def pipeline(col_num, step, dist, acf_dist, prefix, threshold, seed,
bed_files, mlog=True, region_filter_p=1, region_filter_n=None,
genome_control=False, db=None, use_fdr=True):
sys.path.insert(0, op.join(op.dirname(__file__), ".."))
from cpv import acf, slk, fdr, peaks, region_p, stepsize, filter
from cpv._common import genome_control_adjust, genomic_control, bediter
import operator
if step is None:
step = min(acf_dist, stepsize.stepsize(bed_files, col_num))
print("calculated stepsize as: %i" % step, file=sys.stderr)
lags = list(range(1, acf_dist, step))
lags.append(lags[-1] + step)
prefix = prefix.rstrip(".")
putative_acf_vals = acf.acf(bed_files, lags, col_num, simple=False,
mlog=mlog)
acf_vals = []
# go out to max requested distance but stop once an autocorrelation
# < 0.05 is added.
for a in putative_acf_vals:
# a is ((lmin, lmax), (corr, N))
# this heuristic seems to work. stop just above the 0.08 correlation
# lag.
if a[1][0] < 0.04 and len(acf_vals) > 2: break
acf_vals.append(a)
if a[1][0] < 0.04 and len(acf_vals): break
# save the arguments that this was called with.
with open(prefix + ".args.txt", "w") as fh:
print(" ".join(sys.argv[1:]) + "\n", file=fh)
import datetime
print("date: %s" % datetime.datetime.today(), file=fh)
from .__init__ import __version__
print("version:", __version__, file=fh)
with open(prefix + ".acf.txt", "w") as fh:
acf_vals = acf.write_acf(acf_vals, fh)
print("wrote: %s" % fh.name, file=fh)
print("ACF:\n", open(prefix + ".acf.txt").read(), file=sys.stderr)
spvals, opvals = array.array('f'), array.array('f')
with ts.nopen(prefix + ".slk.bed.gz", "w") as fhslk:
fhslk.write('#chrom\tstart\tend\tp\tregion-p\n')
for chrom, results in slk.adjust_pvals(bed_files, col_num, acf_vals):
fmt = chrom + "\t%i\t%i\t%.4g\t%.4g\n"
for row in results:
row = tuple(row)
fhslk.write(fmt % row)
opvals.append(row[-2])
spvals.append(row[-1])
print("# original lambda: %.2f" % genomic_control(opvals), file=sys.stderr)
del opvals
gc_lambda = genomic_control(spvals)
print("wrote: %s with lambda: %.2f" % (fhslk.name, gc_lambda),
file=sys.stderr)
if genome_control:
fhslk = ts.nopen(prefix + ".slk.gc.bed.gz", "w")
adj = genome_control_adjust([d['p'] for d in bediter(prefix + ".slk.bed.gz", -1)])
for i, line in enumerate(ts.nopen(prefix + ".slk.bed.gz")):
print("%s\t%.5g" % (line.rstrip("\r\n"), adj[i]), file=fhslk)
fhslk.close()
print("wrote: %s" % fhslk.name, file=sys.stderr)
with ts.nopen(prefix + ".fdr.bed.gz", "w") as fh:
fh.write('#chrom\tstart\tend\tp\tregion-p\tregion-q\n')
for bh, l in fdr.fdr(fhslk.name, -1):
fh.write("%s\t%.4g\n" % (l.rstrip("\r\n"), bh))
print("wrote: %s" % fh.name, file=sys.stderr)
fregions = prefix + ".regions.bed.gz"
with ts.nopen(fregions, "w") as fh:
list(peaks.peaks(prefix + ".fdr.bed.gz", -1 if use_fdr else -2, threshold, seed,
dist, fh, operator.le))
n_regions = sum(1 for _ in ts.nopen(fregions))
print("wrote: %s (%i regions)" % (fregions, n_regions), file=sys.stderr)
if n_regions == 0:
sys.exit()
with ts.nopen(prefix + ".regions-p.bed.gz", "w") as fh:
N = 0
fh.write("#chrom\tstart\tend\tmin_p\tn_probes\tz_p\tz_sidak_p\n")
# use -2 for original, uncorrected p-values in slk.bed
for region_line, slk_p, slk_sidak_p, sim_p in region_p.region_p(
prefix + ".slk.bed.gz",
prefix + ".regions.bed.gz", -2,
step):
fh.write("%s\t%.4g\t%.4g\n" % (region_line, slk_p, slk_sidak_p))
fh.flush()
N += int(slk_sidak_p < 0.05)
print("wrote: %s, (regions with corrected-p < 0.05: %i)" \
% (fh.name, N), file=sys.stderr)
regions_bed = fh.name
#if all(h in header for h in ('t', 'start', 'end')):
if region_filter_n is None: region_filter_n = 0
with ts.nopen(prefix + ".regions-t.bed", "w") as fh:
N = 0
for i, toks in enumerate(filter.filter(bed_files[0],
regions_bed, p_col_name=col_num)):
if i == 0: toks[0] = "#" + toks[0]
else:
if float(toks[6]) > region_filter_p: continue
if int(toks[4]) < region_filter_n: continue
#if region_filter_t and "/" in toks[7]:
# # t-pos/t-neg. if the lower one is > region_filter_t?
# vals = map(int, toks[7].split("/"))
# if min(vals) > region_filter_t: continue
N += 1
print("\t".join(toks), file=sys.stderr)
print(("wrote: %s, (regions with region-p "
"< %.3f and n-probes >= %i: %i)") \
% (fh.name, region_filter_p, region_filter_n, N),
file=sys.stderr)
try:
from cpv import manhattan
regions = manhattan.read_regions(fh.name)
manhattan.manhattan(prefix + ".slk.bed.gz", 3, prefix.rstrip(".") + ".manhattan.png",
False, ['#959899', '#484B4C'], "", False, None,
regions=regions, bonferonni=False)
except ImportError:
pass # they dont have matplotlib
if db is not None:
from cruzdb import Genome
g = Genome(db)
lastf = fh.name
with open(prefix + ".anno.%s.bed" % db, "w") as fh:
fh.write('#')
g.annotate(lastf, ("refGene", "cpgIslandExt"), out=fh,
feature_strand=True, parallel=len(spvals) > 500)
print("wrote: %s annotated with %s" % (fh.name, db), file=sys.stderr)
def pipeline(col_num, step, dist, prefix, threshold, seed, bed_files, mlog=False,
region_filter_p=1, region_filter_n=1):
sys.path.insert(0, op.join(op.dirname(__file__), ".."))
from cpv import acf, slk, fdr, peaks, region_p, stepsize, filter
import operator
if step is None:
step = stepsize.stepsize(bed_files, col_num)
print >>sys.stderr, "calculated stepsize as: %i" % step
lags = range(1, dist, step)
lags.append(lags[-1] + step)
prefix = prefix.rstrip(".")
# go out to max requested distance but stop once an autocorrelation
# < 0.05 is added.
putative_acf_vals = acf.acf(bed_files, lags, col_num, simple=False,
mlog=mlog)
acf_vals = []
for a in putative_acf_vals:
# a is ((lmin, lmax), (corr, N))
# this heuristic seems to work. stop just above the 0.08 correlation
# lag.
if a[1][0] < 0.04 and len(acf_vals) > 2: break
acf_vals.append(a)
if a[1][0] < 0.04 and len(acf_vals): break
# save the arguments that this was called with.
with open(prefix + ".args.txt", "w") as fh:
print >>fh, " ".join(sys.argv[1:]) + "\n"
import datetime
print >>fh, "date: %s" % datetime.datetime.today()
with open(prefix + ".acf.txt", "w") as fh:
acf_vals = acf.write_acf(acf_vals, fh)
print >>sys.stderr, "wrote: %s" % fh.name
print >>sys.stderr, "ACF:\n", open(prefix + ".acf.txt").read()
with open(prefix + ".slk.bed", "w") as fh:
for row in slk.adjust_pvals(bed_files, col_num, acf_vals):
fh.write("%s\t%i\t%i\t%.4g\t%.4g\n" % row)
print >>sys.stderr, "wrote: %s" % fh.name
with open(prefix + ".fdr.bed", "w") as fh:
for bh, l in fdr.fdr(prefix + ".slk.bed", -1):
fh.write("%s\t%.4g\n" % (l.rstrip("\r\n"), bh))
print >>sys.stderr, "wrote: %s" % fh.name
fregions = prefix + ".regions.bed"
with open(fregions, "w") as fh:
peaks.peaks(prefix + ".fdr.bed", -1, threshold, seed,
step, fh, operator.le)
n_regions = sum(1 for _ in open(fregions))
print >>sys.stderr, "wrote: %s (%i regions)" % (fregions, n_regions)
with open(prefix + ".regions-p.bed", "w") as fh:
N = 0
fh.write("#chrom\tstart\tend\tmin_p\tn_probes\tslk_p\tslk_sidak_p\n")
# use -2 for original, uncorrected p-values in slk.bed
for region_line, slk_p, slk_sidak_p, sim_p in region_p.region_p(
prefix + ".slk.bed",
prefix + ".regions.bed", -2,
0, step, mlog=mlog):
fh.write("%s\t%.4g\t%.4g\n" % (region_line, slk_p, slk_sidak_p))
fh.flush()
N += int(slk_sidak_p < 0.05)
print >>sys.stderr, "wrote: %s, (regions with corrected-p < 0.05: %i)" \
% (fh.name, N)
regions_bed = fh.name
with open(prefix + ".regions-t.bed", "w") as fh:
N = 0
for i, toks in enumerate(filter.filter(bed_files[0], regions_bed)):
if i == 0: toks[0] = "#" + toks[0]
else:
if float(toks[6]) > region_filter_p: continue
if int(toks[4]) < region_filter_n: continue
N += 1
print >>fh, "\t".join(toks)
print >>sys.stderr, "wrote: %s, (regions with region-p < %.3f and n-probes >= %i: %i)" \
% (fh.name, region_filter_p, region_filter_n, N)
def pipeline(col_num,
step,
dist,
acf_dist,
prefix,
threshold,
seed,
bed_files,
mlog=True,
region_filter_p=1,
region_filter_n=None,
genome_control=False,
db=None,
use_fdr=True):
sys.path.insert(0, op.join(op.dirname(__file__), ".."))
from cpv import acf, slk, fdr, peaks, region_p, stepsize, filter
from cpv._common import genome_control_adjust, genomic_control, bediter
import operator
if step is None:
step = min(acf_dist, stepsize.stepsize(bed_files, col_num))
print >> sys.stderr, "calculated stepsize as: %i" % step
lags = range(1, acf_dist, step)
lags.append(lags[-1] + step)
prefix = prefix.rstrip(".")
putative_acf_vals = acf.acf(bed_files,
lags,
col_num,
simple=False,
mlog=mlog)
acf_vals = []
# go out to max requested distance but stop once an autocorrelation
# < 0.05 is added.
for a in putative_acf_vals:
# a is ((lmin, lmax), (corr, N))
# this heuristic seems to work. stop just above the 0.08 correlation
# lag.
if a[1][0] < 0.04 and len(acf_vals) > 2: break
acf_vals.append(a)
if a[1][0] < 0.04 and len(acf_vals): break
# save the arguments that this was called with.
with open(prefix + ".args.txt", "w") as fh:
print >> fh, " ".join(sys.argv[1:]) + "\n"
import datetime
print >> fh, "date: %s" % datetime.datetime.today()
from .__init__ import __version__
print >> fh, "version:", __version__
with open(prefix + ".acf.txt", "w") as fh:
acf_vals = acf.write_acf(acf_vals, fh)
print >> sys.stderr, "wrote: %s" % fh.name
print >> sys.stderr, "ACF:\n", open(prefix + ".acf.txt").read()
spvals, opvals = [], []
with ts.nopen(prefix + ".slk.bed.gz", "w") as fhslk:
fhslk.write('#chrom\tstart\tend\tp\tregion-p\n')
for row in slk.adjust_pvals(bed_files, col_num, acf_vals):
fhslk.write("%s\t%i\t%i\t%.4g\t%.4g\n" % row)
opvals.append(row[-2])
spvals.append(row[-1])
print >> sys.stderr, "# original lambda: %.2f" % genomic_control(opvals)
del opvals
gc_lambda = genomic_control(spvals)
print >> sys.stderr, "wrote: %s with lambda: %.2f" % (fhslk.name,
gc_lambda)
if genome_control:
fhslk = ts.nopen(prefix + ".slk.gc.bed.gz", "w")
adj = genome_control_adjust(
[d['p'] for d in bediter(prefix + ".slk.bed.gz", -1)])
for i, line in enumerate(ts.nopen(prefix + ".slk.bed.gz")):
print >> fhslk, "%s\t%.5g" % (line.rstrip("\r\n"), adj[i])
fhslk.close()
print >> sys.stderr, "wrote: %s" % fhslk.name
with ts.nopen(prefix + ".fdr.bed.gz", "w") as fh:
fh.write('#chrom\tstart\tend\tp\tregion-p\tregion-q\n')
for bh, l in fdr.fdr(fhslk.name, -1):
fh.write("%s\t%.4g\n" % (l.rstrip("\r\n"), bh))
print >> sys.stderr, "wrote: %s" % fh.name
fregions = prefix + ".regions.bed.gz"
with ts.nopen(fregions, "w") as fh:
list(
peaks.peaks(prefix + ".fdr.bed.gz", -1 if use_fdr else -2,
threshold, seed, dist, fh, operator.le))
n_regions = sum(1 for _ in ts.nopen(fregions))
print >> sys.stderr, "wrote: %s (%i regions)" % (fregions, n_regions)
if n_regions == 0:
sys.exit()
with ts.nopen(prefix + ".regions-p.bed.gz", "w") as fh:
N = 0
fh.write("#chrom\tstart\tend\tmin_p\tn_probes\tz_p\tz_sidak_p\n")
# use -2 for original, uncorrected p-values in slk.bed
for region_line, slk_p, slk_sidak_p, sim_p in region_p.region_p(
prefix + ".slk.bed.gz", prefix + ".regions.bed.gz", -2, step):
fh.write("%s\t%.4g\t%.4g\n" % (region_line, slk_p, slk_sidak_p))
fh.flush()
N += int(slk_sidak_p < 0.05)
print >>sys.stderr, "wrote: %s, (regions with corrected-p < 0.05: %i)" \
% (fh.name, N)
regions_bed = fh.name
header = ts.header(bed_files[0])
#if all(h in header for h in ('t', 'start', 'end')):
if region_filter_n is None: region_filter_n = 0
with ts.nopen(prefix + ".regions-t.bed", "w") as fh:
N = 0
for i, toks in enumerate(
filter.filter(bed_files[0], regions_bed, p_col_name=col_num)):
if i == 0: toks[0] = "#" + toks[0]
else:
if float(toks[6]) > region_filter_p: continue
if int(toks[4]) < region_filter_n: continue
#if region_filter_t and "/" in toks[7]:
# # t-pos/t-neg. if the lower one is > region_filter_t?
# vals = map(int, toks[7].split("/"))
# if min(vals) > region_filter_t: continue
N += 1
print >> fh, "\t".join(toks)
print >>sys.stderr, ("wrote: %s, (regions with region-p "
"< %.3f and n-probes >= %i: %i)") \
% (fh.name, region_filter_p, region_filter_n, N)
try:
from cpv import manhattan
regions = manhattan.read_regions(fh.name)
manhattan.manhattan(prefix + ".slk.bed.gz",
3,
prefix.rstrip(".") + ".manhattan.png",
False, ['#959899', '#484B4C'],
"",
False,
None,
regions=regions,
bonferonni=False)
except ImportError:
pass # they dont have matplotlib
if db is not None:
from cruzdb import Genome
g = Genome(db)
lastf = fh.name
with open(prefix + ".anno.%s.bed" % db, "w") as fh:
fh.write('#')
g.annotate(lastf, ("refGene", "cpgIslandExt"),
out=fh,
feature_strand=True,
parallel=len(spvals) > 500)
print >> sys.stderr, "wrote: %s annotated with %s" % (fh.name, db)
def pipeline(col_num, step, dist, prefix, threshold, seed, bed_files, mlog=False,
region_filter_p=1, region_filter_n=1, genome_control=False, db=None):
sys.path.insert(0, op.join(op.dirname(__file__), ".."))
from cpv import acf, slk, fdr, peaks, region_p, stepsize, filter
from cpv._common import genome_control_adjust, genomic_control, bediter
import operator
if step is None:
step = stepsize.stepsize(bed_files, col_num)
print >>sys.stderr, "calculated stepsize as: %i" % step
lags = range(1, dist, step)
lags.append(lags[-1] + step)
prefix = prefix.rstrip(".")
#if genome_control:
# with open(prefix + ".adj.bed", "w") as fh:
# genome_control_adjust_bed(bed_files, col_num, fh)
# bed_files = [fh.name]
putative_acf_vals = acf.acf(bed_files, lags, col_num, simple=False,
mlog=mlog)
acf_vals = []
# go out to max requested distance but stop once an autocorrelation
# < 0.05 is added.
for a in putative_acf_vals:
# a is ((lmin, lmax), (corr, N))
# this heuristic seems to work. stop just above the 0.08 correlation
# lag.
if a[1][0] < 0.04 and len(acf_vals) > 2: break
acf_vals.append(a)
if a[1][0] < 0.04 and len(acf_vals): break
# save the arguments that this was called with.
with open(prefix + ".args.txt", "w") as fh:
print >>fh, " ".join(sys.argv[1:]) + "\n"
import datetime
print >>fh, "date: %s" % datetime.datetime.today()
with open(prefix + ".acf.txt", "w") as fh:
acf_vals = acf.write_acf(acf_vals, fh)
print >>sys.stderr, "wrote: %s" % fh.name
print >>sys.stderr, "ACF:\n", open(prefix + ".acf.txt").read()
spvals, opvals = [], []
with open(prefix + ".slk.bed", "w") as fhslk:
for row in slk.adjust_pvals(bed_files, col_num, acf_vals):
fhslk.write("%s\t%i\t%i\t%.4g\t%.4g\n" % row)
opvals.append(row[-2])
spvals.append(row[-1])
print >>sys.stderr, "# original lambda: %.2f" % genomic_control(opvals)
del opvals
gc_lambda = genomic_control(spvals)
print >>sys.stderr, "wrote: %s with lambda: %.2f" % (fhslk.name, gc_lambda)
if genome_control:
fhslk = open(prefix + ".slk.gc.bed", "w")
adj = genome_control_adjust([d['p'] for d in bediter(prefix + ".slk.bed", -1)])
for i, line in enumerate(open(prefix + ".slk.bed")):
print >>fhslk, "%s\t%.5g" % (line.rstrip("\r\n"), adj[i])
fhslk.close()
print >>sys.stderr, "wrote: %s" % fhslk.name
with open(prefix + ".fdr.bed", "w") as fh:
for bh, l in fdr.fdr(fhslk.name, -1):
fh.write("%s\t%.4g\n" % (l.rstrip("\r\n"), bh))
print >>sys.stderr, "wrote: %s" % fh.name
fregions = prefix + ".regions.bed"
with open(fregions, "w") as fh:
list(peaks.peaks(prefix + ".fdr.bed", -1, threshold, seed,
step, fh, operator.le))
n_regions = sum(1 for _ in open(fregions))
print >>sys.stderr, "wrote: %s (%i regions)" % (fregions, n_regions)
with open(prefix + ".regions-p.bed", "w") as fh:
N = 0
fh.write("#chrom\tstart\tend\tmin_p\tn_probes\tslk_p\tslk_sidak_p\n")
# use -2 for original, uncorrected p-values in slk.bed
for region_line, slk_p, slk_sidak_p, sim_p in region_p.region_p(
prefix + ".slk.bed",
prefix + ".regions.bed", -2,
0, step, mlog=mlog):
fh.write("%s\t%.4g\t%.4g\n" % (region_line, slk_p, slk_sidak_p))
fh.flush()
N += int(slk_sidak_p < 0.05)
print >>sys.stderr, "wrote: %s, (regions with corrected-p < 0.05: %i)" \
% (fh.name, N)
regions_bed = fh.name
header = (gzip.open(bed_files[0]) if bed_files[0].endswith(".gz")
else open(bed_files[0])).next().split("\t")
if all(h in header for h in ('t', 'start', 'end')):
with open(prefix + ".regions-t.bed", "w") as fh:
N = 0
for i, toks in enumerate(filter.filter(bed_files[0], regions_bed,
p_col_name=col_num)):
if i == 0: toks[0] = "#" + toks[0]
else:
if float(toks[6]) > region_filter_p: continue
if int(toks[4]) < region_filter_n: continue
N += 1
print >>fh, "\t".join(toks)
print >>sys.stderr, ("wrote: %s, (regions with region-p"
"< %.3f and n-probes >= %i: %i)") \
% (fh.name, region_filter_p, region_filter_n, N)
try:
from cpv import manhattan
regions = manhattan.read_regions(fh.name)
manhattan.manhattan(prefix + ".slk.bed", 3, prefix.rstrip(".") + ".manhattan.png",
False, ['#959899', '#484B4C'], "", False, None,
regions=regions, bonferonni=True)
except ImportError:
pass # they dont have matplotlib
if db is not None:
from cruzdb import Genome
g = Genome(db)
lastf = fh.name
with open(prefix + ".anno.%s.bed" % db, "w") as fh:
g.annotate(lastf, ("refGene", "cpgIslandExt", "cytoBand"), out=fh,
feature_strand=True, parallel=len(spvals) > 500)
print >>sys.stderr, "wrote: %s annotated with %s" % (fh.name, db)