag"] == "FN", "REF"] = featuretable.ix[featuretable["tag"] == "FN",
"REF.truth"]
featuretable.ix[featuretable["tag"] == "FN", "ALT"] = featuretable.ix[featuretable["tag"] == "FN",
"ALT.truth"]
af_t_feature = args.af_strat_truth
af_q_feature = args.af_strat_query
for vtype in ["records", "SNVs", "indels"]:
if vtype == "SNVs":
featuretable_this_type = featuretable[(featuretable["REF"].str.len() > 0) &
(featuretable["ALT"].str.len() ==
featuretable["REF"].str.len())]
elif vtype == "indels":
featuretable_this_type = featuretable[(featuretable["REF"].str.len() != 1) |
(featuretable["ALT"].str.len() != 1)]
else:
featuretable_this_type = featuretable
if args.count_filtered_fn:
res.ix[res["type"] == vtype, "fp.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "FP") & (featuretable_this_type["FILTER"] != "")].shape[0]
res.ix[res["type"] == vtype, "tp.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "TP") & (featuretable_this_type["FILTER"] != "")].shape[0]
res.ix[res["type"] == vtype, "unk.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "UNK") & (featuretable_this_type["FILTER"] != "")].shape[0]
res.ix[res["type"] == vtype, "ambi.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "AMBI") & (featuretable_this_type["FILTER"] != "")].shape[0]
if args.af_strat:
start = 0.0
current_binsize = args.af_strat_binsize[0]
next_binsize = 0
while start < 1.0:
# include 1 in last interval
end = min(1.000000001, start + current_binsize)
n_tp = featuretable_this_type[(featuretable_this_type["tag"] == "TP") &
(featuretable_this_type[af_t_feature] >= start) &
(featuretable_this_type[af_t_feature] < end)]
n_fn = featuretable_this_type[(featuretable_this_type["tag"] == "FN") &
(featuretable_this_type[af_t_feature] >= start) &
(featuretable_this_type[af_t_feature] < end)]
n_fp = featuretable_this_type[(featuretable_this_type["tag"] == "FP") &
(featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
n_ambi = featuretable_this_type[(featuretable_this_type["tag"] == "AMBI") &
(featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
n_unk = featuretable_this_type[(featuretable_this_type["tag"] == "UNK") &
(featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
r = {"type": "%s.%f-%f" % (vtype, start, end),
"total.truth": n_tp.shape[0] + n_fn.shape[0],
"total.query": n_tp.shape[0] + n_fp.shape[0] + n_ambi.shape[0] + n_unk.shape[0],
"tp": n_tp.shape[0],
"fp": n_fp.shape[0],
"fn": n_fn.shape[0],
"unk": n_unk.shape[0],
"ambi": n_ambi.shape[0], }
if args.count_filtered_fn:
r["fp.filtered"] = n_fp[n_fp["FILTER"] != ""].shape[0]
r["tp.filtered"] = n_tp[n_tp["FILTER"] != ""].shape[0]
r["unk.filtered"] = n_unk[n_unk["FILTER"] != ""].shape[0]
r["ambi.filtered"] = n_ambi[n_ambi["FILTER"] != ""].shape[0]
res = pandas.concat([res, pandas.DataFrame([r])])
if args.roc is not None and (n_tp.shape[0] + n_fn.shape[0] + n_fp.shape[0]) > 0:
roc_table_strat = args.roc.from_table(pandas.concat([n_tp, n_fp, n_fn]))
rtname = "%s.%s.%f-%f.roc.csv" % (args.output, vtype, start, end)
roc_table_strat.to_csv(rtname, float_format='%.8f')
start += current_binsize
next_binsize += 1
if next_binsize >= len(args.af_strat_binsize):
next_binsize = 0
current_binsize = args.af_strat_binsize[next_binsize]
# remove things where we haven't seen any variants in truth and query
res = res[(res["total.truth"] > 0) & (res["total.query"] > 0)]
# summary metrics
res["recall"] = res["tp"] / (res["tp"] + res["fn"])
res["recall2"] = res["tp"] / (res["total.truth"])
res["precision"] = res["tp"] / (res["tp"] + res["fp"])
res["na"] = res["unk"] / (res["total.query"])
res["ambiguous"] = res["ambi"] / res["total.query"]
any_fp = fpclasses.countbases(label="FP")
fp_region_count = 0
auto_size = True
if args.fpr_size:
try:
fp_region_count = int(args.fpr_size)
auto_size = False
except:
pass
if auto_size:
if any_fp:
if args.location:
chrom, _, rest = args.location.partition(":")
if rest:
start, _, end = rest.partition("_")
if start:
start = int(start)
if end:
end = int(end)
else:
fp_region_count += fpclasses.countbases(chrom, label="FP")
else:
fp_region_count = any_fp
else:
cs = fastaContigLengths(args.ref)
if args.location:
fp_region_count = calculateLength(cs, args.location)
else:
# use all locations we saw calls on
h1 = Tools.vcfextract.extractHeadersJSON(ntpath)
h1_chrs = h1["tabix"]["chromosomes"]
if not h1_chrs:
logging.warn("ntpath is empty")
h1_chrs = []
h2 = Tools.vcfextract.extractHeadersJSON(nqpath)
h2_chrs = h2["tabix"]["chromosomes"]
if not h2_chrs:
logging.warn("nqpath is empty")
h2_chrs = []
combined_chrs = list(set(h1_chrs + h2_chrs))
if len(combined_chrs) > 0:
qlocations = " ".join(combined_chrs)
fp_region_count = calculateLength(cs, qlocations)
else:
fp_region_count = 0
res["fp.region.size"] = fp_region_count
res["fp.rate"] = 1e6 * res["fp"] / res["fp.region.size"]
if args.count_filtered_fn:
res["recall.filtered"] = (res["tp"] - res["tp.filtered"]) / (res["tp"] + res["fn"])
res["precision.filtered"] = (res["tp"] - res["tp.filtered"]) / (res["tp"] - res["tp.filtered"] +
res["fp"] - res["fp.filtered"])
res["fp.rate.filtered"] = 1e6 * (res["fp"] - res["fp.filtered"]) / res["fp.region.size"]
res["na.filtered"] = (res["unk"] - res["unk.filtered"]) / (res["total.query"])
res["ambiguous.filtered"] = (res["ambi"] - res["ambi.filtered"]) / res["total.query"]
# HAP-162 remove inf values
res.replace([np.inf, -np.inf], 0)
metrics_output["metrics"].append(dataframeToMetricsTable("result", res))
vstring = "som.py-%s" % Tools.version
logging.info("\n" + res.to_string())
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "\n" + res.to_string()
res["sompyversion"] = vstring
vstring = " ".join(sys.argv)
res["sompycmd"] = vstring
res.to_csv(args.output + ".stats.csv")
with open(args.output + ".metrics.json", "w") as fp:
json.dump(metrics_output, fp)
finally:
if args.delete_scratch:
shutil.rmtree(scratch)
else:
logging.info("Scratch kept at %s" % scratch)
def main():
parser = argparse.ArgumentParser("Somatic Comparison")
parser.add_argument("truth", help="Truth VCF file")
parser.add_argument("query", help="Query VCF file")
parser.add_argument(
"-o",
"--output",
dest="output",
required=True,
help="Output file prefix for statistics and feature table (when selected)",
)
parser.add_argument("-l", "--location", dest="location", default="", help="Location for bcftools view (e.g. chr1)")
parser.add_argument(
"-R",
"--restrict-regions",
dest="regions_bedfile",
default=None,
type=str,
help="Restrict analysis to given (sparse) regions (using -R in bcftools).",
)
parser.add_argument(
"-T",
"--target-regions",
dest="targets_bedfile",
default=None,
type=str,
help="Restrict analysis to given (dense) regions (using -T in bcftools).",
)
parser.add_argument(
"-f", "--false-positives", dest="FP", help="False-positive region bed file to distinguish UNK from FP"
)
parser.add_argument(
"-a",
"--ambiguous",
dest="ambi",
action="append",
help="Ambiguous region bed file(s) to distinguish from FP (e.g. variant only observed " "in some replicates)",
)
parser.add_argument(
"--ambiguous-fp",
dest="ambi_fp",
action="store_true",
default=False,
help="Use FP calls from ambiguous region files also.",
)
parser.add_argument(
"-e",
"--explain_ambiguous",
dest="explain_ambiguous",
required=False,
default=False,
action="store_true",
help="print a table giving the number of ambiguous events per category",
)
parser.add_argument(
"-r", "--reference", dest="ref", default=Tools.defaultReference(), help="Specify a reference file."
)
parser.add_argument(
"--scratch-prefix", dest="scratch_prefix", default=None, help="Filename prefix for scratch report output."
)
parser.add_argument(
"--keep-scratch",
dest="delete_scratch",
default=True,
action="store_false",
help="Filename prefix for scratch report output.",
)
parser.add_argument(
"--continue",
dest="cont",
default=False,
action="store_true",
help="Continue from scratch space (i.e. use VCFs in there if they already exist).",
)
parser.add_argument(
"-P",
"--include-nonpass",
dest="inc_nonpass",
action="store_true",
default=False,
help="Use to include failing variants in comparison.",
)
parser.add_argument(
"--feature-table",
dest="features",
default=False,
choices=Somatic.FeatureSet.sets.keys(),
help="Select a feature table to output.",
)
parser.add_argument(
"--bam",
dest="bams",
default=[],
action="append",
help="pass one or more BAM files for feature table extraction",
)
parser.add_argument(
"--normalize-truth",
dest="normalize_truth",
default=False,
action="store_true",
help="Enable running of bcftools norm on the truth file.",
)
parser.add_argument(
"--normalize-query",
dest="normalize_query",
default=False,
action="store_true",
help="Enable running of bcftools norm on the query file.",
)
parser.add_argument(
"-N",
"--normalize-all",
dest="normalize_all",
default=False,
action="store_true",
help="Enable running of bcftools norm on both truth and query file.",
)
parser.add_argument(
"--fix-chr-query",
dest="fixchr_query",
default=False,
action="store_true",
help="Replace numeric chromosome names in the query by chr*-type names",
)
parser.add_argument(
"--fix-chr-truth",
dest="fixchr_truth",
default=False,
action="store_true",
help="Replace numeric chromosome names in the truth by chr*-type names",
)
parser.add_argument(
"--no-order-check",
dest="disable_order_check",
default=False,
action="store_true",
help="Disable checking the order of TP features (dev feature).",
)
parser.add_argument(
"--roc",
dest="roc",
default=None,
choices=ROC.list(),
help="Create a ROC-style table. This is caller specific " " - this will override the --feature-table switch!",
)
parser.add_argument(
"--logfile", dest="logfile", default=None, help="Write logging information into file rather than to stderr"
)
verbosity_options = parser.add_mutually_exclusive_group(required=False)
verbosity_options.add_argument(
"--verbose",
dest="verbose",
default=False,
action="store_true",
help="Raise logging level from warning to info.",
)
verbosity_options.add_argument(
"--quiet", dest="quiet", default=False, action="store_true", help="Set logging level to output errors only."
)
args = parser.parse_args()
if args.verbose:
loglevel = logging.INFO
elif args.quiet:
loglevel = logging.ERROR
else:
loglevel = logging.WARNING
# reinitialize logging
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
logging.basicConfig(filename=args.logfile, format="%(asctime)s %(levelname)-8s %(message)s", level=loglevel)
if args.normalize_all:
args.normalize_truth = True
args.normalize_query = True
if args.roc:
args.roc = ROC.make(args.roc)
args.features = args.roc.ftname
if args.scratch_prefix:
scratch = os.path.abspath(args.scratch_prefix)
args.delete_scratch = False
Tools.mkdir_p(scratch)
else:
scratch = tempfile.mkdtemp()
logging.info("Scratch path is %s" % scratch)
try:
bams = []
md = None
for x in args.bams:
bams.append(bamStats(x))
if bams:
bres = pandas.concat(bams).groupby("CHROM").mean()
md = {}
for x in bres.index:
logging.info("Mean coverage on %s is %f" % (x, bres.loc[x]["COVERAGE"]))
md[x] = float(bres.loc[x]["COVERAGE"]) * 3.0
logging.info("Normalizing/reading inputs")
ntpath = os.path.join(scratch, "normalized_truth.vcf.gz")
if not (args.cont and os.path.exists(ntpath)):
preprocessVCF(
args.truth,
ntpath,
args.location,
True, # pass_only
args.fixchr_truth, # chrprefix
args.normalize_truth, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref,
)
else:
logging.info("Continuing from %s" % ntpath)
if not (args.cont and os.path.exists(ntpath + ".csi")):
runBcftools("index", ntpath)
nqpath = os.path.join(scratch, "normalized_query.vcf.gz")
if not (args.cont and os.path.exists(nqpath)):
preprocessVCF(
args.query,
nqpath,
args.location,
not args.inc_nonpass, # pass_only
args.fixchr_query, # chrprefix
args.normalize_query, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref,
)
else:
logging.info("Continuing from %s" % nqpath)
if not (args.cont and os.path.exists(nqpath + ".csi")):
runBcftools("index", nqpath)
logging.info("Intersecting")
tpfn_files = all(
[
os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz")),
]
)
tpfn_r_files = all(
[
os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz")),
]
)
if not (args.cont and tpfn_files):
runBcftools("isec", ntpath, nqpath, "-p", os.path.join(scratch, "tpfn"), "-O", "z")
else:
logging.info("Continuing from %s" % os.path.join(scratch, "tpfn"))
if args.features and not (args.cont and tpfn_r_files):
# only need to do this for getting the feature table
runBcftools("isec", nqpath, ntpath, "-p", os.path.join(scratch, "tpfn_r"), "-O", "z")
logging.info("Getting FPs / Ambi / Unk")
fppath = os.path.join(scratch, "fp.vcf.gz")
unkpath = os.path.join(scratch, "unk.vcf.gz")
ambipath = os.path.join(scratch, "ambi.vcf.gz")
# get header to print to unk and ambi VCFs
rununiquepath = os.path.join(scratch, "tpfn", "0001.vcf.gz")
header = runBcftools("view", rununiquepath, "--header-only")
fp = Tools.BGZipFile(fppath, True)
fp.write(header)
unk = Tools.BGZipFile(unkpath, True)
unk.write(header)
ambi = Tools.BGZipFile(ambipath, True)
ambi.write(header)
ambiClasses = Counter()
ambiReasons = Counter()
fpclasses = BedIntervalTree()
if args.ambi:
# can have multiple ambiguous BED files
for aBED in args.ambi:
# auto-label from first value after chr start end
# new ambi files have the label in position 4
# old ones will look weird here.
fpclasses.addFromBed(aBED, lambda xe: xe[4])
if args.FP:
fpclasses.addFromBed(args.FP, "FP")
has_fp = (fpclasses.count("FP") > 0) or (fpclasses.count("fp") > 0 and args.ambi_fp)
# split VCF into FP, UNK and AMBI
toProcess = gzip.open(rununiquepath, "rb")
for entry in toProcess:
if entry[0] == "#":
continue
fields = entry.strip().split("\t")
chrom = fields[0]
start = int(fields[1])
stop = int(fields[1]) + len(fields[3])
overlap = fpclasses.intersect(chrom, start, stop)
is_fp = False
is_ambi = False
classes_this_pos = set()
for o in overlap:
reason = o.value[0]
if reason == "fp" and args.ambi_fp:
reason = "FP"
elif reason == "fp":
reason = "ambi-fp"
elif reason == "unk":
reason = "ambi-unk"
classes_this_pos.add(reason)
try:
ambiReasons["%s: rep. count %s" % (reason, o.value[1])] += 1
except IndexError:
ambiReasons["%s: rep. count *" % reason] += 1
for x in o.value[3:]:
ambiReasons["%s: %s" % (reason, x)] += 1
if reason == "FP":
is_fp = True
else:
is_ambi = True
for reason in classes_this_pos:
ambiClasses[reason] += 1
if is_fp:
fp.write(entry)
elif is_ambi:
ambi.write(entry)
elif not has_fp:
# when we don't have FP regions, unk stuff becomes FP
fp.write(entry)
else:
unk.write(entry)
toProcess.close()
# since 0001.vcf.gz should already be sorted, we can just convert to bgzipped vcf
# and create index
fp.close()
ambi.close()
unk.close()
runBcftools("index", "--tbi", fppath)
runBcftools("index", "--tbi", unkpath)
runBcftools("index", "--tbi", ambipath)
logging.info("Counting variants...")
truthcounts = parseStats(runBcftools("stats", ntpath), "total.truth")
querycounts = parseStats(runBcftools("stats", nqpath), "total.query")
tpcounts = parseStats(runBcftools("stats", os.path.join(scratch, "tpfn", "0002.vcf.gz")), "tp")
fncounts = parseStats(runBcftools("stats", os.path.join(scratch, "tpfn", "0000.vcf.gz")), "fn")
fpcounts = parseStats(runBcftools("stats", fppath), "fp")
ambicounts = parseStats(runBcftools("stats", ambipath), "ambi")
unkcounts = parseStats(runBcftools("stats", unkpath), "unk")
res = pandas.merge(truthcounts, querycounts, on="type")
res = pandas.merge(res, tpcounts, on="type")
res = pandas.merge(res, fpcounts, on="type")
res = pandas.merge(res, fncounts, on="type")
res = pandas.merge(res, unkcounts, on="type")
res = pandas.merge(res, ambicounts, on="type")
# no explicit guarantee that total.query is equal to unk + ambi + fp + tp
# testSum = res["fp"] + res["tp"] + res["unk"] + res["ambi"]
# filter and relabel
res = res[res["type"] != "samples"]
res = res[res["type"] != "multiallelic SNP sites"]
res = res[res["type"] != "multiallelic sites"]
res.loc[res["type"] == "SNPs", "type"] = "SNVs"
res = res[(res["total.truth"] > 0) | (res["total.query"] > 0)]
# use this to use plain row counts rather than stratified bcftools counts
# truthcounts = countVCFRows(ntpath) # , "total.truth")
# querycounts = countVCFRows(nqpath) # , "total.query")
#
# tpcounts = countVCFRows(os.path.join(scratch, "tpfn", "0002.vcf.gz")) #, "tp")
# fncounts = countVCFRows(os.path.join(scratch, "tpfn", "0000.vcf.gz")) #, "fn")
# fpcounts = countVCFRows(fppath) #, "fp")
# ambicounts = countVCFRows(ambipath) #, "ambi")
# unkcounts = countVCFRows(unkpath) #, "unk")
#
# res = pandas.DataFrame({
# "total.truth" : [ truthcounts ],
# "total.query" : [ querycounts ],
# "tp" : [ tpcounts ],
# "fn" : [ fncounts ],
# "fp" : [ fpcounts ],
# "ambi" : [ ambicounts ],
# "unk" : [ unkcounts ]
# })
#
# res["type"] = "records"
# summary metrics
res["recall"] = res["tp"] / (res["tp"] + res["fn"])
res["recall2"] = res["tp"] / (res["total.truth"])
res["precision"] = res["tp"] / (res["tp"] + res["fp"])
res["na"] = res["unk"] / (res["total.query"])
res["ambiguous"] = res["ambi"] / res["total.query"]
metrics_output = makeMetricsObject("som.py.comparison")
metrics_output["metrics"].append(dataframeToMetricsTable("result", res))
vstring = "som.py-%s" % Tools.version
logging.info("\n" + res.to_string())
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "\n" + res.to_string()
res["sompyversion"] = vstring
vstring = " ".join(sys.argv)
res["sompycmd"] = vstring
if args.ambi and args.explain_ambiguous:
ac = list(ambiClasses.iteritems())
if ac:
ambie = pandas.DataFrame(ac, columns=["class", "count"])
ambie.sort(["class"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info(
"FP/ambiguity classes with info (multiple classes can " "overlap):\n" + ambie.to_string(index=False)
)
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "FP/ambiguity classes with info (multiple classes can " "overlap):\n" + ambie.to_string(
index=False
)
ambie.to_csv(args.output + ".ambiclasses.csv")
metrics_output["metrics"].append(dataframeToMetricsTable("ambiclasses", ambie))
else:
logging.info("No ambiguous variants.")
ar = list(ambiReasons.iteritems())
if ar:
ambie = pandas.DataFrame(ar, columns=["reason", "count"])
ambie.sort(["reason"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info(
"Reasons for defining as ambiguous (multiple reasons can overlap):\n"
+ ambie.to_string(
formatters={"reason": "{{:<{}s}}".format(ambie["reason"].str.len().max()).format}, index=False
)
)
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "Reasons for defining as ambiguous (multiple reasons can overlap):\n" + ambie.to_string(
formatters={"reason": "{{:<{}s}}".format(ambie["reason"].str.len().max()).format}, index=False
)
ambie.to_csv(args.output + ".ambireasons.csv")
metrics_output["metrics"].append(dataframeToMetricsTable("ambireasons", ambie))
else:
logging.info("No ambiguous variants.")
res.to_csv(args.output + ".stats.csv")
with open(args.output + ".metrics.json", "w") as fp:
json.dump(metrics_output, fp)
if args.features:
logging.info("Extracting features...")
fset = Somatic.FeatureSet.make(args.features)
fset.setChrDepths(md)
logging.info("Collecting TP info (1)...")
tps = fset.collect(os.path.join(scratch, "tpfn", "0002.vcf.gz"), "TP")
# TP_r is a hint for fset, they are both TPs
logging.info("Collecting TP info (2)...")
tps2 = fset.collect(os.path.join(scratch, "tpfn_r", "0002.vcf.gz"), "TP_r")
# this is slow because it tries to sort
# ... which we don't need to do since tps1 and tps2 have the same ordering
logging.info("Sorting...")
tps.sort(["CHROM", "POS"], inplace=True)
tps2.sort(["CHROM", "POS"], inplace=True)
tps = tps.reset_index(drop=True)
tps2 = tps2.reset_index(drop=True)
logging.info("Merging TP info...")
columns_tps = list(tps)
columns_tps2 = list(tps2)
len1 = tps.shape[0]
len2 = tps.shape[0]
if len1 != len2:
raise Exception("Cannot read TP features, lists have different lengths : %i != %i" % (len1, len2))
if not args.disable_order_check:
logging.info("Checking order %i / %i" % (len1, len2))
for x in xrange(0, len1):
for a in ["CHROM", "POS"]:
if tps.loc[x][a] != tps2.loc[x][a]:
raise Exception(
"Cannot merge TP features, inputs are out of order at %s / %s"
% (str(tps[x : x + 1]), str(tps2[x : x + 1]))
)
logging.info("Merging...")
cdata = {"CHROM": tps["CHROM"], "POS": tps["POS"], "tag": tps["tag"]}
tpc = pandas.DataFrame(cdata, columns=["CHROM", "POS", "tag"])
all_columns = list(set(columns_tps + columns_tps2))
for a in all_columns:
if a in columns_tps and not a in columns_tps2:
tpc[a] = tps[a]
elif not a in columns_tps and a in columns_tps2:
tpc[a] = tps2[a]
elif a not in ["CHROM", "POS", "tag"]:
tpc[a] = tps2[a]
tpc[a + ".truth"] = tps[a]
logging.info("Collecting FP info...")
fps = fset.collect(fppath, "FP")
ambs = fset.collect(fppath, "AMBI")
unks = fset.collect(fppath, "UNK")
logging.info("Collecting FN info...")
fns = fset.collect(os.path.join(scratch, "tpfn", "0000.vcf.gz"), "FN")
renamed = {}
tp_cols = list(tpc)
for col in list(fns):
if col + ".truth" in tp_cols:
renamed[col] = col + ".truth"
fns.rename(columns=renamed, inplace=True)
featurelist = [tpc, fps, fns, ambs, unks]
if unkpath is not None:
logging.info("Collecting UNK info...")
unk = fset.collect(unkpath, "UNK")
featurelist.append(unk)
logging.info("Making feature table...")
featuretable = pandas.concat(featurelist)
# reorder to make more legible
first_columns = ["CHROM", "POS", "tag"]
all_columns = list(featuretable)
if "REF" in all_columns:
first_columns.append("REF")
if "REF.truth" in all_columns:
first_columns.append("REF.truth")
if "ALT" in all_columns:
first_columns.append("ALT")
if "ALT.truth" in all_columns:
first_columns.append("ALT.truth")
ordered_columns = first_columns + sorted([x for x in all_columns if x not in first_columns])
featuretable = featuretable[ordered_columns]
# make sure positions are integers
featuretable["POS"] = featuretable["POS"].astype(int)
logging.info("Saving feature table...")
featuretable.to_csv(args.output + ".features.csv", float_format="%.8f")
if args.roc is not None:
roc_table = args.roc.from_table(featuretable)
roc_table.to_csv(args.output + ".roc.csv", float_format="%.8f")
finally:
if args.delete_scratch:
shutil.rmtree(scratch)
else:
logging.info("Scratch kept at %s" % scratch)
def main():
args = parse_args()
if args.scratch_prefix:
scratch = os.path.abspath(args.scratch_prefix)
args.delete_scratch = False
Tools.mkdir_p(scratch)
else:
scratch = tempfile.mkdtemp()
logging.info("Scratch path is %s" % scratch)
try:
bams = []
md = None
for x in args.bams:
bams.append(bamStats(x))
if bams:
bres = pandas.concat(bams).groupby("CHROM").mean()
md = {}
for x in bres.index:
logging.info("Mean coverage on %s is %f" %
(x, bres.loc[x]["COVERAGE"]))
md[x] = float(bres.loc[x]["COVERAGE"]) * 3.0
logging.info("Normalizing/reading inputs")
ntpath = os.path.join(scratch, "normalized_truth.vcf.gz")
if not (args.cont and os.path.exists(ntpath)):
preprocessVCF(
args.truth,
ntpath,
args.location,
True, # pass_only
args.fixchr_truth, # chrprefix
args.normalize_truth, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref)
else:
logging.info("Continuing from %s" % ntpath)
if not (args.cont and os.path.exists(ntpath + ".csi")):
runBcftools("index", ntpath)
nqpath = os.path.join(scratch, "normalized_query.vcf.gz")
if not (args.cont and os.path.exists(nqpath)):
preprocessVCF(
args.query,
nqpath,
args.location,
not args.inc_nonpass, # pass_only
args.fixchr_query, # chrprefix
args.normalize_query, # norm,
args.regions_bedfile,
args.targets_bedfile,
args.ref)
else:
logging.info("Continuing from %s" % nqpath)
if not (args.cont and os.path.exists(nqpath + ".csi")):
runBcftools("index", nqpath)
logging.info("Intersecting")
tpfn_files = all([
os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz"))
])
tpfn_r_files = all([
os.path.exists(os.path.join(scratch, "tpfn", "0000.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0001.vcf.gz")),
os.path.exists(os.path.join(scratch, "tpfn", "0002.vcf.gz"))
])
if not (args.cont and tpfn_files):
runBcftools("isec", ntpath, nqpath, "-p",
os.path.join(scratch, "tpfn"), "-O", "z")
else:
logging.info("Continuing from %s" % os.path.join(scratch, "tpfn"))
if args.features and not (args.cont and tpfn_r_files):
# only need to do this for getting the feature table
runBcftools("isec", nqpath, ntpath, "-p",
os.path.join(scratch, "tpfn_r"), "-O", "z")
logging.info("Getting FPs / Ambi / Unk")
fppath = os.path.join(scratch, "fp.vcf.gz")
unkpath = os.path.join(scratch, "unk.vcf.gz")
ambipath = os.path.join(scratch, "ambi.vcf.gz")
# get header to print to unk and ambi VCFs
rununiquepath = os.path.join(scratch, "tpfn", "0001.vcf.gz")
header = runBcftools("view", rununiquepath, "--header-only")
fp = Tools.BGZipFile(fppath, True)
fp.write(header)
unk = Tools.BGZipFile(unkpath, True)
unk.write(header)
ambi = Tools.BGZipFile(ambipath, True)
ambi.write(header)
ambiClasses = Counter()
ambiReasons = Counter()
fpclasses = BedIntervalTree()
if args.ambi:
# can have multiple ambiguous BED files
for aBED in args.ambi:
# auto-label from first value after chr start end
# new ambi files have the label in position 4
# old ones will look weird here.
fpclasses.addFromBed(aBED, lambda xe: xe[4], args.fixchr_truth)
if args.FP:
fpclasses.addFromBed(args.FP, "FP", args.fixchr_truth)
# split VCF into FP, UNK and AMBI
toProcess = gzip.open(rununiquepath, "rb")
for entry in toProcess:
if entry[0] == '#':
continue
fields = entry.strip().split("\t")
chrom = fields[0]
start = int(fields[1])
stop = int(fields[1]) + len(fields[3])
overlap = fpclasses.intersect(chrom, start, stop)
is_fp = False
is_ambi = False
classes_this_pos = set()
for o in overlap:
reason = o.value[0]
if reason == "fp" and args.ambi_fp:
reason = "FP"
elif reason == "fp":
reason = "ambi-fp"
elif reason == "unk":
reason = "ambi-unk"
classes_this_pos.add(reason)
try:
ambiReasons["%s: rep. count %s" %
(reason, o.value[1])] += 1
except IndexError:
ambiReasons["%s: rep. count *" % reason] += 1
for x in o.value[3:]:
ambiReasons["%s: %s" % (reason, x)] += 1
if reason == "FP":
is_fp = True
else:
is_ambi = True
for reason in classes_this_pos:
ambiClasses[reason] += 1
if is_fp:
fp.write(entry)
elif is_ambi:
ambi.write(entry)
elif not args.count_unk:
# when we don't have FP regions, unk stuff becomes FP
fp.write(entry)
else:
unk.write(entry)
toProcess.close()
# since 0001.vcf.gz should already be sorted, we can just convert to bgzipped vcf
# and create index
fp.close()
ambi.close()
unk.close()
runBcftools("index", "--tbi", fppath)
runBcftools("index", "--tbi", unkpath)
runBcftools("index", "--tbi", ambipath)
logging.info("Counting variants...")
truthcounts = parseStats(runBcftools("stats", ntpath), "total.truth")
querycounts = parseStats(runBcftools("stats", nqpath), "total.query")
tpcounts = parseStats(
runBcftools("stats", os.path.join(scratch, "tpfn", "0002.vcf.gz")),
"tp")
fncounts = parseStats(
runBcftools("stats", os.path.join(scratch, "tpfn", "0000.vcf.gz")),
"fn")
fpcounts = parseStats(runBcftools("stats", fppath), "fp")
ambicounts = parseStats(runBcftools("stats", ambipath), "ambi")
unkcounts = parseStats(runBcftools("stats", unkpath), "unk")
res = pandas.merge(truthcounts, querycounts, on="type")
res = pandas.merge(res, tpcounts, on="type")
res = pandas.merge(res, fpcounts, on="type")
res = pandas.merge(res, fncounts, on="type")
res = pandas.merge(res, unkcounts, on="type")
res = pandas.merge(res, ambicounts, on="type")
# no explicit guarantee that total.query is equal to unk + ambi + fp + tp
# testSum = res["fp"] + res["tp"] + res["unk"] + res["ambi"]
# filter and relabel
res = res[res["type"] != "samples"]
res = res[res["type"] != "multiallelic SNP sites"]
res = res[res["type"] != "multiallelic sites"]
res.loc[res["type"] == "SNPs", "type"] = "SNVs"
metrics_output = makeMetricsObject("som.py.comparison")
if args.ambi and args.explain_ambiguous:
ac = list(ambiClasses.iteritems())
if ac:
ambie = pandas.DataFrame(ac, columns=["class", "count"])
ambie.sort_values(["class"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info(
"FP/ambiguity classes with info (multiple classes can "
"overlap):\n" + ambie.to_string(index=False))
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "FP/ambiguity classes with info (multiple classes can " \
"overlap):\n" + ambie.to_string(index=False)
ambie.to_csv(args.output + ".ambiclasses.csv")
metrics_output["metrics"].append(
dataframeToMetricsTable("ambiclasses", ambie))
else:
logging.info("No ambiguous variants.")
ar = list(ambiReasons.iteritems())
if ar:
ambie = pandas.DataFrame(ar, columns=["reason", "count"])
ambie.sort_values(["reason"], inplace=True)
pandas.set_option("display.max_rows", 1000)
pandas.set_option("display.max_columns", 1000)
pandas.set_option("display.width", 1000)
pandas.set_option("display.height", 1100)
logging.info(
"Reasons for defining as ambiguous (multiple reasons can overlap):\n"
+ ambie.to_string(formatters={
'reason':
'{{:<{}s}}'.format(
ambie['reason'].str.len().max()).format
},
index=False))
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "Reasons for defining as ambiguous (multiple reasons can overlap):\n" + ambie.to_string(
formatters={
'reason':
'{{:<{}s}}'.format(
ambie['reason'].str.len().max()).format
},
index=False)
ambie.to_csv(args.output + ".ambireasons.csv")
metrics_output["metrics"].append(
dataframeToMetricsTable("ambireasons", ambie))
else:
logging.info("No ambiguous variants.")
if args.features:
logging.info("Extracting features...")
fset = Somatic.FeatureSet.make(args.features)
fset.setChrDepths(md)
logging.info("Collecting TP info (1)...")
tps = fset.collect(os.path.join(scratch, "tpfn", "0002.vcf.gz"),
"TP")
# TP_r is a hint for fset, they are both TPs
logging.info("Collecting TP info (2)...")
tps2 = fset.collect(os.path.join(scratch, "tpfn_r", "0002.vcf.gz"),
"TP_r")
# this is slow because it tries to sort
# ... which we don't need to do since tps1 and tps2 have the same ordering
logging.info("Sorting...")
tps.sort_values(["CHROM", "POS"], inplace=True)
tps2.sort_values(["CHROM", "POS"], inplace=True)
tps = tps.reset_index(drop=True)
tps2 = tps2.reset_index(drop=True)
logging.info("Merging TP info...")
columns_tps = list(tps)
columns_tps2 = list(tps2)
len1 = tps.shape[0]
len2 = tps2.shape[0]
if len1 != len2:
raise Exception(
"Cannot read TP features, lists have different lengths : %i != %i"
% (len1, len2))
if not args.disable_order_check:
logging.info("Checking order %i / %i" % (len1, len2))
for x in xrange(0, len1):
for a in ["CHROM", "POS"]:
if tps.loc[x][a] != tps2.loc[x][a]:
raise Exception(
"Cannot merge TP features, inputs are out of order at %s / %s"
% (str(tps[x:x + 1]), str(tps2[x:x + 1])))
logging.info("Merging...")
cdata = {
"CHROM": tps["CHROM"],
"POS": tps["POS"],
"tag": tps["tag"]
}
tpc = pandas.DataFrame(cdata, columns=["CHROM", "POS", "tag"])
all_columns = list(set(columns_tps + columns_tps2))
for a in all_columns:
if a in columns_tps and a not in columns_tps2:
tpc[a] = tps[a]
elif a not in columns_tps and a in columns_tps2:
tpc[a] = tps2[a]
elif a not in ["CHROM", "POS", "tag"]:
tpc[a] = tps2[a]
tpc[a + ".truth"] = tps[a]
logging.info("Collecting FP info...")
fps = fset.collect(fppath, "FP")
ambs = fset.collect(ambipath, "AMBI")
logging.info("Collecting FN info...")
fns = fset.collect(os.path.join(scratch, "tpfn", "0000.vcf.gz"),
"FN")
renamed = {}
tp_cols = list(tpc)
for col in list(fns):
if col + ".truth" in tp_cols:
renamed[col] = col + ".truth"
fns.rename(columns=renamed, inplace=True)
featurelist = [tpc, fps, fns, ambs]
if unkpath is not None:
logging.info("Collecting UNK info...")
unk = fset.collect(unkpath, "UNK")
featurelist.append(unk)
logging.info("Making feature table...")
featuretable = pandas.concat(featurelist)
# reorder to make more legible
first_columns = ["CHROM", "POS", "tag"]
# noinspection PyTypeChecker
all_columns = list(featuretable)
if "REF" in all_columns:
first_columns.append("REF")
if "REF.truth" in all_columns:
first_columns.append("REF.truth")
if "ALT" in all_columns:
first_columns.append("ALT")
if "ALT.truth" in all_columns:
first_columns.append("ALT.truth")
ordered_columns = first_columns + sorted(
[x for x in all_columns if x not in first_columns])
featuretable = featuretable[ordered_columns]
# make sure positions are integers
featuretable["POS"] = featuretable["POS"].astype(int)
logging.info("Saving feature table...")
featuretable.to_csv(args.output + ".features.csv",
float_format='%.8f')
if args.roc is not None:
roc_table = args.roc.from_table(featuretable)
roc_table.to_csv(args.output + ".roc.csv", float_format='%.8f')
featuretable["FILTER"].fillna("", inplace=True)
featuretable.ix[featuretable["REF"].str.len() < 1, "absent"] = True
featuretable.ix[featuretable["tag"] == "FN",
"REF"] = featuretable.ix[featuretable["tag"] ==
"FN", "REF.truth"]
featuretable.ix[featuretable["tag"] == "FN",
"ALT"] = featuretable.ix[featuretable["tag"] ==
"FN", "ALT.truth"]
af_t_feature = args.af_strat_truth
af_q_feature = args.af_strat_query
for vtype in ["records", "SNVs", "indels"]:
featuretable["vtype"] = resolve_vtype(args)
featuretable_this_type = featuretable
if args.count_filtered_fn:
res.ix[res["type"] == vtype,
"fp.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "FP")
& (featuretable_this_type["FILTER"] != ""
)].shape[0]
res.ix[res["type"] == vtype,
"tp.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "TP")
& (featuretable_this_type["FILTER"] != ""
)].shape[0]
res.ix[res["type"] == vtype,
"unk.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "UNK")
& (featuretable_this_type["FILTER"] != ""
)].shape[0]
res.ix[res["type"] == vtype,
"ambi.filtered"] = featuretable_this_type[
(featuretable_this_type["tag"] == "AMBI")
& (featuretable_this_type["FILTER"] != ""
)].shape[0]
if args.af_strat:
start = 0.0
end = 1.0
current_binsize = args.af_strat_binsize[0]
next_binsize = 0
while start < 1.0:
# include 1 in last interval
end = start + current_binsize
if end >= 1:
end = 1.00000001
if start >= end:
break
n_tp = featuretable_this_type[
(featuretable_this_type["tag"] == "TP")
& (featuretable_this_type[af_t_feature] >= start) &
(featuretable_this_type[af_t_feature] < end)]
n_fn = featuretable_this_type[
(featuretable_this_type["tag"] == "FN")
& (featuretable_this_type[af_t_feature] >= start) &
(featuretable_this_type[af_t_feature] < end)]
n_fp = featuretable_this_type[
(featuretable_this_type["tag"] == "FP")
& (featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
n_ambi = featuretable_this_type[
(featuretable_this_type["tag"] == "AMBI")
& (featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
n_unk = featuretable_this_type[
(featuretable_this_type["tag"] == "UNK")
& (featuretable_this_type[af_q_feature] >= start) &
(featuretable_this_type[af_q_feature] < end)]
r = {
"type":
"%s.%f-%f" % (vtype, start, end),
"total.truth":
n_tp.shape[0] + n_fn.shape[0],
"total.query":
n_tp.shape[0] + n_fp.shape[0] + n_ambi.shape[0] +
n_unk.shape[0],
"tp":
n_tp.shape[0],
"fp":
n_fp.shape[0],
"fn":
n_fn.shape[0],
"unk":
n_unk.shape[0],
"ambi":
n_ambi.shape[0]
}
if args.count_filtered_fn:
r["fp.filtered"] = n_fp[
n_fp["FILTER"] != ""].shape[0]
r["tp.filtered"] = n_tp[
n_tp["FILTER"] != ""].shape[0]
r["unk.filtered"] = n_unk[
n_unk["FILTER"] != ""].shape[0]
r["ambi.filtered"] = n_ambi[
n_ambi["FILTER"] != ""].shape[0]
res = pandas.concat([res, pandas.DataFrame([r])])
if args.roc is not None and (n_tp.shape[0] +
n_fn.shape[0] +
n_fp.shape[0]) > 0:
roc_table_strat = args.roc.from_table(
pandas.concat([n_tp, n_fp, n_fn]))
rtname = "%s.%s.%f-%f.roc.csv" % (
args.output, vtype, start, end)
roc_table_strat.to_csv(rtname, float_format='%.8f')
start = end
next_binsize += 1
if next_binsize >= len(args.af_strat_binsize):
next_binsize = 0
current_binsize = args.af_strat_binsize[next_binsize]
if not args.af_strat:
res = res[(res["total.truth"] > 0)]
# summary metrics with confidence intervals
ci_alpha = 1.0 - args.ci_level
recall = binomialCI(res["tp"], res["tp"] + res["fn"], ci_alpha)
precision = binomialCI(res["tp"], res["tp"] + res["fp"], ci_alpha)
res["recall"], res["recall_lower"], res["recall_upper"] = recall
res["recall2"] = res["tp"] / (res["total.truth"])
res["precision"], res["precision_lower"], res[
"precision_upper"] = precision
res["na"] = res["unk"] / (res["total.query"])
res["ambiguous"] = res["ambi"] / res["total.query"]
any_fp = fpclasses.countbases(label="FP")
fp_region_count = 0
auto_size = True
if args.fpr_size:
try:
fp_region_count = int(args.fpr_size)
auto_size = False
except:
pass
if auto_size:
if any_fp:
if args.location:
chrom, _, rest = args.location.partition(":")
if rest:
start, _, end = rest.partition("_")
if start:
start = int(start)
if end:
end = int(end)
else:
fp_region_count += fpclasses.countbases(chrom,
label="FP")
else:
fp_region_count = any_fp
else:
cs = fastaContigLengths(args.ref)
if args.location:
fp_region_count = calculateLength(cs, args.location)
else:
# use all locations we saw calls on
h1 = Tools.vcfextract.extractHeadersJSON(ntpath)
h1_chrs = h1["tabix"]["chromosomes"]
if not h1_chrs:
logging.warn("No contigs in truth file")
h1_chrs = []
if len(h1_chrs) > 0:
qlocations = " ".join(h1_chrs)
fp_region_count = calculateLength(cs, qlocations)
else:
fp_region_count = 0
res["fp.region.size"] = fp_region_count
res["fp.rate"] = 1e6 * res["fp"] / res["fp.region.size"]
if args.count_filtered_fn:
res["recall.filtered"] = (res["tp"] - res["tp.filtered"]) / (
res["tp"] + res["fn"])
res["precision.filtered"] = (res["tp"] - res["tp.filtered"]) / (
res["tp"] - res["tp.filtered"] + res["fp"] -
res["fp.filtered"])
res["fp.rate.filtered"] = 1e6 * (
res["fp"] - res["fp.filtered"]) / res["fp.region.size"]
res["na.filtered"] = (res["unk"] -
res["unk.filtered"]) / (res["total.query"])
res["ambiguous.filtered"] = (
res["ambi"] - res["ambi.filtered"]) / res["total.query"]
# HAP-162 remove inf values
res.replace([np.inf, -np.inf], 0)
metrics_output["metrics"].append(dataframeToMetricsTable(
"result", res))
vstring = "som.py-%s" % Tools.version
logging.info("\n" + res.to_string())
# in default mode, print result summary to stdout
if not args.quiet and not args.verbose:
print "\n" + res.to_string()
res["sompyversion"] = vstring
vstring = " ".join(sys.argv)
res["sompycmd"] = vstring
# save results
res.to_csv(args.output + ".stats.csv")
with open(args.output + ".metrics.json", "w") as fp:
json.dump(metrics_output, fp)
if args.happy_stats:
# parse saved feature table as the one in memory has been updated
featuretable = pandas.read_csv(args.output + ".features.csv",
low_memory=False,
dtype={"FILTER": str})
# hap.py summary.csv
summary = summary_from_featuretable(featuretable, args)
summary.to_csv(args.output + ".summary.csv")
# hap.py extended.csv
if args.af_strat:
extended = extended_from_featuretable(featuretable, args)
extended.to_csv(args.output + ".extended.csv",
index=False,
na_rep="NA")
finally:
if args.delete_scratch:
shutil.rmtree(scratch)
else:
logging.info("Scratch kept at %s" % scratch)