def ld(args):
"""
%prog ld map
Calculate pairwise linkage disequilibrium given MSTmap.
"""
import numpy as np
from random import sample
from jcvi.algorithms.matrix import symmetrize
p = OptionParser(ld.__doc__)
p.add_option("--subsample", default=1000, type="int",
help="Subsample markers to speed up [default: %default]")
opts, args, iopts = p.set_image_options(args, figsize="8x8")
if len(args) != 1:
sys.exit(not p.print_help())
mstmap, = args
subsample = opts.subsample
data = MSTMap(mstmap)
markerbedfile = mstmap + ".subsample.bed"
ldmatrix = mstmap + ".subsample.matrix"
# Take random subsample while keeping marker order
if subsample < data.nmarkers:
data = [data[x] for x in \
sorted(sample(xrange(len(data)), subsample))]
else:
logging.debug("Use all markers, --subsample ignored")
nmarkers = len(data)
if need_update(mstmap, (ldmatrix, markerbedfile)):
fw = open(markerbedfile, "w")
print("\n".join(x.bedline for x in data), file=fw)
logging.debug("Write marker set of size {0} to file `{1}`."\
.format(nmarkers, markerbedfile))
fw.close()
M = np.zeros((nmarkers, nmarkers), dtype=float)
for i, j in combinations(range(nmarkers), 2):
a = data[i]
b = data[j]
M[i, j] = calc_ldscore(a.genotype, b.genotype)
M = symmetrize(M)
logging.debug("Write LD matrix to file `{0}`.".format(ldmatrix))
M.tofile(ldmatrix)
else:
nmarkers = len(Bed(markerbedfile))
M = np.fromfile(ldmatrix, dtype="float").reshape(nmarkers, nmarkers)
logging.debug("LD matrix `{0}` exists ({1}x{1})."\
.format(ldmatrix, nmarkers))
from jcvi.graphics.base import plt, savefig, Rectangle, draw_cmap
plt.rcParams["axes.linewidth"] = 0
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1])
ax = fig.add_axes([.1, .1, .8, .8]) # the heatmap
ax.matshow(M, cmap=iopts.cmap)
# Plot chromosomes breaks
bed = Bed(markerbedfile)
xsize = len(bed)
extent = (0, nmarkers)
chr_labels = []
ignore_size = 20
for (seqid, beg, end) in bed.get_breaks():
ignore = abs(end - beg) < ignore_size
pos = (beg + end) / 2
chr_labels.append((seqid, pos, ignore))
if ignore:
continue
ax.plot((end, end), extent, "w-", lw=1)
ax.plot(extent, (end, end), "w-", lw=1)
# Plot chromosome labels
for label, pos, ignore in chr_labels:
pos = .1 + pos * .8 / xsize
if not ignore:
root.text(pos, .91, label,
ha="center", va="bottom", rotation=45, color="grey")
root.text(.09, pos, label,
ha="right", va="center", color="grey")
ax.set_xlim(extent)
ax.set_ylim(extent)
ax.set_axis_off()
draw_cmap(root, "Pairwise LD (r2)", 0, 1, cmap=iopts.cmap)
root.add_patch(Rectangle((.1, .1), .8, .8, fill=False, ec="k", lw=2))
m = mstmap.split(".")[0]
root.text(.5, .06, "Linkage Disequilibrium between {0} markers".format(m), ha="center")
root.set_xlim(0, 1)
root.set_ylim(0, 1)
root.set_axis_off()
image_name = m + ".subsample" + "." + iopts.format
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
def ld(args):
"""
%prog ld map
Calculate pairwise linkage disequilibrium given MSTmap.
"""
import numpy as np
from random import sample
from jcvi.algorithms.matrix import symmetrize
p = OptionParser(ld.__doc__)
p.add_option("--subsample",
default=1000,
type="int",
help="Subsample markers to speed up [default: %default]")
opts, args, iopts = p.set_image_options(args, figsize="8x8")
if len(args) != 1:
sys.exit(not p.print_help())
mstmap, = args
subsample = opts.subsample
data = MSTMap(mstmap)
markerbedfile = mstmap + ".subsample.bed"
ldmatrix = mstmap + ".subsample.matrix"
# Take random subsample while keeping marker order
if subsample < data.nmarkers:
data = [data[x] for x in \
sorted(sample(xrange(len(data)), subsample))]
else:
logging.debug("Use all markers, --subsample ignored")
nmarkers = len(data)
if need_update(mstmap, (ldmatrix, markerbedfile)):
fw = open(markerbedfile, "w")
print("\n".join(x.bedline for x in data), file=fw)
logging.debug("Write marker set of size {0} to file `{1}`."\
.format(nmarkers, markerbedfile))
fw.close()
M = np.zeros((nmarkers, nmarkers), dtype=float)
for i, j in combinations(range(nmarkers), 2):
a = data[i]
b = data[j]
M[i, j] = calc_ldscore(a.genotype, b.genotype)
M = symmetrize(M)
logging.debug("Write LD matrix to file `{0}`.".format(ldmatrix))
M.tofile(ldmatrix)
else:
nmarkers = len(Bed(markerbedfile))
M = np.fromfile(ldmatrix, dtype="float").reshape(nmarkers, nmarkers)
logging.debug("LD matrix `{0}` exists ({1}x{1})."\
.format(ldmatrix, nmarkers))
from jcvi.graphics.base import plt, savefig, Rectangle, draw_cmap
plt.rcParams["axes.linewidth"] = 0
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1])
ax = fig.add_axes([.1, .1, .8, .8]) # the heatmap
ax.matshow(M, cmap=iopts.cmap)
# Plot chromosomes breaks
bed = Bed(markerbedfile)
xsize = len(bed)
extent = (0, nmarkers)
chr_labels = []
ignore_size = 20
for (seqid, beg, end) in bed.get_breaks():
ignore = abs(end - beg) < ignore_size
pos = (beg + end) / 2
chr_labels.append((seqid, pos, ignore))
if ignore:
continue
ax.plot((end, end), extent, "w-", lw=1)
ax.plot(extent, (end, end), "w-", lw=1)
# Plot chromosome labels
for label, pos, ignore in chr_labels:
pos = .1 + pos * .8 / xsize
if not ignore:
root.text(pos,
.91,
label,
ha="center",
va="bottom",
rotation=45,
color="grey")
root.text(.09, pos, label, ha="right", va="center", color="grey")
ax.set_xlim(extent)
ax.set_ylim(extent)
ax.set_axis_off()
draw_cmap(root, "Pairwise LD (r2)", 0, 1, cmap=iopts.cmap)
root.add_patch(Rectangle((.1, .1), .8, .8, fill=False, ec="k", lw=2))
m = mstmap.split(".")[0]
root.text(.5,
.06,
"Linkage Disequilibrium between {0} markers".format(m),
ha="center")
root.set_xlim(0, 1)
root.set_ylim(0, 1)
root.set_axis_off()
image_name = m + ".subsample" + "." + iopts.format
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
def dotplot(args):
"""
%prog dotplot map.csv ref.fasta
Make dotplot between chromosomes and linkage maps.
The input map is csv formatted, for example:
ScaffoldID,ScaffoldPosition,LinkageGroup,GeneticPosition
scaffold_2707,11508,1,0
scaffold_2707,11525,1,1.2
"""
from jcvi.assembly.allmaps import CSVMapLine
from jcvi.formats.sizes import Sizes
from jcvi.utils.natsort import natsorted
from jcvi.graphics.base import shorten
from jcvi.graphics.dotplot import plt, savefig, markup, normalize_axes, \
downsample, plot_breaks_and_labels, thousands
p = OptionParser(dotplot.__doc__)
p.set_outfile(outfile=None)
opts, args, iopts = p.set_image_options(args, figsize="8x8",
style="dark", dpi=90, cmap="copper")
if len(args) != 2:
sys.exit(not p.print_help())
csvfile, fastafile = args
sizes = natsorted(Sizes(fastafile).mapping.items())
seen = set()
raw_data = []
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1]) # the whole canvas
ax = fig.add_axes([.1, .1, .8, .8]) # the dot plot
fp = must_open(csvfile)
for row in fp:
m = CSVMapLine(row)
seen.add(m.seqid)
raw_data.append(m)
# X-axis is the genome assembly
ctgs, ctg_sizes = zip(*sizes)
xsize = sum(ctg_sizes)
qb = list(np.cumsum(ctg_sizes))
qbreaks = list(zip(ctgs, [0] + qb, qb))
qstarts = dict(zip(ctgs, [0] + qb))
# Y-axis is the map
key = lambda x: x.lg
raw_data.sort(key=key)
ssizes = {}
for lg, d in groupby(raw_data, key=key):
ssizes[lg] = max([x.cm for x in d])
ssizes = natsorted(ssizes.items())
lgs, lg_sizes = zip(*ssizes)
ysize = sum(lg_sizes)
sb = list(np.cumsum(lg_sizes))
sbreaks = list(zip([("LG" + x) for x in lgs], [0] + sb, sb))
sstarts = dict(zip(lgs, [0] + sb))
# Re-code all the scatter dots
data = [(qstarts[x.seqid] + x.pos, sstarts[x.lg] + x.cm, 'g') \
for x in raw_data if (x.seqid in qstarts)]
npairs = downsample(data)
x, y, c = zip(*data)
ax.scatter(x, y, c=c, edgecolors="none", s=2, lw=0)
# Flip X-Y label
gy, gx = op.basename(csvfile).split(".")[:2]
gx, gy = shorten(gx, maxchar=30), shorten(gy, maxchar=30)
xlim, ylim = plot_breaks_and_labels(fig, root, ax, gx, gy,
xsize, ysize, qbreaks, sbreaks)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
title = "Alignment: {} vs {}".format(gx, gy)
title += " ({} markers)".format(thousands(npairs))
root.set_title(markup(title), x=.5, y=.96, color="k")
logging.debug(title)
normalize_axes(root)
image_name = opts.outfile or \
(csvfile.rsplit(".", 1)[0] + "." + iopts.format)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
fig.clear()
def dotplot(args):
"""
%prog dotplot map.csv ref.fasta
Make dotplot between chromosomes and linkage maps.
The input map is csv formatted, for example:
ScaffoldID,ScaffoldPosition,LinkageGroup,GeneticPosition
scaffold_2707,11508,1,0
scaffold_2707,11525,1,1.2
"""
from jcvi.assembly.allmaps import CSVMapLine
from jcvi.formats.sizes import Sizes
from jcvi.utils.natsort import natsorted
from jcvi.graphics.base import shorten
from jcvi.graphics.dotplot import plt, savefig, markup, normalize_axes, \
downsample, plot_breaks_and_labels, thousands
p = OptionParser(dotplot.__doc__)
p.set_outfile(outfile=None)
opts, args, iopts = p.set_image_options(args,
figsize="8x8",
style="dark",
dpi=90,
cmap="copper")
if len(args) != 2:
sys.exit(not p.print_help())
csvfile, fastafile = args
sizes = natsorted(Sizes(fastafile).mapping.items())
seen = set()
raw_data = []
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1]) # the whole canvas
ax = fig.add_axes([.1, .1, .8, .8]) # the dot plot
fp = must_open(csvfile)
for row in fp:
m = CSVMapLine(row)
seen.add(m.seqid)
raw_data.append(m)
# X-axis is the genome assembly
ctgs, ctg_sizes = zip(*sizes)
xsize = sum(ctg_sizes)
qb = list(np.cumsum(ctg_sizes))
qbreaks = list(zip(ctgs, [0] + qb, qb))
qstarts = dict(zip(ctgs, [0] + qb))
# Y-axis is the map
key = lambda x: x.lg
raw_data.sort(key=key)
ssizes = {}
for lg, d in groupby(raw_data, key=key):
ssizes[lg] = max([x.cm for x in d])
ssizes = natsorted(ssizes.items())
lgs, lg_sizes = zip(*ssizes)
ysize = sum(lg_sizes)
sb = list(np.cumsum(lg_sizes))
sbreaks = list(zip([("LG" + x) for x in lgs], [0] + sb, sb))
sstarts = dict(zip(lgs, [0] + sb))
# Re-code all the scatter dots
data = [(qstarts[x.seqid] + x.pos, sstarts[x.lg] + x.cm, 'g') \
for x in raw_data if (x.seqid in qstarts)]
npairs = downsample(data)
x, y, c = zip(*data)
ax.scatter(x, y, c=c, edgecolors="none", s=2, lw=0)
# Flip X-Y label
gy, gx = op.basename(csvfile).split(".")[:2]
gx, gy = shorten(gx, maxchar=30), shorten(gy, maxchar=30)
xlim, ylim = plot_breaks_and_labels(fig, root, ax, gx, gy, xsize, ysize,
qbreaks, sbreaks)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
title = "Alignment: {} vs {}".format(gx, gy)
title += " ({} markers)".format(thousands(npairs))
root.set_title(markup(title), x=.5, y=.96, color="k")
logging.debug(title)
normalize_axes(root)
image_name = opts.outfile or \
(csvfile.rsplit(".", 1)[0] + "." + iopts.format)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
fig.clear()