def plot_GC3(GC3, cdsfile, fill="white"):
from jcvi.graphics.histogram import histogram
numberfile = "{0}.gc3".format(cdsfile)
fw = must_open(numberfile, "w")
fw.write("\n".join(map(str, GC3.values())))
fw.close()
histogram(numberfile, vmin=0, vmax=1, xlabel="GC3", title=cdsfile,
bins=50, skip=0, ascii=False, log=0, fill=fill)
logging.debug("{0} GC3 values plotted to {1}.pdf".\
format(len(GC3), numberfile))
def plot_GC3(GC3, cdsfile, fill="white"):
from jcvi.graphics.histogram import histogram
numberfile = "{0}.gc3".format(cdsfile)
fw = must_open(numberfile, "w")
fw.write("\n".join(map(str, GC3.values())))
fw.close()
histogram(numberfile, vmin=0, vmax=1, xlabel="GC3", title=cdsfile,
bins=50, skip=0, ascii=False, log=0, fill=fill)
logging.debug("{0} GC3 values plotted to {1}.pdf".\
format(len(GC3), numberfile))
def report_pairs(data,
cutoff=0,
mateorientation=None,
pairsfile=None,
insertsfile=None,
rclip=1,
ascii=False,
bins=20,
distmode="ss",
mpcutoff=1000):
"""
This subroutine is used by the pairs function in blast.py and cas.py.
Reports number of fragments and pairs as well as linked pairs
"""
allowed_mateorientations = ("++", "--", "+-", "-+")
if mateorientation:
assert mateorientation in allowed_mateorientations
num_fragments, num_pairs = 0, 0
all_dist = []
linked_dist = []
# +- (forward-backward) is `innie`, -+ (backward-forward) is `outie`
orientations = defaultdict(int)
# clip how many chars from end of the read name to get pair name
key = (lambda x: x.accn[:-rclip]) if rclip else (lambda x: x.accn)
data.sort(key=key)
if pairsfile:
pairsfw = open(pairsfile, "w")
if insertsfile:
insertsfw = open(insertsfile, "w")
for pe, lines in groupby(data, key=key):
lines = list(lines)
if len(lines) != 2:
num_fragments += len(lines)
continue
num_pairs += 1
a, b = lines
asubject, astart, astop = a.seqid, a.start, a.end
bsubject, bstart, bstop = b.seqid, b.start, b.end
aquery, bquery = a.accn, b.accn
astrand, bstrand = a.strand, b.strand
dist, orientation = range_distance(\
(asubject, astart, astop, astrand),
(bsubject, bstart, bstop, bstrand),
distmode=distmode)
if dist >= 0:
all_dist.append((dist, orientation, aquery, bquery))
# select only pairs with certain orientations - e.g. innies, outies, etc.
if mateorientation:
all_dist = [x for x in all_dist if x[1] == mateorientation]
# try to infer cutoff as twice the median until convergence
if cutoff <= 0:
dists = np.array([x[0] for x in all_dist], dtype="int")
p0 = analyze_dists(dists, cutoff=mpcutoff)
cutoff = int(2 * p0) # initial estimate
cutoff = int(math.ceil(cutoff / bins)) * bins
logging.debug("Insert size cutoff set to {0}, ".format(cutoff) +
"use '--cutoff' to override")
for dist, orientation, aquery, bquery in all_dist:
if dist > cutoff:
continue
if cutoff > 2 * mpcutoff and dist < mpcutoff:
continue
linked_dist.append(dist)
if pairsfile:
print >> pairsfw, "{0}\t{1}\t{2}".format(aquery, bquery, dist)
orientations[orientation] += 1
print >>sys.stderr, "{0} fragments, {1} pairs ({2} total)".\
format(num_fragments, num_pairs, num_fragments + num_pairs * 2)
s = SummaryStats(linked_dist, dtype="int")
num_links = s.size
meandist, stdev = s.mean, s.sd
p0, p1, p2 = s.median, s.p1, s.p2
print >>sys.stderr, "%d pairs (%.1f%%) are linked (cutoff=%d)" % \
(num_links, num_links * 100. / num_pairs, cutoff)
print >>sys.stderr, "mean distance between mates: {0} +/- {1}".\
format(meandist, stdev)
print >> sys.stderr, "median distance between mates: {0}".format(p0)
print >> sys.stderr, "95% distance range: {0} - {1}".format(p1, p2)
print >> sys.stderr, "\nOrientations:"
orientation_summary = []
for orientation, count in sorted(orientations.items()):
o = "{0}:{1}".format(orientation, \
percentage(count, num_links, mode=1))
orientation_summary.append(o.split()[0])
print >> sys.stderr, o
if insertsfile:
from jcvi.graphics.histogram import histogram
print >> insertsfw, "\n".join(str(x) for x in linked_dist)
insertsfw.close()
prefix = insertsfile.rsplit(".", 1)[0]
if prefix > 10:
prefix = prefix.split("-")[0]
osummary = " ".join(orientation_summary)
title = "{0} ({1}; median:{2} bp)".format(prefix, osummary, p0)
histogram(insertsfile,
vmin=0,
vmax=cutoff,
bins=bins,
xlabel="Insertsize",
title=title,
ascii=ascii)
if op.exists(insertsfile):
os.remove(insertsfile)
return s
def report_pairs(data, cutoff=0, mateorientation=None,
pairsfile=None, insertsfile=None, rclip=1, ascii=False, bins=20,
distmode="ss"):
"""
This subroutine is used by the pairs function in blast.py and cas.py.
Reports number of fragments and pairs as well as linked pairs
"""
from jcvi.utils.cbook import percentage
allowed_mateorientations = ("++", "--", "+-", "-+")
if mateorientation:
assert mateorientation in allowed_mateorientations
num_fragments, num_pairs = 0, 0
all_dist = []
linked_dist = []
# +- (forward-backward) is `innie`, -+ (backward-forward) is `outie`
orientations = defaultdict(int)
# clip how many chars from end of the read name to get pair name
key = (lambda x: x.accn[:-rclip]) if rclip else (lambda x: x.accn)
data.sort(key=key)
if pairsfile:
pairsfw = open(pairsfile, "w")
if insertsfile:
insertsfw = open(insertsfile, "w")
for pe, lines in groupby(data, key=key):
lines = list(lines)
if len(lines) != 2:
num_fragments += len(lines)
continue
num_pairs += 1
a, b = lines
asubject, astart, astop = a.seqid, a.start, a.end
bsubject, bstart, bstop = b.seqid, b.start, b.end
aquery, bquery = a.accn, b.accn
astrand, bstrand = a.strand, b.strand
dist, orientation = range_distance(\
(asubject, astart, astop, astrand),
(bsubject, bstart, bstop, bstrand),
distmode=distmode)
if dist >= 0:
all_dist.append((dist, orientation, aquery, bquery))
# select only pairs with certain orientations - e.g. innies, outies, etc.
if mateorientation:
all_dist = [x for x in all_dist if x[1] == mateorientation]
# try to infer cutoff as twice the median until convergence
if cutoff <= 0:
dists = np.array([x[0] for x in all_dist], dtype="int")
p0 = np.median(dists)
cutoff = int(2 * p0) # initial estimate
cutoff = int(math.ceil(cutoff / bins)) * bins
logging.debug("Insert size cutoff set to {0}, ".format(cutoff) +
"use '--cutoff' to override")
for dist, orientation, aquery, bquery in all_dist:
if dist > cutoff:
continue
linked_dist.append(dist)
if pairsfile:
print >> pairsfw, "{0}\t{1}\t{2}".format(aquery, bquery, dist)
orientations[orientation] += 1
print >>sys.stderr, "%d fragments, %d pairs" % (num_fragments, num_pairs)
num_links = len(linked_dist)
linked_dist = np.array(linked_dist, dtype="int")
linked_dist = np.sort(linked_dist)
meandist = np.mean(linked_dist)
stdev = np.std(linked_dist)
p0 = np.median(linked_dist)
p1 = linked_dist[int(num_links * .025)]
p2 = linked_dist[int(num_links * .975)]
meandist, stdev = int(meandist), int(stdev)
p0 = int(p0)
print >>sys.stderr, "%d pairs (%.1f%%) are linked (cutoff=%d)" % \
(num_links, num_links * 100. / num_pairs, cutoff)
print >>sys.stderr, "mean distance between mates: {0} +/- {1}".\
format(meandist, stdev)
print >>sys.stderr, "median distance between mates: {0}".format(p0)
print >>sys.stderr, "95% distance range: {0} - {1}".format(p1, p2)
print >>sys.stderr, "\nOrientations:"
orientation_summary = []
for orientation, count in sorted(orientations.items()):
o = "{0}:{1}".format(orientation, \
percentage(count, num_links, denominator=False))
orientation_summary.append(o.split()[0])
print >>sys.stderr, o
if insertsfile:
from jcvi.graphics.histogram import histogram
print >>insertsfw, "\n".join(str(x) for x in linked_dist)
insertsfw.close()
prefix = insertsfile.rsplit(".", 1)[0]
osummary = " ".join(orientation_summary)
title="{0} ({1}; median dist:{2})".format(prefix, osummary, p0)
histogram(insertsfile, vmin=0, vmax=cutoff, bins=bins,
xlabel="Insertsize", title=title, ascii=ascii)
if op.exists(insertsfile):
os.remove(insertsfile)
return meandist, stdev, p0, p1, p2
