def plot_heatmap(tsv, options):
""" make a heatmap """
out_dir = os.path.join(options.comp_dir, "heatmaps")
robust_makedirs(out_dir)
mat, col_names, row_names, row_label = read_tsv(tsv)
names = name_map()
for i in range(len(col_names)):
if col_names[i] in names:
col_names[i] = names[col_names[i]]
for i in range(len(row_names)):
if row_names[i] in names:
row_names[i] = names[row_names[i]]
if "_rename" in tsv:
return
fix_tsv = tsv.replace(".tsv", "_rename.tsv")
write_tsv(fix_tsv, mat, col_names, row_names, row_label)
out_hm = os.path.join(out_dir, os.path.basename(tsv).replace(".tsv", ".pdf"))
ph_opts = "--skip {}".format(options.skip) if options.skip is not None else ""
cmd = "scripts/plotHeatmap.py {} {} {}".format(fix_tsv, out_hm, ph_opts)
print cmd
os.system(cmd)
cmd = "scripts/plotHeatmap.py {} {} {} --log_scale".format(fix_tsv, out_hm.replace(".pdf", "_log.pdf"), ph_opts)
print cmd
os.system(cmd)
def temp_path(options, prefix="tmp", ext="", length=6):
""" get a temporary file in out_dir/temp
"""
tempdir = os.path.join(options.out_dir, "temp")
robust_makedirs(tempdir)
tag = "".join([random.choice(string.ascii_uppercase + string.digits) for i in xrange(length)])
return os.path.join(tempdir, prefix + tag + ext)
def plot_kmer_comp(tsv_path, options):
""" take a kmer compare table and make a
jaccard boxplot for the first column and a
recall / precision ploot for the 2nd and third column
"""
out_dir = os.path.join(options.comp_dir, "comp_plots")
robust_makedirs(out_dir)
out_name = os.path.basename(os.path.splitext(tsv_path)[0])
out_base_path = os.path.join(out_dir, out_name)
region = out_name.split("-")[-1].upper()
params = " ".join(PLOT_PARAMS)
# jaccard boxplot
jac_tsv = out_base_path + "_jac.tsv"
awkstr = '''awk '{if (NR!=1) print $1 "\t" $2}' '''
run("{} {} > {}".format(awkstr, tsv_path, jac_tsv))
jac_png = out_base_path + "_jac.png"
run("scripts/boxplot.py {} --save {} --title \"{} KMER Set Jaccard\" --x_label \"Graph\" --y_label \"Jaccard Index\" --x_sideways {}".format(jac_tsv, jac_png, region, params))
# precision recall scatter plot
acc_tsv = out_base_path + "_acc.tsv"
awkstr = '''awk '{if (NR!=1) print $1 "\t" $4 "\t" $3}' '''
run("{} {} > {}".format(awkstr, tsv_path, acc_tsv))
acc_png = out_base_path + "_acc.png"
run("scripts/scatter.py {} --save {} --title \"{} KMER Set Accuracy\" --x_label \"Recall\" --y_label \"Precision\" --width 12 --height 9 --lines {}".format(acc_tsv, acc_png, region, params))
def plot_heatmap(tsv, options):
""" make a heatmap """
out_dir = os.path.join(options.comp_dir, "heatmaps")
robust_makedirs(out_dir)
mat, col_names, row_names, row_label = read_tsv(tsv)
names = name_map()
for i in range(len(col_names)):
if col_names[i] in names:
col_names[i] = names[col_names[i]]
for i in range(len(row_names)):
if row_names[i] in names:
row_names[i] = names[row_names[i]]
if "_rename" in tsv:
return
fix_tsv = tsv.replace(".tsv", "_rename.tsv")
write_tsv(fix_tsv, mat, col_names, row_names, row_label)
out_hm = os.path.join(out_dir, os.path.basename(tsv).replace(".tsv", ".pdf"))
ph_opts = "--skip {}".format(options.skip) if options.skip is not None else ""
cmd = "scripts/plotHeatmap.py {} {} {}".format(fix_tsv, out_hm, ph_opts)
print cmd
os.system(cmd)
cmd = "scripts/plotHeatmap.py {} {} {} --log_scale".format(fix_tsv, out_hm.replace(".pdf", "_log.pdf"), ph_opts)
print cmd
os.system(cmd)
def make_best_calls(best_table, options):
""" using softlinks, make a call set with best f1s from the roc. this is dependent on the
call directories being obtainable from the comparison directory by dropping extension """
best_dir = options.out_dir.strip("/") + ".best"
for region in best_table.keys():
for graph in best_table[region].keys():
comp_tsv_path = best_table[region][graph][0]
comp_tsv_path = comp_tsv_path[:comp_tsv_path.find("/comp_tables")]
call_base_path = os.path.splitext(comp_tsv_path)[0]
call_path = os.path.join(call_base_path, region, graph)
# gatk3 and platypus: we just link in their vcf since they don't have call directory
if graph in ["gatk3", "platypus"]:
robust_makedirs(os.path.join(best_dir, region, graph))
else:
robust_makedirs(os.path.join(best_dir, region))
os.system("ln -fs {} {}".format(os.path.abspath(call_path),
os.path.abspath(os.path.join(best_dir, region))))
# link in the preprocessed vcf from the comp dir to the same directory
comp_path = os.path.join(call_base_path +".comp")
for pvcf in glob.glob(os.path.join(comp_path, "preprocessed_vcfs", region, "*_{}.vcf".format(graph))):
os.system("ln -fs {} {}".format(os.path.abspath(pvcf),
os.path.abspath(os.path.join(best_dir, region, graph, os.path.basename(pvcf).replace(graph, "sample_preprocessed")))))
# link in the truth while we're at it
for pvcf in glob.glob(os.path.join(comp_path, "preprocessed_vcfs", region, "*_platvcf*.vcf")):
os.system("ln -fs {} {}".format(os.path.abspath(pvcf),
os.path.abspath(os.path.join(best_dir, region, graph))))
def main(args):
options = parse_args(args)
RealTimeLogger.start_master()
for gam in options.in_gams:
if len(gam.split("/")) < 3 or os.path.splitext(gam)[1] != ".gam":
raise RuntimeError("Input gam paths must be of the form "
".../<alg>/<reads>/<filename>.gam")
robust_makedirs(json_out_path(options))
robust_makedirs(compare_out_path(options))
# Make a root job
root_job = Job.wrapJobFn(compute_all_indexes, options,
cores=1, memory="2G", disk=0)
# Run it and see how many jobs fail
if not options.only_summary:
failed_jobs = Job.Runner.startToil(root_job, options)
else:
failed_jobs = 0
if failed_jobs > 0:
raise Exception("{} jobs failed!".format(failed_jobs))
RealTimeLogger.stop_master()
# make some tables from the json comparison output
#dist_table(options)
#acc_table(options)
snp_count_table(options)
graph_size_table(options)
def plot_kmer_comp(tsv_path, options):
""" take a kmer compare table and make a
jaccard boxplot for the first column and a
recall / precision ploot for the 2nd and third column
"""
out_dir = os.path.join(options.comp_dir, "comp_plots")
robust_makedirs(out_dir)
out_name = os.path.basename(os.path.splitext(tsv_path)[0])
out_base_path = os.path.join(out_dir, out_name)
sample = out_name.split("-")[-1].upper()
region = out_name.split("-")[-2].upper()
params = " ".join(PLOT_PARAMS)
# jaccard boxplot
jac_tsv = out_base_path + "_jac.tsv"
awkstr = '''awk '{if (NR!=1) print $1 "\t" $2}' '''
run("{} {} > {}".format(awkstr, tsv_path, jac_tsv))
jac_png = out_base_path + "_jac.png"
run("scripts/boxplot.py {} --save {} --title \"{} KMER Set Jaccard\" --x_label \"Graph\" --y_label \"Jaccard Index\" --x_sideways {}".format(jac_tsv, jac_png, region, params))
# precision recall scatter plot
acc_tsv = out_base_path + "_acc.tsv"
awkstr = '''awk '{if (NR!=1) print $1 "\t" $4 "\t" $3}' '''
run("{} {} > {}".format(awkstr, tsv_path, acc_tsv))
acc_png = out_base_path + "_acc.png"
run("scripts/scatter.py {} --save {} --title \"{} KMER Set Accuracy\" --x_label \"Recall\" --y_label \"Precision\" --width 12 --height 9 --lines {}".format(acc_tsv, acc_png, region, params))
def temp_path(options, prefix="tmp", ext="", length=6):
""" get a temporary file in out_dir/temp
"""
tempdir = os.path.join(options.out_dir, "temp")
robust_makedirs(tempdir)
tag = "".join([random.choice(
string.ascii_uppercase + string.digits) for i in xrange(length)])
return os.path.join(tempdir, prefix + tag + ext)
def out_base_path(tag, label, extension):
bd = tag if extension != ".tsv" else "tsv"
ret = (
os.path.join(out_dir, bd, "-".join(out_name.split("-")[:-1]) + "-{}-{}-".format(sample, tag) + region)
+ "_"
+ label
+ extension
)
robust_makedirs(os.path.dirname(ret))
return ret
def main(args):
options = parse_args(args)
RealTimeLogger.start_master()
robust_makedirs(options.out_dir)
vcfmap = munge_vcf_results(options.comp_dir)
mergetable = make_trio_vcfs(vcfmap, options)
do_mendel(mergetable, options)
def main(args):
options = parse_args(args)
RealTimeLogger.start_master()
robust_makedirs(options.out_dir)
vcfmap = munge_vcf_results(options.comp_dir)
mergetable = make_trio_vcfs(vcfmap, options)
do_mendel(mergetable, options)
def main(args):
options = parse_args(args)
robust_makedirs(options.out_dir)
evalmap = munge_vcfeval_results(options.comp_dir)
counts_table = do_all_counts(evalmap, options)
for region, rd in counts_table.items():
for sample, graph_table in rd.items():
tsv = counts_tsv(graph_table, options)
with open(os.path.join(options.out_dir, "call_stats_{}_{}.tsv".format(region, sample)), "w") as f:
f.write(tsv)
def make_trio_vcfs(vcfmap, options):
""" merge up samples into same vcf using rtg return index of merged files"""
robust_makedirs(options.out_dir)
ped_file = os.path.join(options.out_dir, "predigree.ped")
with open(ped_file, "w") as f:
f.write(options.ped + "\n")
mergetable = dict()
for region, rd in vcfmap.items():
mergetable[region] = dict()
region_dir = os.path.join(options.out_dir, "trio_vcfs", region)
robust_makedirs(region_dir)
# round up all sampels for graph
bygraph = dict()
for sample, sd in rd.items():
for graph, pvcf in sd.items():
if graph not in bygraph:
bygraph[graph] = dict()
bygraph[graph][sample] = pvcf
# make a merged vcf for each graph
for graph, sd in bygraph.items():
input_vcfs = {"snp": [], "indel": [], "all": []}
for sample, pvcf in sd.items():
work_dir = os.path.join(region_dir, "input_vcf")
merge_dir = os.path.join(region_dir, "merged_vcf")
robust_makedirs(work_dir)
robust_makedirs(merge_dir)
for kind in input_vcfs.keys():
filter_vcf = os.path.join(
work_dir, "{}_{}_{}.vcf".format(graph, sample, kind))
vstr = "-v snps,mnps" if kind is "snp" else "-V snps,mnps" if kind is "indel" else ""
if options.clip is not None:
vstr += " -R {}".format(options.clip)
run("bcftools view {} -f PASS,. {} | bcftools norm - -f {} > {}"
.format(pvcf, vstr, options.chrom_fa_path, filter_vcf))
run("bgzip -f {}".format(filter_vcf))
run("tabix -f -p vcf {}.gz".format(filter_vcf))
input_vcfs[kind].append("{}.gz".format(filter_vcf))
if len(sd.items()) >= 3 and \
len(input_vcfs["all"]) == len(sd.items()) and\
len(input_vcfs["snp"]) == len(sd.items()) and\
len(input_vcfs["indel"]) == len(sd.items()):
mergetable[region][graph] = dict()
for kind in input_vcfs.keys():
out_vcf = os.path.join(
merge_dir, "{}_{}_merged.vcf.gz".format(graph, kind))
run("rm -f {}".format(out_vcf))
run("rtg vcfmerge {} -o {}".format(
" ".join(input_vcfs[kind]), out_vcf),
fail_hard=True)
mergetable[region][graph][kind] = out_vcf
return mergetable
def compute_kmer_comparison(job, graph1, graph2, options):
""" run vg compare between two graphs
"""
out_path = comp_path(graph1, graph2, options)
graph1_index_path = index_path(graph1, options)
assert os.path.exists(graph1_index_path)
graph2_index_path = index_path(graph2, options)
assert os.path.exists(graph2_index_path)
do_comp = options.overwrite or not os.path.exists(out_path)
if do_comp:
robust_makedirs(os.path.dirname(out_path))
os.system("vg compare {} {} -t {} > {}".format(graph1, graph2,
min(options.vg_cores, 2), out_path))
def make_trio_vcfs(vcfmap, options):
""" merge up samples into same vcf using rtg return index of merged files"""
robust_makedirs(options.out_dir)
ped_file = os.path.join(options.out_dir, "predigree.ped")
with open(ped_file, "w") as f:
f.write(options.ped + "\n")
mergetable = dict()
for region, rd in vcfmap.items():
mergetable[region] = dict()
region_dir = os.path.join(options.out_dir, "trio_vcfs", region)
robust_makedirs(region_dir)
# round up all sampels for graph
bygraph = dict()
for sample, sd in rd.items():
for graph, pvcf in sd.items():
if graph not in bygraph:
bygraph[graph] = dict()
bygraph[graph][sample] = pvcf
# make a merged vcf for each graph
for graph, sd in bygraph.items():
input_vcfs = { "snp" : [], "indel" : [], "all" : [] }
for sample, pvcf in sd.items():
work_dir = os.path.join(region_dir, "input_vcf")
merge_dir = os.path.join(region_dir, "merged_vcf")
robust_makedirs(work_dir)
robust_makedirs(merge_dir)
for kind in input_vcfs.keys():
filter_vcf = os.path.join(work_dir, "{}_{}_{}.vcf".format(graph, sample, kind))
vstr = "-v snps,mnps" if kind is "snp" else "-V snps,mnps" if kind is "indel" else ""
if options.clip is not None:
vstr += " -R {}".format(options.clip)
run("bcftools view {} -f PASS,. {} | bcftools norm - -f {} > {}".format(
pvcf, vstr, options.chrom_fa_path, filter_vcf))
run("bgzip -f {}".format(filter_vcf))
run("tabix -f -p vcf {}.gz".format(filter_vcf))
input_vcfs[kind].append("{}.gz".format(filter_vcf))
if len(sd.items()) >= 3 and \
len(input_vcfs["all"]) == len(sd.items()) and\
len(input_vcfs["snp"]) == len(sd.items()) and\
len(input_vcfs["indel"]) == len(sd.items()):
mergetable[region][graph] = dict()
for kind in input_vcfs.keys():
out_vcf = os.path.join(merge_dir, "{}_{}_merged.vcf.gz".format(graph, kind))
run("rm -f {}".format(out_vcf))
run("rtg vcfmerge {} -o {}".format(" ".join(input_vcfs[kind]), out_vcf), fail_hard = True)
mergetable[region][graph][kind] = out_vcf
return mergetable
def main(args):
options = parse_args(args)
robust_makedirs(options.out_dir)
evalmap = munge_vcfeval_results(options.comp_dir)
counts_table = do_all_counts(evalmap, options)
for region, rd in counts_table.items():
for sample, graph_table in rd.items():
tsv = counts_tsv(graph_table, options)
with open(
os.path.join(options.out_dir,
"call_stats_{}_{}.tsv".format(region,
sample)),
"w") as f:
f.write(tsv)
def do_mendel(mergetable, options):
""" run rtg mendelian on all our merged vcfs """
header = ["graph", "all", "snp", "indel"]
for region, gd in mergetable.items():
table = []
for graph, mergefiles in gd.items():
annot_dir = os.path.join(options.out_dir, "mendel", region, graph)
robust_makedirs(annot_dir)
concordance = dict()
for kind, mergefile in mergefiles.items():
out_vcf = os.path.join(annot_dir,
"mendel_{}.vcf.gz".format(kind))
con_vcf = os.path.join(annot_dir,
"consistent_{}.vcf.gz".format(kind))
incon_vcf = os.path.join(annot_dir,
"inconsistent_{}.vcf.gz".format(kind))
out_stdout = os.path.join(annot_dir,
"mendel_{}.stdout".format(kind))
run("rtg mendelian -l -i {} -t {} --pedigree {} --output {} --output-consistent {} --output-inconsistent {} > {}"
.format(mergefile,
os.path.join(options.comp_dir, "chrom.sdf"),
os.path.join(options.out_dir, "predigree.ped"),
out_vcf, con_vcf, incon_vcf, out_stdout))
concordance[kind] = scrape_mendel(out_stdout)
table.append([
graph, concordance["all"], concordance["snp"],
concordance["indel"]
])
# write the tsv for this region
with open(
os.path.join(options.out_dir, "mendel-{}.tsv".format(region)),
"w") as f:
f.write("\t".join(header) + "\n")
for row in table:
if None not in row:
line = [str(s) for s in row]
f.write("\t".join(line) + "\n")
def compute_linear_variants(job, input_gam, options):
""" project to bam, then run samtools to call some variants
"""
input_graph_path = graph_path(input_gam, options)
input_index_path = index_path(input_graph_path, options)
# can only do this if there is a "ref" path in the vg graph
res_path = temp_path(options)
run("scripts/vgHasPath.sh {} {} > {}".format(input_graph_path, "ref", res_path))
has_ref = False
with open(res_path) as res_file:
has_ref = res_file.read()[0] == "1"
run("rm {}".format(res_path))
if has_ref:
surject_path = projected_bam_path(input_gam, options)
out_vcf_path = linear_vcf_path(input_gam, options)
out_vg_path = linear_vg_path(input_gam, options)
fasta_path = ref_path(input_gam, options)
do_surject = options.overwrite or not os.path.isfile(surject_path)
do_vcf = do_surject or not os.path.isfile(out_vcf_path + ".gz")
do_vg = do_vcf or not os.path.isfile(out_vg_path)
if do_surject:
robust_makedirs(os.path.dirname(surject_path))
prefix_path = temp_path(options, ".prefix")
# surject to reference path (name hardcoded to ref for now)
run("vg surject -d {} -p {} -b {} -t {} | samtools sort -o - {}> {}".format(
input_index_path,
"ref",
input_gam,
options.vg_cores,
prefix_path,
surject_path),
timeout_sec=options.timeout,
timeout_dep=surject_path)
run("rm -f {}".format(prefix_path))
if do_vcf:
# todo: we assume that all graphs have same reference fasta, here.
# this is false for, ex, simons which uses grchg37 instead of 38.
# create pileup in bcf using samtools
# http://samtools.sourceforge.net/mpileup.shtml
assert os.path.isfile(fasta_path)
robust_makedirs(os.path.dirname(out_vcf_path))
run("samtools mpileup -I -u -t DP -f {} {} | bcftools call -m -V indels - > {}".format(
fasta_path,
surject_path,
out_vcf_path))
# make compressed index
run("bgzip -f {}".format(out_vcf_path))
run("tabix -f -p vcf {}.gz".format(out_vcf_path))
if do_vg:
# and convert back to vg...
robust_makedirs(os.path.dirname(out_vg_path))
run("vg construct -v {}.gz -r {} -t {} > {}".format(out_vcf_path, fasta_path,
options.vg_cores, out_vg_path))
def compute_vg_variants(job, input_gam, options):
""" run vg pileup and vg call on the input
"""
input_graph_path = graph_path(input_gam, options)
out_pileup_path = pileup_path(input_gam, options)
out_sample_vg_path = sample_vg_path(input_gam, options)
out_augmented_vg_path = augmented_vg_path(input_gam, options)
do_pu = options.overwrite or not os.path.isfile(out_pileup_path)
do_call = do_pu or not os.path.isfile(out_sample_vg_path)
do_aug = do_pu or not os.path.isfile(out_augmented_vg_path)
if do_pu:
RealTimeLogger.get().info("Computing Variants for {} {}".format(
input_graph_path,
input_gam))
robust_makedirs(os.path.dirname(out_pileup_path))
run("vg pileup {} {} -t {} > {}".format(input_graph_path,
input_gam,
options.vg_cores,
out_pileup_path))
if do_call:
robust_makedirs(os.path.dirname(out_sample_vg_path))
run("vg call {} {} -r 0.001 -d 50 -e 150 -s 25 -t {} | vg ids -c - | vg ids -s - > {}".format(input_graph_path,
out_pileup_path,
options.vg_cores,
out_sample_vg_path))
if do_aug:
robust_makedirs(os.path.dirname(out_augmented_vg_path))
run("vg call {} {} -r 0.001 -d 50 -e 150 -s 25 -t {} -l | vg ids -c - | vg ids -s - > {}".format(input_graph_path,
out_pileup_path,
options.vg_cores,
out_augmented_vg_path))
def make_best_calls(best_table, options):
""" using softlinks, make a call set with best f1s from the roc. this is dependent on the
call directories being obtainable from the comparison directory by dropping extension """
best_dir = options.out_dir.strip("/") + ".best"
for region in best_table.keys():
for graph in best_table[region].keys():
comp_tsv_path = best_table[region][graph][0]
comp_tsv_path = comp_tsv_path[:comp_tsv_path.find("/comp_tables")]
call_base_path = os.path.splitext(comp_tsv_path)[0]
call_path = os.path.join(call_base_path, region, graph)
# gatk3 and platypus: we just link in their vcf since they don't have call directory
if graph in ["gatk3", "platypus"]:
robust_makedirs(os.path.join(best_dir, region, graph))
else:
robust_makedirs(os.path.join(best_dir, region))
os.system("ln -fs {} {}".format(
os.path.abspath(call_path),
os.path.abspath(os.path.join(best_dir, region))))
# link in the preprocessed vcf from the comp dir to the same directory
comp_path = os.path.join(call_base_path + ".comp")
for pvcf in glob.glob(
os.path.join(comp_path, "preprocessed_vcfs", region,
"*_{}.vcf".format(graph))):
os.system("ln -fs {} {}".format(
os.path.abspath(pvcf),
os.path.abspath(
os.path.join(
best_dir, region, graph,
os.path.basename(pvcf).replace(
graph, "sample_preprocessed")))))
# link in the truth while we're at it
for pvcf in glob.glob(
os.path.join(comp_path, "preprocessed_vcfs", region,
"*_platvcf*.vcf")):
os.system("ln -fs {} {}".format(
os.path.abspath(pvcf),
os.path.abspath(os.path.join(best_dir, region, graph))))
def do_mendel(mergetable, options):
""" run rtg mendelian on all our merged vcfs """
header = ["graph", "all", "snp", "indel"]
for region, gd in mergetable.items():
table = []
for graph, mergefiles in gd.items():
annot_dir = os.path.join(options.out_dir, "mendel", region, graph)
robust_makedirs(annot_dir)
concordance = dict()
for kind, mergefile in mergefiles.items():
out_vcf = os.path.join(annot_dir, "mendel_{}.vcf.gz".format(kind))
con_vcf = os.path.join(annot_dir, "consistent_{}.vcf.gz".format(kind))
incon_vcf = os.path.join(annot_dir, "inconsistent_{}.vcf.gz".format(kind))
out_stdout = os.path.join(annot_dir, "mendel_{}.stdout".format(kind))
run("rtg mendelian -l -i {} -t {} --pedigree {} --output {} --output-consistent {} --output-inconsistent {} > {}".format(
mergefile,
os.path.join(options.comp_dir, "chrom.sdf"),
os.path.join(options.out_dir, "predigree.ped"),
out_vcf,
con_vcf,
incon_vcf,
out_stdout))
concordance[kind] = scrape_mendel(out_stdout)
table.append([graph, concordance["all"], concordance["snp"], concordance["indel"]])
# write the tsv for this region
with open(os.path.join(options.out_dir, "mendel-{}.tsv".format(region)), "w") as f:
f.write("\t".join(header) + "\n")
for row in table:
if None not in row:
line = [str(s) for s in row]
f.write("\t".join(line) + "\n")
def compute_snp1000g_baseline(job, input_gam, platinum, filter_indels, options):
""" make 1000 genomes sample graph by filtering the vcf
"""
# there is only one g1vcf graph per region per sample
# this function is also going to get called once for each graph type
# so we hack here to only run on refonly graphs (arbitrary choice)
if alignment_graph_tag(input_gam, options) != "refonly":
return
sample = alignment_sample_tag(input_gam, options)
if platinum is True and sample not in options.platinum_samples.split(","):
return
region = alignment_region_tag(input_gam, options)
if platinum is False:
g1kvcf_path = os.path.join(options.g1kvcf_path, region.upper() + ".vcf")
else:
g1kvcf_path = os.path.join(options.platinum_path, sample, region.upper() + ".vcf")
g1kbed_path = os.path.join(options.g1kvcf_path, region.upper() + ".bed")
filter_vcf_path = g1k_vcf_path(input_gam, platinum, filter_indels, options)
filter_fa_path = g1k_fa_path(input_gam, platinum, filter_indels, options)
filter_vg_path = g1k_vg_path(input_gam, platinum, filter_indels, options)
fasta_path = options.chrom_fa_path
do_filter = options.overwrite or not os.path.isfile(filter_vcf_path + ".gz")
do_construct = do_filter or not os.path.isfile(filter_vg_path)
# make sure we're dealing with a sample that's in the vcf
if do_filter or do_construct:
p = subprocess.Popen("grep {} {} | wc -l".format(sample, g1kvcf_path),
shell=True, stdout=subprocess.PIPE, stderr=sys.stderr, bufsize=-1)
output, _ = p.communicate()
assert p.wait() == 0
if int(output) == 0:
do_filter = False
do_construct = False
# make filtered compressed vcf for this sample
if do_filter:
robust_makedirs(os.path.dirname(filter_vcf_path))
if filter_indels is True:
filter_input_path = filter_vcf_path + ".in"
run("scripts/vcfFilterIndels.py {} > {}".format(g1kvcf_path, filter_input_path),
fail_hard = True)
else:
filter_input_path = g1kvcf_path
run("scripts/vcfFilterSample.py {} {} {} {} {}".format(filter_input_path,
fasta_path,
sample,
filter_vcf_path,
filter_fa_path),
fail_hard = True)
run("scripts/vcfsort {} > {}.sort ; mv {}.sort {}".format(filter_vcf_path,
filter_vcf_path,
filter_vcf_path,
filter_vcf_path))
run("bgzip -f {}".format(filter_vcf_path), fail_hard = True)
run("tabix -f -p vcf {}.gz".format(filter_vcf_path), fail_hard = True)
# load it into a vg graph
if do_construct:
with open(g1kbed_path) as bed_file:
coords = bed_file.readline().split()
# convert from bed to vcf coordinates by adding one to start
coords = (coords[0], int(coords[1]) + 1, int(coords[2]))
run("vg construct -v {}.gz -r {} -t {} -R {}:{}-{} > {}".format(filter_vcf_path, filter_fa_path,
options.vg_cores,
coords[0], coords[1], coords[2],
filter_vg_path),
fail_hard = True)
def compute_vg_variants(job, input_gam, options):
""" run vg pileup and vg call on the input
"""
# Move to the appropriate working directory from wherever Toil dropped us
os.chdir(options.cwd)
input_graph_path = graph_path(input_gam, options)
out_pileup_path = pileup_path(input_gam, options)
out_sample_vg_path = sample_vg_path(input_gam, options)
out_sample_vcf_path = out_sample_vg_path.replace(".vg", ".vcf")
out_sample_txt_path = sample_txt_path(input_gam, options)
out_augmented_vg_path = augmented_vg_path(input_gam, options)
out_gam_filter_path = gam_filter_path(input_gam, options)
out_gam_index_path = gam_index_path(input_gam, options)
out_bam_path = out_sample_vg_path.replace(".vg", ".bam")
do_genotype = options.genotype and (options.overwrite or not os.path.isfile(out_sample_vcf_path))
do_gam_filter= (options.overwrite or not os.path.isfile(out_gam_filter_path))
do_gam_index = do_genotype and (do_gam_filter or options.overwrite or not os.path.isdir(out_gam_index_path))
do_pu = not options.genotype and (options.overwrite or not os.path.isfile(out_pileup_path))
do_call = not options.genotype and (do_pu or not os.path.isfile(out_sample_vcf_path))
do_surject = options.surject and (options.overwrite or do_gam_filter or not os.path.isfile(out_bam_path))
# We need an XG here for the mase graph, but I haven't got time to refactor
# to make it in the right place. So just make it here.
temp_xg_path = job.fileStore.getLocalTempDir() + "/filter.xg"
if do_gam_filter or do_pu:
# Make sure we have the xg index around, which fiulter may need.
run("vg index -x {} {}".format(temp_xg_path, input_graph_path), fail_hard = True)
if do_gam_filter:
robust_makedirs(os.path.dirname(out_pileup_path))
run("vg filter -x {} {} {} {} > {}".format(temp_xg_path, input_gam,
options.filter_opts, input_graph_path,
out_gam_filter_path),
fail_hard = True)
if do_gam_index:
robust_makedirs(os.path.dirname(out_pileup_path))
run("rm -rf {} ; vg index {} -N -d {}".format(out_gam_index_path, out_gam_filter_path, out_gam_index_path),
fail_hard = True)
if do_pu:
robust_makedirs(os.path.dirname(out_pileup_path))
run("vg pileup {} {} {} -t {} > {}".format(input_graph_path,
out_gam_filter_path,
options.pileup_opts,
options.vg_cores,
out_pileup_path),
fail_hard = True)
ref = None
bedLength = -1
if do_call or do_genotype or do_surject:
robust_makedirs(os.path.dirname(out_sample_vcf_path))
region = alignment_region_tag(input_gam, options)
g1kbed_path = os.path.join(options.g1kvcf_path, region.upper() + ".bed")
with open(g1kbed_path) as f:
contig, offset, end = f.readline().split()[0:3]
bedLength = int(end) - int(offset)
# make the vcf
# can only do this if there is a "ref" path in the vg graph
ref = None
res_path = temp_path(options)
for ref_name in ["ref", contig]:
run("scripts/vgHasPath.sh {} {} > {}".format(input_graph_path, ref_name, res_path))
with open(res_path) as res_file:
if res_file.read()[0] == "1":
ref = ref_name
break
run("rm {}".format(res_path))
if ref is not None:
if do_genotype:
run("vg genotype {} {} -S -pv -q -i -C -o {} -r {} -c {} -s {} -t {} > {} 2> {}".format(input_graph_path,
out_gam_index_path,
offset,
ref,
contig,
alignment_sample_tag(input_gam, options),
options.vg_cores,
out_sample_vcf_path,
out_sample_vcf_path.replace(".vcf", ".vcf.stderr")),
fail_hard = True)
if do_call:
run("vg call {} {} {} -t {} -o {} -r {} -c {} -S {} -A {} > {} 2> {}".format(input_graph_path,
out_pileup_path,
options.call_opts,
options.vg_cores,
offset,
ref,
contig,
alignment_sample_tag(input_gam, options),
out_augmented_vg_path,
out_sample_vg_path.replace(".vg", ".vcf"),
out_sample_vg_path.replace(".vg", ".vcf.stderr")),
fail_hard = True)
if do_surject:
run("vg index {} -k {} -e {} -s -d {}.index -t {}".format(input_graph_path, 20, 5,
os.path.join(os.path.dirname(out_bam_path), "graph"),
options.vg_cores),
fail_hard = True)
run("vg surject {} -t {} -p {} -b -d {}.index > {}".format(out_gam_filter_path, options.vg_cores, ref,
os.path.join(os.path.dirname(out_bam_path), "graph"),
out_bam_path),
fail_hard = True)
# fix up chromosome coordinates so we can display on browser
if contig[0] != "c":
contig = "chr{}".format(contig)
contigLength = {"chr5": 181538259, "chr6": 170805979, "chr13": 114364328,
"chr17": 83257441, "chr19": 58617616}
# in header, change up the contig name and
run("samtools view -H {} | sed -e \"s/{}/{}/\" | sed -e \"s/{}/{}/\" > {}.sam".format(out_bam_path,
ref, contig,
bedLength, contigLength[contig],
out_bam_path),
fail_hard = True)
# in body, add offset and fix contig, leave in sam for now so we can debug
run("samtools view -F 256 {} | awk -v OFS=\'\\t\' \'{{$3=\"{}\"; $4=$4+{}; $5=60; $8=$8+{}; print $0}}\' >> {}.sam".format(out_bam_path,
contig,
offset, offset,
out_bam_path),
fail_hard = True)
# back to bam
run("samtools view {}.sam -b -F 4 | samtools sort - --threads {} -o {}".format(out_bam_path, options.vg_cores, out_bam_path),
fail_hard = True)
# and index
run("samtools index -b {}".format(out_bam_path), fail_hard = True)
def main(args):
options = parse_args(args)
robust_makedirs(os.path.join(options.out_dir, "comp_tables"))
# compute average score for each roc dir in this table
avg_table = []
# [region][method] --> (path, f1)
best_table = defaultdict(lambda: defaultdict(lambda: (None, -1)))
first = True
# sort the directories, assuming their names give info on their order
# in the roc
for comp_dir in sorted(
options.comp_dirs)[options.skip_first:len(options.comp_dirs) -
options.skip_last]:
print comp_dir
# this can happen easily using wildcards in input
if comp_dir == options.out_dir:
continue
# look through tsvs in comp_tables.
for tsv in glob.glob(os.path.join(comp_dir, "comp_tables", "*.tsv")):
# overwrite if first
# strip header and append if second
c = "cp {} ".format(
tsv) if first is True else "tail -n +2 {} >> ".format(tsv)
# just cat into the output directory
os.system("{} {}".format(
c,
os.path.join(options.out_dir, "comp_tables",
os.path.basename(tsv))))
print "{} {}".format(
c,
os.path.join(options.out_dir, "comp_tables",
os.path.basename(tsv)))
tb = os.path.basename(tsv).split("-")
if len(tb) > 2 and tb[0] == options.best_baseline and tb[
1] == options.best_comp:
avg_table.append(avg_acc(tsv, options))
update_best_table(best_table, tsv, options)
first = False
# make a call directory of links to the best in roc points for each graph
make_best_calls(best_table, options)
# write out our sompy vcf snp accutacy
with open(
os.path.join(
options.out_dir,
"{}-{}-{}_{}-avg.tsv".format(options.best_baseline,
options.best_comp, options.pcol,
options.rcol)), "w") as f:
lines = sorted(avg_table)
for line in lines:
for tok in line:
f.write(str(tok) + "\t")
f.write("\n")
# let's sort the output to make it easier to remove dead points
if options.smooth is True:
for tsv in glob.glob(
os.path.join(options.out_dir, "comp_tables", "*.tsv")):
print "smoothing {}".format(tsv)
with open(tsv) as f:
lines = [line for line in f]
lines = [lines[0]] + sorted(
lines[1:],
key=lambda x: (x.split()[0], float(x.split()[
options.pcol]), 1 - float(x.split()[options.rcol])))
# precisions can be bumpy (need to change to sensitivy?)
# use simple smoother in the meantime
lines = smooth_table([x.split() for x in lines], options)
with open(tsv, "w") as f:
for line in lines:
f.write(line)
def compute_vg_variants(job, input_gam, options):
""" run vg pileup and vg call on the input
"""
input_graph_path = graph_path(input_gam, options)
out_pileup_path = pileup_path(input_gam, options)
out_sample_vg_path = sample_vg_path(input_gam, options)
out_sample_txt_path = sample_txt_path(input_gam, options)
out_augmented_vg_path = augmented_vg_path(input_gam, options)
do_pu = options.overwrite or not os.path.isfile(out_pileup_path)
do_call = do_pu or not os.path.isfile(out_augmented_vg_path)
do_sample = options.sample and (do_pu or not os.path.isfile(out_sample_vg_path))
do_vcf = do_call or not os.path.isfile(out_sample_vg_path.replace(".vg", ".vcf"))
if do_pu:
RealTimeLogger.get().info("Computing Variants for {} {}".format(
input_graph_path,
input_gam))
robust_makedirs(os.path.dirname(out_pileup_path))
run("vg filter {} {} | vg pileup {} - {} -t {} > {}".format(input_gam,
options.filter_opts,
input_graph_path,
options.pileup_opts,
options.vg_cores,
out_pileup_path),
fail_hard = True)
if do_call:
robust_makedirs(os.path.dirname(out_sample_vg_path))
run("vg call {} {} {} -l -c {} -t {} > {}".format(input_graph_path,
out_pileup_path,
options.call_opts,
out_sample_txt_path,
options.vg_cores,
out_augmented_vg_path),
fail_hard = True)
if do_vcf:
region = alignment_region_tag(input_gam, options)
g1kbed_path = os.path.join(options.g1kvcf_path, region.upper() + ".bed")
with open(g1kbed_path) as f:
contig, offset = f.readline().split()[0:2]
# make the vcf
# can only do this if there is a "ref" path in the vg graph
ref = None
res_path = temp_path(options)
for ref_name in ["ref", contig]:
run("scripts/vgHasPath.sh {} {} > {}".format(input_graph_path, ref_name, res_path))
with open(res_path) as res_file:
if res_file.read()[0] == "1":
ref = ref_name
break
run("rm {}".format(res_path))
if ref is not None:
tasks = []
run("glenn2vcf {} {} -o {} -r {} -c {} -s {} -d {} > {} 2> {}".format(out_augmented_vg_path,
out_sample_txt_path,
offset,
ref,
contig,
alignment_sample_tag(input_gam, options),
options.depth,
out_sample_vg_path.replace(".vg", ".vcf"),
out_sample_vg_path.replace(".vg", ".vcf.stderr")),
fail_hard = True)
if do_sample:
robust_makedirs(os.path.dirname(out_augmented_vg_path))
run("vg call {} {} {} -t {} | vg ids -cs - > {}".format(input_graph_path,
out_pileup_path,
options.call_opts,
options.vg_cores,
out_sample_vg_path),
fail_hard = True)
def compute_vg_variants(job, input_gam, options):
""" run vg pileup and vg call on the input
"""
# Move to the appropriate working directory from wherever Toil dropped us
os.chdir(options.cwd)
input_graph_path = graph_path(input_gam, options)
out_pileup_path = pileup_path(input_gam, options)
out_sample_vg_path = sample_vg_path(input_gam, options)
out_sample_vcf_path = out_sample_vg_path.replace(".vg", ".vcf")
out_sample_txt_path = sample_txt_path(input_gam, options)
out_augmented_vg_path = augmented_vg_path(input_gam, options)
out_gam_filter_path = gam_filter_path(input_gam, options)
out_gam_index_path = gam_index_path(input_gam, options)
out_bam_path = out_sample_vg_path.replace(".vg", ".bam")
do_genotype = options.genotype and (options.overwrite or not os.path.isfile(out_sample_vcf_path))
do_gam_filter = options.overwrite or not os.path.isfile(out_gam_filter_path)
do_gam_index = do_genotype and (do_gam_filter or options.overwrite or not os.path.isdir(out_gam_index_path))
do_pu = not options.genotype and (options.overwrite or not os.path.isfile(out_pileup_path))
do_call = not options.genotype and (do_pu or not os.path.isfile(out_sample_vcf_path))
do_surject = options.surject and (options.overwrite or do_gam_filter or not os.path.isfile(out_bam_path))
# We need an XG here for the mase graph, but I haven't got time to refactor
# to make it in the right place. So just make it here.
temp_xg_path = job.fileStore.getLocalTempDir() + "/filter.xg"
if do_gam_filter or do_pu:
# Make sure we have the xg index around, which fiulter may need.
run("vg index -x {} {}".format(temp_xg_path, input_graph_path), fail_hard=True)
if do_gam_filter:
robust_makedirs(os.path.dirname(out_pileup_path))
run(
"vg filter -x {} {} {} {} > {}".format(
temp_xg_path, input_gam, options.filter_opts, input_graph_path, out_gam_filter_path
),
fail_hard=True,
)
if do_gam_index:
robust_makedirs(os.path.dirname(out_pileup_path))
run(
"rm -rf {} ; vg index {} -N -d {}".format(out_gam_index_path, out_gam_filter_path, out_gam_index_path),
fail_hard=True,
)
if do_pu:
robust_makedirs(os.path.dirname(out_pileup_path))
run(
"vg pileup {} {} {} -t {} > {}".format(
input_graph_path, out_gam_filter_path, options.pileup_opts, options.vg_cores, out_pileup_path
),
fail_hard=True,
)
ref = None
bedLength = -1
if do_call or do_genotype or do_surject:
robust_makedirs(os.path.dirname(out_sample_vcf_path))
region = alignment_region_tag(input_gam, options)
g1kbed_path = os.path.join(options.g1kvcf_path, region.upper() + ".bed")
with open(g1kbed_path) as f:
contig, offset, end = f.readline().split()[0:3]
bedLength = int(end) - int(offset)
# make the vcf
# can only do this if there is a "ref" path in the vg graph
ref = None
res_path = temp_path(options)
for ref_name in ["ref", contig]:
run("scripts/vgHasPath.sh {} {} > {}".format(input_graph_path, ref_name, res_path))
with open(res_path) as res_file:
if res_file.read()[0] == "1":
ref = ref_name
break
run("rm {}".format(res_path))
if ref is not None:
if do_genotype:
run(
"vg genotype {} {} -S -pv -q -i -C -o {} -r {} -c {} -s {} -t {} > {} 2> {}".format(
input_graph_path,
out_gam_index_path,
offset,
ref,
contig,
alignment_sample_tag(input_gam, options),
options.vg_cores,
out_sample_vcf_path,
out_sample_vcf_path.replace(".vcf", ".vcf.stderr"),
),
fail_hard=True,
)
if do_call:
run(
"vg call {} {} {} -t {} -o {} -r {} -c {} -S {} -A {} > {} 2> {}".format(
input_graph_path,
out_pileup_path,
options.call_opts,
options.vg_cores,
offset,
ref,
contig,
alignment_sample_tag(input_gam, options),
out_augmented_vg_path,
out_sample_vg_path.replace(".vg", ".vcf"),
out_sample_vg_path.replace(".vg", ".vcf.stderr"),
),
fail_hard=True,
)
if do_surject:
run(
"vg index {} -k {} -e {} -s -d {}.index -t {}".format(
input_graph_path, 20, 5, os.path.join(os.path.dirname(out_bam_path), "graph"), options.vg_cores
),
fail_hard=True,
)
run(
"vg surject {} -t {} -p {} -b -d {}.index > {}".format(
out_gam_filter_path,
options.vg_cores,
ref,
os.path.join(os.path.dirname(out_bam_path), "graph"),
out_bam_path,
),
fail_hard=True,
)
# fix up chromosome coordinates so we can display on browser
if contig[0] != "c":
contig = "chr{}".format(contig)
contigLength = {
"chr5": 181538259,
"chr6": 170805979,
"chr13": 114364328,
"chr17": 83257441,
"chr19": 58617616,
}
# in header, change up the contig name and
run(
'samtools view -H {} | sed -e "s/{}/{}/" | sed -e "s/{}/{}/" > {}.sam'.format(
out_bam_path, ref, contig, bedLength, contigLength[contig], out_bam_path
),
fail_hard=True,
)
# in body, add offset and fix contig, leave in sam for now so we can debug
run(
"samtools view -F 256 {} | awk -v OFS='\\t' '{{$3=\"{}\"; $4=$4+{}; $5=60; $8=$8+{}; print $0}}' >> {}.sam".format(
out_bam_path, contig, offset, offset, out_bam_path
),
fail_hard=True,
)
# back to bam
run(
"samtools view {}.sam -b -F 4 | samtools sort - --threads {} -o {}".format(
out_bam_path, options.vg_cores, out_bam_path
),
fail_hard=True,
)
# and index
run("samtools index -b {}".format(out_bam_path), fail_hard=True)
def main(args):
options = parse_args(args)
robust_makedirs(os.path.join(options.out_dir, "comp_tables"))
# compute average score for each roc dir in this table
avg_table = []
# [region][method] --> (path, f1)
best_table = defaultdict(lambda : defaultdict(lambda : (None, -1)))
first = True
# sort the directories, assuming their names give info on their order
# in the roc
for comp_dir in sorted(options.comp_dirs)[options.skip_first:len(options.comp_dirs) - options.skip_last]:
print comp_dir
# this can happen easily using wildcards in input
if comp_dir == options.out_dir:
continue
# look through tsvs in comp_tables.
for tsv in glob.glob(os.path.join(comp_dir, "comp_tables", "*.tsv")):
# overwrite if first
# strip header and append if second
c = "cp {} ".format(tsv) if first is True else "tail -n +2 {} >> ".format(tsv)
# just cat into the output directory
os.system("{} {}".format(c, os.path.join(options.out_dir, "comp_tables",
os.path.basename(tsv))))
print "{} {}".format(c, os.path.join(options.out_dir, "comp_tables",
os.path.basename(tsv)))
tb = os.path.basename(tsv).split("-")
if len(tb) > 2 and tb[0] == options.best_baseline and tb[1] == options.best_comp:
avg_table.append(avg_acc(tsv, options))
update_best_table(best_table, tsv, options)
first = False
# make a call directory of links to the best in roc points for each graph
make_best_calls(best_table, options)
# write out our sompy vcf snp accutacy
with open(os.path.join(options.out_dir, "{}-{}-{}_{}-avg.tsv".format(options.best_baseline,
options.best_comp,
options.pcol,
options.rcol)), "w") as f:
lines = sorted(avg_table)
for line in lines:
for tok in line:
f.write(str(tok) + "\t")
f.write("\n")
# let's sort the output to make it easier to remove dead points
if options.smooth is True:
for tsv in glob.glob(os.path.join(options.out_dir, "comp_tables", "*.tsv")):
print "smoothing {}".format(tsv)
with open(tsv) as f:
lines = [line for line in f]
lines = [lines[0]] + sorted(lines[1:], key = lambda x : (x.split()[0], float(x.split()[options.pcol]), 1 - float(x.split()[options.rcol])))
# precisions can be bumpy (need to change to sensitivy?)
# use simple smoother in the meantime
lines = smooth_table([x.split() for x in lines], options)
with open(tsv, "w") as f:
for line in lines:
f.write(line)
def main(args):
options = parse_args(args)
RealTimeLogger.start_master()
if options.classic:
# expect call_dir/SAMPLE/region.vcf
for sampledir in glob.glob(os.path.join(options.call_dir, "*")):
if os.path.isdir(sampledir):
sample = os.path.basename(sampledir)
vcfs = []
outfile = os.path.join(sampledir, "TOTAL.vcf")
for vcf in glob.glob(os.path.join(sampledir, "*.vcf")):
if os.path.basename(vcf) in [
"BRCA1.vcf", "BRCA2.vcf", "SMA.vcf", "LRC_KIR.vcf",
"MHC.vcf"
]:
run("vcfsort {} > {}.sort".format(vcf, vcf),
fail_hard=True)
run("bgzip -c {}.sort > {}.gz".format(vcf, vcf),
fail_hard=True)
run("rm -f {}.sort".format(vcf))
run("tabix -f -p vcf {}.gz".format(vcf),
fail_hard=True)
vcfs.append("{}.gz".format(vcf))
if len(vcfs) > 0:
run("vt cat {} > {}".format(" ".join(vcfs), outfile),
fail_hard=True)
run("vcfsort {} > {}.sort".format(outfile, outfile),
fail_hard=True)
run("mv {}.sort {}".format(outfile, outfile),
fail_hard=True)
run("bgzip -c {} > {}.gz".format(outfile, outfile),
fail_hard=True)
run("tabix -f -p vcf {}.gz".format(outfile),
fail_hard=True)
return 0
# expect call_dir/<REGION>/<GRAPH>/<SAMPLE>_sample.vcf
# count up regions
regions = set()
for regiondir in glob.glob(os.path.join(options.call_dir, "*")):
if os.path.isdir(regiondir):
region = os.path.basename(regiondir)
# avoid crufty directories (including outputs of previous runs of this script)
if region in ["brca1", "brca2", "mhc", "lrc_kir", "sma"]:
regions.add(region)
print regions
# count up graphs (that are present in every region)
graphs = set()
gcount = defaultdict(int)
for region in regions:
for graphdir in glob.glob(os.path.join(options.call_dir, region, "*")):
if os.path.isdir(graphdir):
graph = os.path.basename(graphdir)
gcount[graph] = gcount[graph] + 1
for graph, count in gcount.items():
if count == len(regions):
graphs.add(graph)
print graphs
# count up samples
samples = set()
scount = defaultdict(int)
for region in regions:
for graph in graphs:
for vcf in glob.glob(
os.path.join(options.call_dir, region, graph,
"*_sample.vcf")):
sample = os.path.basename(vcf).split("_")[0]
scount[sample] = scount[sample] + 1
for sample, count in scount.items():
samples.add(sample)
print samples
# make our output directory
out_dir = os.path.join(options.call_dir, options.name)
robust_makedirs(out_dir)
for graph in graphs:
g_out_dir = os.path.join(out_dir, graph)
for sample in samples:
vcf_files = []
for region in regions:
vcf = os.path.join(options.call_dir, region, graph,
"{}_sample.vcf".format(sample))
if os.path.isfile(vcf):
vcf_files.append((region, vcf))
# this sample doesn't span all regions, skip it
if len(vcf_files) < len(regions):
print "Skipping Sample {} for Graph {}".format(sample, graph)
continue
# output vcf
merge_vcf_path = os.path.join(out_dir, graph,
"{}_sample.vcf".format(sample))
# working directory for intermediates / debugging
work_path = os.path.join(out_dir, graph, "input", sample)
robust_makedirs(work_path)
# preprocess all the vcfs and leave in input dir
input_files = []
for region, vcf in vcf_files:
outbase = os.path.join(work_path, region)
run("vcfsort {} > {}.vcf".format(vcf, outbase), fail_hard=True)
run("bgzip -f {}.vcf".format(outbase))
run("tabix -f -p vcf {}.vcf.gz".format(outbase))
input_files.append("{}.vcf.gz".format(outbase))
# run the merge
run("vt cat {} > {}".format(" ".join(input_files), merge_vcf_path),
fail_hard=True)
# make an index just in case
run("vcfsort {} > {}.sort".format(merge_vcf_path, merge_vcf_path),
fail_hard=True)
run("mv {}.sort {}".format(merge_vcf_path, merge_vcf_path),
fail_hard=True)
run("bgzip -c {} > {}.gz".format(merge_vcf_path, merge_vcf_path),
fail_hard=True)
run("tabix -f -p vcf {}.gz".format(merge_vcf_path, merge_vcf_path),
fail_hard=True)
return 0
def compute_tree(options, mat, names):
""" make upgma hierarchical clustering and write it as png and
graphviz dot
"""
# oops, convert to biopython matrix
matrix = []
for i in xrange(len(names)):
row = []
for j in xrange(i + 1):
# tree constructor writes 0-distances as 1s for some reason
# so we hack around here
val = float(mat[names[i]][names[j]])
if val == 0.:
val = 1e-10
elif val == 1.:
val = 1.1
row.append(val)
matrix.append(row)
dm = _DistanceMatrix(names, matrix)
# upgma tree
constructor = DistanceTreeConstructor()
tree = constructor.upgma(dm)
robust_makedirs(os.path.dirname(tree_path(options)))
Phylo.write(tree, tree_path(options), "newick")
# png tree -- note : doesn't work in toil
def f(x):
if "Inner" in str(x):
return ""
else:
return x
Phylo.draw_graphviz(tree, label_func = f, node_size=1000, node_shape="s", font_size=10)
pylab.savefig(tree_path(options).replace("newick", "png"))
# graphviz
# get networkx graph
nxgraph = Phylo.to_networkx(tree)
# make undirected
nxgraph = nx.Graph(nxgraph)
# push names to name labels
nxgraph = nx.convert_node_labels_to_integers(nxgraph, label_attribute="label")
for node_id in nxgraph.nodes():
node = nxgraph.node[node_id]
if "Inner" in str(node["label"]):
node["label"] = "\"\""
node["width"] = 0.001
node["height"] = 0.001
else:
node["fontsize"] = 18
for edge_id in nxgraph.edges():
edge = nxgraph.edge[edge_id[0]][edge_id[1]]
# in graphviz, weight means something else, so make it a label
weight = float(edge["weight"])
# undo hack from above
if weight > 1:
weight = 1.
if weight <= 1e-10 or weight == 1.:
weight = 0.
edge["weight"] = None
edge["label"] = "{0:.3g}".format(float(weight) * 100.)
edge["fontsize"] = 14
edge["len"] = draw_len(weight)
nx.write_dot(nxgraph, tree_path(options).replace("newick", "dot"))
def plot_vcf_comp(tsv_path, options):
""" take the big vcf compare table and make precision_recall plots for all the categories"""
out_dir = os.path.join(options.comp_dir, "comp_plots")
robust_makedirs(out_dir)
out_name = os.path.basename(os.path.splitext(tsv_path)[0])
sample = out_name.split("-")[-1].upper()
region = out_name.split("-")[-2].upper()
def out_base_path(tag, label, extension):
bd = tag if extension != ".tsv" else "tsv"
ret = os.path.join(out_dir, bd, "-".join(out_name.split("-")[:-1]) + "-{}-{}-".format(sample, tag) + region) + "_" + label + extension
robust_makedirs(os.path.dirname(ret))
return ret
params = " ".join(PLOT_PARAMS)
# precision recall scatter plot
header = vcf_dist_header(options)
# strip qual
header = header[:-1]
for i in range(len(header) / 2):
prec_idx = 2 * i
rec_idx = prec_idx + 1
qual_idx = len(header)
print prec_idx, header[prec_idx], rec_idx, header[rec_idx]
ptoks = header[prec_idx].split("-")
rtoks = header[rec_idx].split("-")
assert ptoks[1] == "Precision"
assert rtoks[1] == "Recall"
assert ptoks[:1] == rtoks[:1]
comp_cat = ptoks[0]
if comp_cat not in ["TOT", "SNP", "INDEL"]:
continue
label = header[prec_idx].replace("Precision", "acc")
acc_tsv = out_base_path("pr", label, ".tsv")
print "Make {} tsv with cols {} {}".format(label, rec_idx, prec_idx)
# +1 to convert to awk 1-base coordinates. +1 again since header doesnt include row_label col
awkcmd = '''if (NR!=1) print $1 "\t" ${} "\t" ${} "\t" ${}'''.format(rec_idx + 2, prec_idx + 2, qual_idx + 2)
awkstr = "awk \'{" + awkcmd + "}\'"
run("{} {} > {}".format(awkstr, tsv_path, acc_tsv))
acc_png = out_base_path("pr", label, ".png")
title = sample.upper() + " "
if comp_cat == "TOT":
title += " Total Accuracy"
else:
title += " {} Accuracy".format(comp_cat.title())
if region == "TOTAL":
title += ", all regions"
else:
title += ", {}".format(region)
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x -0.01 --max_x 1.01 --min_y -0.01 --max_y 1.01".format(acc_tsv, acc_png, title, params)
print cmd
os.system(cmd)
#flatten to max f1 tsv and plot as bars
f1_tsv = out_base_path("f1bar", label, ".tsv")
f1_png = out_base_path("f1bar", label, ".png")
f1_pr_tsv = out_base_path("f1pr", label, ".tsv")
f1_pr_png = out_base_path("f1pr", label, ".png")
f1_qual_tsv = out_base_path("f1qual", label, ".tsv")
f1_qual_png = out_base_path("f1qual", label, ".png")
make_max_f1_tsv(acc_tsv, f1_tsv, f1_pr_tsv, f1_qual_tsv, options)
cmd = "scripts/barchart.py {} --ascending --no_n --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Max F1\" {}".format(f1_tsv, f1_png, title, params)
print cmd
os.system(cmd)
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5".format(f1_pr_tsv, f1_pr_png, title, params)
print cmd
os.system(cmd)
cmd = "scripts/barchart.py {} --ascending --no_n --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Quality for Max F1\" {}".format(f1_qual_tsv, f1_qual_png, title, params)
print cmd
os.system(cmd)
if options.top is True:
# top 20
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.798 --max_x 1.002 --min_y 0.798 --max_y 1.002".format(acc_tsv, acc_png.replace(".png", "_top20.png"), title, params)
print cmd
os.system(cmd)
# top 20
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 11 --height 5.5 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.796 --max_x 1.004 --min_y 0.796 --max_y 1.004".format(acc_tsv, acc_png.replace(".png", "_top20_inset.png"), title, params)
print cmd
os.system(cmd)
# top 40
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.596 --max_x 1.004 --min_y 0.596 --max_y 1.004".format(acc_tsv, acc_png.replace(".png", "_top40.png"), title, params)
print cmd
os.system(cmd)
# top .5 bar
cmd = "scripts/barchart.py {} --ascending --no_n --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Max F1\" {} --min 0.5".format(f1_tsv, f1_png.replace(".png", "_top50.png"), title, params)
print cmd
os.system(cmd)
# top .6 bar
cmd = "scripts/barchart.py {} --ascending --no_n --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Max F1\" {} --min 0.6".format(f1_tsv, f1_png.replace(".png", "_top60.png"), title, params)
print cmd
os.system(cmd)
# top .7 bar
cmd = "scripts/barchart.py {} --ascending --no_n --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Max F1\" {} --min 0.7".format(f1_tsv, f1_png.replace(".png", "_top70.png"), title, params)
print cmd
os.system(cmd)
# top .85 bar
cmd = "scripts/barchart.py {} --ascending --no_n --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Max F1\" {} --min 0.85".format(f1_tsv, f1_png.replace(".png", "_top85.png"), title, params)
print cmd
os.system(cmd)
# top .25 f1pr scatter
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.746 --max_x 1.004 --min_y 0.746 --max_y 1.004".format(f1_pr_tsv, f1_pr_png.replace(".png", "_top25.png"), title, params)
print cmd
os.system(cmd)
# top .50 f1pr scatter
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.496 --max_x 1.004 --min_y 0.496 --max_y 1.004".format(f1_pr_tsv, f1_pr_png.replace(".png", "_top50.png"), title, params)
print cmd
os.system(cmd)
# top .65 f1pr scatter
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.646 --max_x 1.004 --min_y 0.646 --max_y 1.004".format(f1_pr_tsv, f1_pr_png.replace(".png", "_top65.png"), title, params)
print cmd
os.system(cmd)
def out_base_path(tag, label, extension):
bd = tag if extension != ".tsv" else "tsv"
ret = os.path.join(out_dir, bd, "-".join(out_name.split("-")[:-1]) + "-{}-{}-".format(sample, tag) + region) + "_" + label + extension
robust_makedirs(os.path.dirname(ret))
return ret
def plot_vcf_comp(tsv_path, options):
""" take the big vcf compare table and make precision_recall plots for all the categories"""
out_dir = os.path.join(options.comp_dir, "comp_plots")
robust_makedirs(out_dir)
out_name = os.path.basename(os.path.splitext(tsv_path)[0])
sample = out_name.split("-")[-1].upper()
region = out_name.split("-")[-2].upper()
def out_base_path(tag, label, extension):
bd = tag if extension != ".tsv" else "tsv"
ret = (
os.path.join(out_dir, bd, "-".join(out_name.split("-")[:-1]) + "-{}-{}-".format(sample, tag) + region)
+ "_"
+ label
+ extension
)
robust_makedirs(os.path.dirname(ret))
return ret
params = " ".join(PLOT_PARAMS)
# precision recall scatter plot
header = vcf_dist_header(options)
# strip qual
header = header[:-1]
for i in range(len(header) / 2):
prec_idx = 2 * i
rec_idx = prec_idx + 1
qual_idx = len(header)
print prec_idx, header[prec_idx], rec_idx, header[rec_idx]
ptoks = header[prec_idx].split("-")
rtoks = header[rec_idx].split("-")
assert ptoks[1] == "Precision"
assert rtoks[1] == "Recall"
assert ptoks[:1] == rtoks[:1]
comp_cat = ptoks[0]
if comp_cat not in ["TOT", "SNP", "INDEL"]:
continue
label = header[prec_idx].replace("Precision", "acc")
acc_tsv = out_base_path("pr", label, ".tsv")
print "Make {} tsv with cols {} {}".format(label, rec_idx, prec_idx)
# +1 to convert to awk 1-base coordinates. +1 again since header doesnt include row_label col
awkcmd = """if (NR!=1) print $1 "\t" ${} "\t" ${} "\t" ${}""".format(rec_idx + 2, prec_idx + 2, qual_idx + 2)
awkstr = "awk '{" + awkcmd + "}'"
run("{} {} > {}".format(awkstr, tsv_path, acc_tsv))
acc_png = out_base_path("pr", label, ".png")
title = sample.upper() + " "
if comp_cat == "TOT":
title += " Total Accuracy"
else:
title += " {} Accuracy".format(comp_cat.title())
if region == "TOTAL":
title += ", all regions"
else:
title += ", {}".format(region)
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x -0.01 --max_x 1.01 --min_y -0.01 --max_y 1.01'.format(
acc_tsv, acc_png, title, params
)
print cmd
os.system(cmd)
# flatten to max f1 tsv and plot as bars
f1_tsv = out_base_path("f1bar", label, ".tsv")
f1_png = out_base_path("f1bar", label, ".png")
f1_pr_tsv = out_base_path("f1pr", label, ".tsv")
f1_pr_png = out_base_path("f1pr", label, ".png")
f1_qual_tsv = out_base_path("f1qual", label, ".tsv")
f1_qual_png = out_base_path("f1qual", label, ".png")
make_max_f1_tsv(acc_tsv, f1_tsv, f1_pr_tsv, f1_qual_tsv, options)
cmd = 'scripts/barchart.py {} --ascending --no_n --save {} --title "{}" --x_sideways --x_label "Graph" --y_label "Max F1" {}'.format(
f1_tsv, f1_png, title, params
)
print cmd
os.system(cmd)
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5'.format(
f1_pr_tsv, f1_pr_png, title, params
)
print cmd
os.system(cmd)
cmd = 'scripts/barchart.py {} --ascending --no_n --save {} --title "{}" --x_sideways --x_label "Graph" --y_label "Quality for Max F1" {}'.format(
f1_qual_tsv, f1_qual_png, title, params
)
print cmd
os.system(cmd)
if options.top is True:
# top 20
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.798 --max_x 1.002 --min_y 0.798 --max_y 1.002'.format(
acc_tsv, acc_png.replace(".png", "_top20.png"), title, params
)
print cmd
os.system(cmd)
# top 20
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 11 --height 5.5 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.796 --max_x 1.004 --min_y 0.796 --max_y 1.004'.format(
acc_tsv, acc_png.replace(".png", "_top20_inset.png"), title, params
)
print cmd
os.system(cmd)
# top 40
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.596 --max_x 1.004 --min_y 0.596 --max_y 1.004'.format(
acc_tsv, acc_png.replace(".png", "_top40.png"), title, params
)
print cmd
os.system(cmd)
# top .5 bar
cmd = 'scripts/barchart.py {} --ascending --no_n --save {} --title "{}" --x_sideways --x_label "Graph" --y_label "Max F1" {} --min 0.5'.format(
f1_tsv, f1_png.replace(".png", "_top50.png"), title, params
)
print cmd
os.system(cmd)
# top .6 bar
cmd = 'scripts/barchart.py {} --ascending --no_n --save {} --title "{}" --x_sideways --x_label "Graph" --y_label "Max F1" {} --min 0.6'.format(
f1_tsv, f1_png.replace(".png", "_top60.png"), title, params
)
print cmd
os.system(cmd)
# top .7 bar
cmd = 'scripts/barchart.py {} --ascending --no_n --save {} --title "{}" --x_sideways --x_label "Graph" --y_label "Max F1" {} --min 0.7'.format(
f1_tsv, f1_png.replace(".png", "_top70.png"), title, params
)
print cmd
os.system(cmd)
# top .85 bar
cmd = 'scripts/barchart.py {} --ascending --no_n --save {} --title "{}" --x_sideways --x_label "Graph" --y_label "Max F1" {} --min 0.85'.format(
f1_tsv, f1_png.replace(".png", "_top85.png"), title, params
)
print cmd
os.system(cmd)
# top .25 f1pr scatter
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.746 --max_x 1.004 --min_y 0.746 --max_y 1.004'.format(
f1_pr_tsv, f1_pr_png.replace(".png", "_top25.png"), title, params
)
print cmd
os.system(cmd)
# top .50 f1pr scatter
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.496 --max_x 1.004 --min_y 0.496 --max_y 1.004'.format(
f1_pr_tsv, f1_pr_png.replace(".png", "_top50.png"), title, params
)
print cmd
os.system(cmd)
# top .65 f1pr scatter
cmd = 'scripts/scatter.py {} --save {} --title "{}" --x_label "Recall" --y_label "Precision" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.646 --max_x 1.004 --min_y 0.646 --max_y 1.004'.format(
f1_pr_tsv, f1_pr_png.replace(".png", "_top65.png"), title, params
)
print cmd
os.system(cmd)
def compute_linear_variants(job, input_gam, options):
""" project to bam, then run samtools to call some variants
"""
# Move to the appropriate working directory from wherever Toil dropped us
os.chdir(options.cwd)
input_graph_path = graph_path(input_gam, options)
input_index_path = index_path(input_graph_path, options)
# can only do this if there is a "ref" path in the vg graph
res_path = temp_path(options)
run("scripts/vgHasPath.sh {} {} > {}".format(input_graph_path, "ref", res_path))
has_ref = False
with open(res_path) as res_file:
has_ref = res_file.read()[0] == "1"
run("rm {}".format(res_path))
if has_ref:
surject_path = projected_bam_path(input_gam, options)
out_vcf_path = linear_vcf_path(input_gam, options)
out_vg_path = linear_vg_path(input_gam, options)
fasta_path = ref_path(input_gam, options)
do_surject = options.overwrite or not os.path.isfile(surject_path)
do_vcf = do_surject or not os.path.isfile(out_vcf_path + ".gz")
do_vg = do_vcf or not os.path.isfile(out_vg_path)
if do_surject:
robust_makedirs(os.path.dirname(surject_path))
prefix_path = temp_path(options, ".prefix")
# surject to reference path (name hardcoded to ref for now)
run("vg surject -d {} -p {} -b {} -t {} | samtools sort -o - {}> {}".format(
input_index_path,
"ref",
input_gam,
options.vg_cores,
prefix_path,
surject_path),
timeout_sec=options.timeout,
timeout_dep=surject_path)
run("rm -f {}".format(prefix_path))
if do_vcf:
# todo: we assume that all graphs have same reference fasta, here.
# this is false for, ex, simons which uses grchg37 instead of 38.
# create pileup in bcf using samtools
# http://samtools.sourceforge.net/mpileup.shtml
assert os.path.isfile(fasta_path)
robust_makedirs(os.path.dirname(out_vcf_path))
run("samtools mpileup -I -u -t DP -f {} {} | bcftools call -m -V indels - > {}".format(
fasta_path,
surject_path,
out_vcf_path))
# make compressed index
run("bgzip -f {}".format(out_vcf_path))
run("tabix -f -p vcf {}.gz".format(out_vcf_path))
if do_vg:
# and convert back to vg...
robust_makedirs(os.path.dirname(out_vg_path))
run("vg construct -v {}.gz -r {} -t {} > {}".format(out_vcf_path, fasta_path,
options.vg_cores, out_vg_path))
def plot_vcf_comp(tsv_path, options):
""" take the big vcf compare table and make precision_recall plots for all the categories"""
out_dir = os.path.join(options.comp_dir, "comp_plots")
robust_makedirs(out_dir)
out_name = os.path.basename(os.path.splitext(tsv_path)[0])
out_base_path = os.path.join(out_dir, out_name)
region = out_name.split("-")[-1].upper()
out_base_path_f1 = os.path.join(out_dir, "-".join(out_name.split("-")[:-1]) + "--f1-" + region)
params = " ".join(PLOT_PARAMS)
# precision recall scatter plot
header = vcf_dist_header(options)
# strip qual
header = header[:-1]
for i in range(len(header) / 2):
prec_idx = 2 * i
rec_idx = prec_idx + 1
qual_idx = len(header)
print prec_idx, header[prec_idx], rec_idx, header[rec_idx]
ptoks = header[prec_idx].split("-")
rtoks = header[rec_idx].split("-")
assert ptoks[1] == "Precision"
assert rtoks[1] == "Recall"
assert ptoks[:1] == rtoks[:1]
comp_cat = ptoks[0]
if comp_cat not in ["TOT", "SNP", "INDEL"]:
continue
label = header[prec_idx].replace("Precision", "acc")
acc_tsv = out_base_path + "_" + label + ".tsv"
print "Make {} tsv with cols {} {}".format(label, rec_idx, prec_idx)
# +1 to convert to awk 1-base coordinates. +1 again since header doesnt include row_label col
awkcmd = '''if (NR!=1) print $1 "\t" ${} "\t" ${} "\t" ${}'''.format(rec_idx + 2, prec_idx + 2, qual_idx + 2)
awkstr = "awk \'{" + awkcmd + "}\'"
run("{} {} > {}".format(awkstr, tsv_path, acc_tsv))
acc_png = out_base_path + "_" + label + ".png"
title = "VCF"
if comp_cat == "TOT":
title += " Total Accuracy"
else:
title += " {} Accuracy".format(comp_cat)
title += " for {}".format(region)
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x -0.01 --max_x 1.01 --min_y -0.01 --max_y 1.01".format(acc_tsv, acc_png, title, params)
print cmd
os.system(cmd)
#flatten to max f1 tsv and plot as bars
f1_tsv = out_base_path_f1 + "_" + label + ".tsv"
f1_png = out_base_path_f1 + "_" + label + ".png"
f1_pr_tsv = out_base_path_f1.replace("-f1-", "-f1--pr-") + "_" + label + ".tsv"
f1_pr_png = out_base_path_f1.replace("-f1-", "-f1--pr-") + "_" + label + ".png"
f1_qual_tsv = out_base_path_f1.replace("-f1-", "-f1-qual-") + "_" + label + ".tsv"
f1_qual_png = out_base_path_f1.replace("-f1-", "-f1-qual-") + "_" + label + ".png"
make_max_f1_tsv(acc_tsv, f1_tsv, f1_pr_tsv, f1_qual_tsv, options)
cmd = "scripts/barchart.py {} --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Max F1\" {}".format(f1_tsv, f1_png, title, params)
print cmd
os.system(cmd)
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x -0.01 --max_x 1.01 --min_y -0.01 --max_y 1.01".format(f1_pr_tsv, f1_pr_png, title, params)
print cmd
os.system(cmd)
cmd = "scripts/barchart.py {} --save {} --title \"{}\" --x_sideways --x_label \"Graph\" --y_label \"Quality for Max F1\" {} --max 20".format(f1_qual_tsv, f1_qual_png, title, params)
print cmd
os.system(cmd)
if options.top is True:
# top 20
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.798 --max_x 1.002 --min_y 0.798 --max_y 1.002".format(acc_tsv, acc_png.replace(".png", "_top20.png"), title, params)
print cmd
os.system(cmd)
# top 20
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 11 --height 5.5 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.796 --max_x 1.004 --min_y 0.796 --max_y 1.004".format(acc_tsv, acc_png.replace(".png", "_top20_inset.png"), title, params)
print cmd
os.system(cmd)
# top 40
cmd = "scripts/scatter.py {} --save {} --title \"{}\" --x_label \"Recall\" --y_label \"Precision\" --width 18 --height 9 {} --lines --no_n --line_width 1.5 --marker_size 5 --min_x 0.596 --max_x 1.004 --min_y 0.596 --max_y 1.004".format(acc_tsv, acc_png.replace(".png", "_top40.png"), title, params)
print cmd
os.system(cmd)
def compute_snp1000g_baseline(job, input_gam, platinum, filter_indels, options):
""" make 1000 genomes sample graph by filtering the vcf
"""
# Move to the appropriate working directory from wherever Toil dropped us
os.chdir(options.cwd)
# there is only one g1vcf graph per region per sample
# this function is also going to get called once for each graph type
# so we hack here to only run on refonly graphs (arbitrary choice)
if alignment_graph_tag(input_gam, options) != "refonly":
return
sample = alignment_sample_tag(input_gam, options)
if platinum is True and sample not in options.platinum_samples.split(","):
return
region = alignment_region_tag(input_gam, options)
if platinum is False:
g1kvcf_path = os.path.join(options.g1kvcf_path, region.upper() + ".vcf")
else:
g1kvcf_path = os.path.join(options.platinum_path, sample, region.upper() + ".vcf")
g1kbed_path = os.path.join(options.g1kvcf_path, region.upper() + ".bed")
filter_vcf_path = g1k_vcf_path(input_gam, platinum, filter_indels, options)
filter_fa_path = g1k_fa_path(input_gam, platinum, filter_indels, options)
filter_vg_path = g1k_vg_path(input_gam, platinum, filter_indels, options)
fasta_path = options.chrom_fa_path
do_filter = options.overwrite or not os.path.isfile(filter_vcf_path + ".gz")
do_construct = do_filter or not os.path.isfile(filter_vg_path)
# make sure we're dealing with a sample that's in the vcf
if do_filter or do_construct:
p = subprocess.Popen("grep {} {} | wc -l".format(sample, g1kvcf_path),
shell=True, stdout=subprocess.PIPE, stderr=sys.stderr, bufsize=-1)
output, _ = p.communicate()
assert p.wait() == 0
if int(output) == 0:
do_filter = False
do_construct = False
# make filtered compressed vcf for this sample
if do_filter:
robust_makedirs(os.path.dirname(filter_vcf_path))
if filter_indels is True:
filter_input_path = filter_vcf_path + ".in"
run("scripts/vcfFilterIndels.py {} > {}".format(g1kvcf_path, filter_input_path),
fail_hard = True)
else:
filter_input_path = g1kvcf_path
run("scripts/vcfFilterSample.py {} {} {} {} {}".format(filter_input_path,
fasta_path,
sample,
filter_vcf_path,
filter_fa_path),
fail_hard = True)
run("scripts/vcfsort {} > {}.sort ; mv {}.sort {}".format(filter_vcf_path,
filter_vcf_path,
filter_vcf_path,
filter_vcf_path))
run("bgzip -f {}".format(filter_vcf_path), fail_hard = True)
run("tabix -f -p vcf {}.gz".format(filter_vcf_path), fail_hard = True)
# load it into a vg graph
if do_construct:
with open(g1kbed_path) as bed_file:
coords = bed_file.readline().split()
# convert from bed to vcf coordinates by adding one to start
coords = (coords[0], int(coords[1]) + 1, int(coords[2]))
run("vg construct -v {}.gz -r {} -t {} -R {}:{}-{} > {}".format(filter_vcf_path, filter_fa_path,
options.vg_cores,
coords[0], coords[1], coords[2],
filter_vg_path),
fail_hard = True)
def main(args):
options = parse_args(args)
RealTimeLogger.start_master()
if options.classic:
# expect call_dir/SAMPLE/region.vcf
for sampledir in glob.glob(os.path.join(options.call_dir, "*")):
if os.path.isdir(sampledir):
sample = os.path.basename(sampledir)
vcfs = []
outfile = os.path.join(sampledir, "TOTAL.vcf")
for vcf in glob.glob(os.path.join(sampledir, "*.vcf")):
if os.path.basename(vcf) in ["BRCA1.vcf", "BRCA2.vcf", "SMA.vcf", "LRC_KIR.vcf", "MHC.vcf"]:
run("vcfsort {} > {}.sort".format(vcf, vcf), fail_hard = True)
run("bgzip -c {}.sort > {}.gz".format(vcf, vcf), fail_hard = True)
run("rm -f {}.sort".format(vcf))
run("tabix -f -p vcf {}.gz".format(vcf), fail_hard = True)
vcfs.append("{}.gz".format(vcf))
if len(vcfs) > 0:
run("vt cat {} > {}".format(" ".join(vcfs), outfile),
fail_hard = True)
run("vcfsort {} > {}.sort".format(outfile, outfile), fail_hard = True)
run("mv {}.sort {}".format(outfile, outfile), fail_hard = True)
run("bgzip -c {} > {}.gz".format(outfile, outfile), fail_hard = True)
run("tabix -f -p vcf {}.gz".format(outfile), fail_hard = True)
return 0
# expect call_dir/<REGION>/<GRAPH>/<SAMPLE>_sample.vcf
# count up regions
regions = set()
for regiondir in glob.glob(os.path.join(options.call_dir, "*")):
if os.path.isdir(regiondir):
region = os.path.basename(regiondir)
# avoid crufty directories (including outputs of previous runs of this script)
if region in ["brca1", "brca2", "mhc", "lrc_kir", "sma"]:
regions.add(region)
print regions
# count up graphs (that are present in every region)
graphs = set()
gcount = defaultdict(int)
for region in regions:
for graphdir in glob.glob(os.path.join(options.call_dir, region, "*")):
if os.path.isdir(graphdir):
graph = os.path.basename(graphdir)
gcount[graph] = gcount[graph] + 1
for graph, count in gcount.items():
if count == len(regions):
graphs.add(graph)
print graphs
# count up samples
samples = set()
scount = defaultdict(int)
for region in regions:
for graph in graphs:
for vcf in glob.glob(os.path.join(options.call_dir, region, graph, "*_sample.vcf")):
sample = os.path.basename(vcf).split("_")[0]
scount[sample] = scount[sample] + 1
for sample, count in scount.items():
samples.add(sample)
print samples
# make our output directory
out_dir = os.path.join(options.call_dir, options.name)
robust_makedirs(out_dir)
for graph in graphs:
g_out_dir = os.path.join(out_dir, graph)
for sample in samples:
vcf_files = []
for region in regions:
vcf = os.path.join(options.call_dir, region, graph, "{}_sample.vcf".format(sample))
if os.path.isfile(vcf):
vcf_files.append((region, vcf))
# this sample doesn't span all regions, skip it
if len(vcf_files) < len(regions):
print "Skipping Sample {} for Graph {}".format(sample, graph)
continue
# output vcf
merge_vcf_path = os.path.join(out_dir, graph, "{}_sample.vcf".format(sample))
# working directory for intermediates / debugging
work_path = os.path.join(out_dir, graph, "input", sample)
robust_makedirs(work_path)
# preprocess all the vcfs and leave in input dir
input_files = []
for region, vcf in vcf_files:
outbase = os.path.join(work_path, region)
run("vcfsort {} > {}.vcf".format(vcf, outbase), fail_hard = True)
run("bgzip -f {}.vcf".format(outbase))
run("tabix -f -p vcf {}.vcf.gz".format(outbase))
input_files.append("{}.vcf.gz".format(outbase))
# run the merge
run("vt cat {} > {}".format(" ".join(input_files), merge_vcf_path), fail_hard = True)
# make an index just in case
run("vcfsort {} > {}.sort".format(merge_vcf_path, merge_vcf_path), fail_hard = True)
run("mv {}.sort {}".format(merge_vcf_path, merge_vcf_path), fail_hard = True)
run("bgzip -c {} > {}.gz".format(merge_vcf_path, merge_vcf_path), fail_hard = True)
run("tabix -f -p vcf {}.gz".format(merge_vcf_path, merge_vcf_path), fail_hard = True)
return 0
