def plot(args):
"""
%prog plot input.bed seqid
Plot the matchings between the reconstructed pseudomolecules and the maps.
Two types of visualizations are available in one canvas:
1. Parallel axes, and matching markers are shown in connecting lines;
2. Scatter plot.
"""
from jcvi.graphics.base import plt, savefig, normalize_axes, \
set2, panel_labels
from jcvi.graphics.chromosome import Chromosome, GeneticMap, \
HorizontalChromosome
p = OptionParser(plot.__doc__)
add_allmaps_plot_options(p)
opts, args, iopts = p.set_image_options(args, figsize="10x6")
if len(args) != 2:
sys.exit(not p.print_help())
inputbed, seqid = args
pf = inputbed.rsplit(".", 1)[0]
bedfile = pf + ".lifted.bed"
agpfile = pf + ".agp"
weightsfile = opts.weightsfile
links = opts.links
function = get_function(opts.distance)
cc = Map(bedfile, function)
allseqids = cc.seqids
mapnames = cc.mapnames
weights = Weights(weightsfile, mapnames)
assert seqid in allseqids, "{0} not in {1}".format(seqid, allseqids)
s = Scaffold(seqid, cc)
mlgs = [k for k, v in s.mlg_counts.items() if v >= links]
mlgsizes = {}
for mlg in mlgs:
mm = cc.extract_mlg(mlg)
mlgsize = max(function(x) for x in mm)
mlgsizes[mlg] = mlgsize
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes([0, 0, 1, 1])
ax1 = fig.add_axes([0, 0, .5, 1])
ax2 = fig.add_axes([.5, 0, .5, 1])
# Find the layout first
ystart, ystop = .9, .1
L = Layout(mlgsizes)
coords = L.coords
tip = .02
marker_pos = {}
# Palette
colors = dict((mapname, set2[i]) for i, mapname in enumerate(mapnames))
colors = dict((mlg, colors[mlg.split("-")[0]]) for mlg in mlgs)
rhos = {}
# Parallel coordinates
for mlg, (x, y1, y2) in coords.items():
mm = cc.extract_mlg(mlg)
markers = [(m.accn, function(m)) for m in mm] # exhaustive marker list
xy = [(m.pos, function(m)) for m in mm if m.seqid == seqid]
mx, my = zip(*xy)
rho = spearmanr(mx, my)
rhos[mlg] = rho
flip = rho < 0
g = GeneticMap(ax1, x, y1, y2, markers, tip=tip, flip=flip)
extra = -3 * tip if x < .5 else 3 * tip
ha = "right" if x < .5 else "left"
mapname = mlg.split("-")[0]
tlg = mlg.replace("_", ".") # Latex does not like underscore char
label = "{0} (w={1})".format(tlg, weights[mapname])
ax1.text(x + extra, (y1 + y2) / 2,
label,
color=colors[mlg],
ha=ha,
va="center",
rotation=90)
marker_pos.update(g.marker_pos)
agp = AGP(agpfile)
agp = [x for x in agp if x.object == seqid]
chrsize = max(x.object_end for x in agp)
# Pseudomolecules in the center
r = ystart - ystop
ratio = r / chrsize
f = lambda x: (ystart - ratio * x)
patchstart = [f(x.object_beg) for x in agp if not x.is_gap]
Chromosome(ax1, .5, ystart, ystop, width=2 * tip, patch=patchstart, lw=2)
label = "{0} ({1})".format(seqid, human_size(chrsize, precision=0))
ax1.text(.5, ystart + tip, label, ha="center")
scatter_data = defaultdict(list)
# Connecting lines
for b in s.markers:
marker_name = b.accn
if marker_name not in marker_pos:
continue
cx = .5
cy = f(b.pos)
mx = coords[b.mlg][0]
my = marker_pos[marker_name]
extra = -tip if mx < cx else tip
extra *= 1.25 # leave boundaries for aesthetic reasons
cx += extra
mx -= extra
ax1.plot((cx, mx), (cy, my), "-", color=colors[b.mlg])
scatter_data[b.mlg].append((b.pos, function(b)))
# Scatter plot, same data as parallel coordinates
xstart, xstop = sorted((ystart, ystop))
f = lambda x: (xstart + ratio * x)
pp = [x.object_beg for x in agp if not x.is_gap]
patchstart = [f(x) for x in pp]
HorizontalChromosome(ax2,
xstart,
xstop,
ystop,
height=2 * tip,
patch=patchstart,
lw=2)
gap = .03
ratio = (r - gap * len(mlgs) - tip) / sum(mlgsizes.values())
tlgs = []
for mlg, mlgsize in sorted(mlgsizes.items()):
height = ratio * mlgsize
ystart -= height
xx = .5 + xstart / 2
width = r / 2
color = colors[mlg]
ax = fig.add_axes([xx, ystart, width, height])
ypos = ystart + height / 2
ystart -= gap
sd = scatter_data[mlg]
xx, yy = zip(*sd)
ax.vlines(pp, 0, mlgsize, colors="beige")
ax.plot(xx, yy, ".", color=color)
rho = rhos[mlg]
ax.text(.5,
1 - .4 * gap / height,
r"$\rho$={0:.3f}".format(rho),
ha="center",
va="top",
transform=ax.transAxes,
color="gray")
tlg = mlg.replace("_", ".")
tlgs.append((tlg, ypos, color))
ax.set_xlim(0, chrsize)
ax.set_ylim(0, mlgsize)
ax.set_xticks([])
while height / len(ax.get_yticks()) < .03 and len(
ax.get_yticks()) >= 2:
ax.set_yticks(ax.get_yticks()[::2]) # Sparsify the ticks
yticklabels = [int(x) for x in ax.get_yticks()]
ax.set_yticklabels(yticklabels, family='Helvetica')
if rho < 0:
ax.invert_yaxis()
for i, (tlg, ypos, color) in enumerate(tlgs):
ha = "center"
if len(tlgs) > 4:
ha = "right" if i % 2 else "left"
root.text(.5, ypos, tlg, color=color, rotation=90, ha=ha, va="center")
if opts.panels:
labels = ((.04, .96, 'A'), (.48, .96, 'B'))
panel_labels(root, labels)
normalize_axes((ax1, ax2, root))
image_name = seqid + "." + iopts.format
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
plt.close(fig)
def ancestral(args):
"""
%prog ancestral ancestral.txt assembly.fasta
Karyotype evolution of pineapple. The figure is inspired by Amphioxus paper
Figure 3 and Tetradon paper Figure 9.
"""
p = OptionParser(ancestral.__doc__)
opts, args, iopts = p.set_image_options(args, figsize="8x7")
if len(args) != 2:
sys.exit(not p.print_help())
regionsfile, sizesfile = args
regions = RegionsFile(regionsfile)
sizes = Sizes(sizesfile).mapping
sizes = dict((k, v) for (k, v) in sizes.iteritems() if k[:2] == "LG")
maxsize = max(sizes.values())
ratio = .5 / maxsize
fig = plt.figure(1, (iopts.w, iopts.h))
root = fig.add_axes((0, 0, 1, 1))
from jcvi.graphics.base import set2
a, b, c, d, e, f, g = set2[:7]
set2 = (c, g, b, e, d, a, f)
# Upper panel is the evolution of segments
# All segments belong to one of seven karyotypes 1 to 7
karyotypes = regions.karyotypes
xgap = 1. / (1 + len(karyotypes))
ygap = .05
mgap = xgap / 4.5
gwidth = mgap * .75
tip = .02
coords = {}
for i, k in enumerate(regions.karyotypes):
x = (i + 1) * xgap
y = .9
root.text(x, y + tip, "Anc" + k, ha="center")
root.plot((x, x), (y, y - ygap), "k-", lw=2)
y -= 2 * ygap
coords['a'] = (x - 1.5 * mgap, y)
coords['b'] = (x - .5 * mgap, y)
coords['c'] = (x + .5 * mgap, y)
coords['d'] = (x + 1.5 * mgap, y)
coords['ab'] = join_nodes_vertical(root, coords, 'a', 'b',
y + ygap / 2)
coords['cd'] = join_nodes_vertical(root, coords, 'c', 'd',
y + ygap / 2)
coords['abcd'] = join_nodes_vertical(root, coords, 'ab', 'cd',
y + ygap)
for n in 'abcd':
nx, ny = coords[n]
root.text(nx, ny - tip, n, ha="center")
coords[n] = (nx, ny - ygap / 2)
kdata = regions.get_karyotype(k)
for kd in kdata:
g = kd.group
gx, gy = coords[g]
gsize = ratio * kd.span
gy -= gsize
p = Rectangle((gx - gwidth / 2, gy),
gwidth,
gsize,
lw=0,
color=set2[i])
root.add_patch(p)
root.text(gx,
gy + gsize / 2,
kd.chromosome,
ha="center",
va="center",
color='w')
coords[g] = (gx, gy - tip)
# Bottom panel shows the location of segments on chromosomes
# TODO: redundant code, similar to graphics.chromosome
ystart = .54
chr_number = len(sizes)
xstart, xend = xgap - 2 * mgap, 1 - xgap + 2 * mgap
xinterval = (xend - xstart - gwidth) / (chr_number - 1)
chrpos = {}
for a, (chr, clen) in enumerate(sorted(sizes.items())):
chr = get_number(chr)
xx = xstart + a * xinterval + gwidth / 2
chrpos[chr] = xx
root.text(xx, ystart + .01, chr, ha="center")
Chromosome(root, xx, ystart, ystart - clen * ratio, width=gwidth)
# Start painting
for r in regions:
xx = chrpos[r.chromosome]
yystart = ystart - r.start * ratio
yyend = ystart - r.end * ratio
p = Rectangle((xx - gwidth / 2, yystart),
gwidth,
yyend - yystart,
color=set2[int(r.karyotype) - 1],
lw=0)
root.add_patch(p)
root.set_xlim(0, 1)
root.set_ylim(0, 1)
root.set_axis_off()
pf = "pineapple-karyotype"
image_name = pf + "." + iopts.format
savefig(image_name, dpi=iopts.dpi, iopts=iopts)