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varsim_validator.py
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#!/usr/bin/env python
import argparse
import os
import sys
import subprocess
import logging
import time
import itertools
import glob
import re
from distutils.version import LooseVersion
from liftover_restricted_vcf_map import lift_vcfs, lift_maps
from generate_small_test_ref import gen_restricted_ref_and_vcfs
from varsim import varsim_main, get_version, check_java, get_loglevel, makedirs, RandVCFOptions, RandDGVOptions
import json
import utils
from copy import deepcopy
MY_DIR = os.path.dirname(os.path.realpath(__file__))
VARSIMJAR = os.path.realpath(os.path.join(MY_DIR, "VarSim.jar"))
ALL_COUNTS = ["fp", "tp", "fn", "tn", "t"]
def safe_mean(counts):
return (float(sum(counts)) / len(counts)) if counts else 0.0
def sum_counts(count1, count2={}, keys_for_summation=["fp", "fn", "tp", "t"]):
for key in keys_for_summation:
if key not in count1:
count1[key] = 0
for key in keys_for_summation:
if key in count2:
count1[key] += count2[key]
return count1
ENABLED_DICT = {"all": ["SNP", "Insertion", "Deletion"], "snp": ["SNP"], "ins": ["Insertion"], "del": ["Deletion"], "indel": ["Insertion", "Deletion"]}
def aggregate_reports(sample_reports, samples, variant_type="all"):
logger = logging.getLogger(aggregate_reports.__name__)
if not samples:
samples = sample_reports.keys()
enabled = ENABLED_DICT[variant_type]
summary_report = sum_counts({}, {}, ALL_COUNTS)
for sample in samples:
sample_s = sample_reports[sample]["num_true_correct"]["data"]
for v in enabled:
if v not in sample_s:
logger.error("{} missing for {}".format(v, sample))
continue
summary_report = sum_counts(summary_report, sample_s[v]["sum_count"])
if variant_type != "all":
summary_report = sum_counts(summary_report, sample_s[v]["sum_per_base_count"], keys_for_summation=["tn"])
if variant_type == "all":
summary_report = sum_counts(summary_report, sample_reports[sample]["num_true_correct"]["all_data"]["sum_per_base_count"], keys_for_summation=["tn"])
summary_report["fn"] = summary_report["t"] - summary_report["tp"]
summary_report["fdr"] = (float(summary_report["fp"]) / float(summary_report["fp"] + summary_report["tp"]) * 100) if (summary_report["fp"] + summary_report["tp"] > 0) else 0
summary_report["tpr"] = (float(summary_report["tp"]) / float(summary_report["t"]) * 100) if summary_report["t"] > 0 else 0
summary_report["ppv"] = 100.0 - summary_report["fdr"]
summary_report["f1"] = (2.0 * float(summary_report["ppv"] * summary_report["tpr"]) / float(summary_report["ppv"] + summary_report["tpr"])) if (summary_report["ppv"] + summary_report["tpr"] > 0) else 0
summary_report["spc"] = 100.0 * float(summary_report["tn"]) / float(summary_report["tn"] + summary_report["fp"]) if (summary_report["tn"] + summary_report["fp"] > 0) else 0
return summary_report
def get_quantile(values, quantile=50.0):
logger = logging.getLogger(get_quantile.__name__)
sorted_values = sorted(values)
return sorted_values[int(len(values)*(1 - quantile/100.0))] if sorted_values else 0.0
def varsim_multi_validation(regions, samples, varsim_dirs, variants_dirs, out_dir, vcfcompare_options="", disable_vcfcompare=False):
logger = logging.getLogger(varsim_multi_validation.__name__)
bed_options = "-bed {}".format(regions) if regions else ""
vcfcompare_options += " " + bed_options
sample_reports = {}
samples_found = {}
for sample in samples:
truth = filter(os.path.isfile, map(lambda d: os.path.join(d, sample, "out", "lifted", "truth.vcf"), varsim_dirs))
called = filter(os.path.isfile, map(lambda d: os.path.join(d, sample, "{}.vcf".format(sample)), variants_dirs))
if truth and called:
samples_found[sample] = {"truth": truth[0], "called": called[0]}
for sample in sorted(samples_found.keys()):
sample_dir = os.path.join(out_dir, sample)
makedirs([sample_dir])
sample_truth = samples_found[sample]["truth"]
sample_called = samples_found[sample]["called"]
if not os.path.isfile(sample_called):
logger.error("{} missing".format(sample_called))
continue
if not disable_vcfcompare:
command = "java {} -jar {} vcfcompare {} -true_vcf {} -prefix {} {}".format(utils.JAVA_XMX, VARSIMJAR, vcfcompare_options, sample_truth, os.path.join(sample_dir, sample), sample_called)
with open(os.path.join(sample_dir, "vcfcompare.out"), "w") as stdout, open(os.path.join(sample_dir, "vcfcompare.err"), "w") as stderr:
subprocess.check_call(command, shell=True, stdout=stdout, stderr=stderr)
report_json = os.path.join(sample_dir, "{}_report.json".format(sample))
if not os.path.isfile(report_json):
logger.error("Accuracy report {} missing for {}".format(report_json, sample))
continue
with open(report_json) as report_json_fd:
sample_reports[sample] = json.load(report_json_fd)
logger.info("Generated accuracy report for {}".format(sample))
# Now generate summary stats
final_report = {}
for v in ["all", "snp", "ins", "del", "indel"]:
final_report[v] = aggregate_reports(sample_reports, None, v)
final_report["samples"] = {}
for sample in sample_reports:
sample_summary = {"report": {}, "truth": samples_found[sample]["truth"], "called": samples_found[sample]["called"]}
for key in ["all", "snp", "ins", "del", "indel"]:
sample_summary["report"][key] = aggregate_reports(sample_reports, [sample], key)
final_report["samples"][sample] = sample_summary
per_sample_accuracies = {}
samples = sorted(sample_reports.keys())
per_sample_accuracies["samples"] = samples
#logger.info(json.dumps(final_report["samples"], indent=2))
for key in ["all", "snp", "ins", "del", "indel"]:
key_metric = {}
for metric in ["tpr", "spc", "ppv", "t", "fp", "fn", "tp", "tn"]:
values = [final_report["samples"][sample]["report"][key][metric] for sample in samples]
key_metric[metric] = {"data": values, "mean": safe_mean(values), "median": get_quantile(values), "ci95": get_quantile(values, 95)}
per_sample_accuracies[key] = key_metric
final_report["per_sample"] = per_sample_accuracies
final_report["num_samples"] = len(final_report["samples"])
print json.dumps(final_report, indent=2)
if __name__ == "__main__":
check_java()
parser = argparse.ArgumentParser(description="VarSim: A high-fidelity simulation validation framework",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--out_dir", metavar="DIR",
help="Output directory for the simulated genome, reads and variants", required=False,
default="out")
parser.add_argument("--reference", metavar="FASTA", help="Reference genome that variants will be inserted into", default="")
parser.add_argument("--regions", help="Regions of interest for simulation. Skip for whole genome simulation")
parser.add_argument("--samples", help="Samples to be simulated", required=True, nargs="+")
parser.add_argument("--varsim", help="Root directory of multi-sample truth generation", nargs="+", required=True)
parser.add_argument("--variants", help="Root directory of variant calls", required=True, nargs="+")
parser.add_argument("--vcfcompare_options", help="Other VCFCompare options", default="")
parser.add_argument("--disable_vcfcompare", action="store_true", help="Do not run VCFcompare if already ran")
parser.add_argument("--java_max_mem", metavar="XMX", help="max java memory", default="10g", type = str)
parser.add_argument('--version', action='version', version=get_version())
parser.add_argument("--loglevel", help="Set logging level", choices=["debug", "warn", "info"], default="info")
args = parser.parse_args()
utils.JAVA_XMX = utils.JAVA_XMX + args.java_max_mem
makedirs([args.out_dir])
# Setup logging
FORMAT = '%(levelname)s %(asctime)-15s %(name)-20s %(message)s'
loglevel = get_loglevel(args.loglevel)
logging.basicConfig(level=loglevel, format=FORMAT)
args.vcfcompare_options = "-reference {} {}".format(args.reference, args.vcfcompare_options) if args.reference else args.vcfcompare_options
varsim_multi_validation(args.regions, args.samples, args.varsim, args.variants, args.out_dir, args.vcfcompare_options, args.disable_vcfcompare)