Esempio n. 1
0
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)
Esempio n. 2
0
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)
Esempio n. 3
0
File: som.py Progetto: jaredo/hap.py
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)
Esempio n. 4
0
def parse_args():
    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("--happy-stats",
                        dest="happy_stats",
                        default=False,
                        action="store_true",
                        help="Generate summary.csv.")

    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.happy_stats and (not args.inc_nonpass or not args.features):
        raise Exception(
            "Obtaining hap.py stats 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()

    return args