def draw_events(canvas, tree, coords, events, losses,
lossColor=(0, 0, 1),
dupColor=(1, 0, 0),
size=4):
# draw duplications
for node in tree:
x, y = coords[node]
if events[node] == "dup":
canvas.rect(x - size/2.0, y - size/2.0,
size, size, fillColor=dupColor, strokeColor=(0,0,0,0))
# draw losses
losses_per_branch = util.hist_dict([node for node, schild in losses])
for node, nlosses in losses_per_branch.iteritems():
if node.parent == None:
continue
x1 = coords[node.parent][0]
x2, y1 = coords[node]
step = (x2 - x1) / float(nlosses + 1)
for x in util.frange(x1 + step, x2-(step/2.0), step):
canvas.line(x, y1 - size, x, y1 + size, color=lossColor)
def find_orthologs(gtree, stree, recon, counts=True):
"""Find all ortholog pairs within a gene tree"""
events = label_events(gtree, recon)
orths = []
for node, event in events.items():
if event == "spec":
leavesmat = [x.leaves() for x in node.children]
sp_counts = [util.hist_dict(util.mget(recon, row))
for row in leavesmat]
for i in range(len(leavesmat)):
for j in range(i+1, len(leavesmat)):
for gene1 in leavesmat[i]:
for gene2 in leavesmat[j]:
if gene1.name > gene2.name:
g1, g2 = gene2, gene1
a, b = j, i
else:
g1, g2 = gene1, gene2
a, b = i, j
if not counts:
orths.append((g1.name, g2.name))
else:
orths.append((g1.name, g2.name,
sp_counts[a][recon[g1]],
sp_counts[b][recon[g2]]))
return orths
def draw_events(canvas,
tree,
coords,
events,
losses,
lossColor=(0, 0, 1),
dupColor=(1, 0, 0),
size=4):
# draw duplications
for node in tree:
x, y = coords[node]
if events[node] == "dup":
canvas.rect(x - size / 2.0,
y - size / 2.0,
size,
size,
fillColor=dupColor,
strokeColor=(0, 0, 0, 0))
# draw losses
losses_per_branch = util.hist_dict([node for node, schild in losses])
for node, nlosses in losses_per_branch.iteritems():
if node.parent == None:
continue
x1 = coords[node.parent][0]
x2, y1 = coords[node]
step = (x2 - x1) / float(nlosses + 1)
for x in util.frange(x1 + step, x2 - (step / 2.0), step):
canvas.line(x, y1 - size, x, y1 + size, color=lossColor)
def draw_events(self):
# draw duplications
dups = [color(*self.dup_color)]
for node in self.tree:
if self.events[node] == "dup":
dups.append(
zoom_clamp(
shapes.box(node.x - .5, node.y - .5,
node.x + .5, node.y + .5),
link=True, link_type="smaller",
maxx=8, minx=1,
maxy=8, miny=1,
origin=(node.x, node.y),
prezoom=(self.xscale, 1.0)))
# draw losses
losses_per_branch = util.hist_dict([node for node, schild in self.losses])
losses = [color(*self.loss_color)]
for node, nlosses in losses_per_branch.iteritems():
if node.parent == None:
continue
x1 = node.parent.x
x2 = node.x
step = (x2 - x1) / float(nlosses + 1)
for x in util.frange(x1 + step, x2-(step/2.0), step):
losses.append(lines(x, node.y - .2, x, node.y + .2))
return group(group(*dups), group(*losses))
def histtab(items, headers=None, item="item", count="count", percent="percent",
cols=None):
"""Make a histogram table."""
if cols is not None:
# items is a Table.
items = items.as_tuples(cols=cols)
if headers is None:
headers = cols + [count, percent]
if headers is None:
headers = [item, count, percent]
h = util.hist_dict(items)
tab = Table(headers=headers)
tot = float(sum(h.itervalues()))
hist_items = h.items()
if cols is not None:
for key, val in hist_items:
row = dict(zip(cols, key))
row[count] = val
tab.append(row)
else:
for key, val in hist_items:
tab.append({item: key,
count: val})
if percent is not None:
for i, (key, val) in enumerate(hist_items):
tab[i][percent] = val / tot
tab.sort(col=count, reverse=True)
return tab
def mode(vals):
"""Computes the mode of a list of numbers"""
top = 0
topkey = None
for key, val in util.hist_dict(vals).iteritems():
if val > top:
top = val
topkey = key
return topkey
def mode(vals):
"""Computes the mode of a list of numbers"""
top = 0
topkey = None
for key, val in util.hist_dict(vals).iteritems():
if val > top:
top = val
topkey = key
return topkey
def make_pep_colors(prop2color=prop2color):
pep_colors = util.Dict(default=color(.5, .5, .5))
AA = 'ARNDCEQGHILKMFPSTWYVU*'
pep_per_prop = util.hist_dict(util.mget(seqlib.AA_PROPERTY, AA))
prop_counts = util.Dict(default=0)
for char in AA:
prop = seqlib.AA_PROPERTY[char]
tint = prop_counts[prop] / float(pep_per_prop[prop])
pep_colors[char] = prop2color(prop, tint * .5)
prop_counts[prop] += 1
return pep_colors
def make_pep_colors(prop2color=prop2color):
pep_colors = util.Dict(default=color(.5, .5, .5))
AA = 'ARNDCEQGHILKMFPSTWYVU*'
pep_per_prop = util.hist_dict(util.mget(seqlib.AA_PROPERTY, AA))
prop_counts = util.Dict(default=0)
for char in AA:
prop = seqlib.AA_PROPERTY[char]
tint = prop_counts[prop] / float(pep_per_prop[prop])
pep_colors[char] = prop2color(prop, tint * .5)
prop_counts[prop] += 1
return pep_colors
def test_sample_coal_recomb(self):
rho = 1.5e-8 # recomb/site/gen
l = 2000 # length of locus
k = 10 # number of lineages
n = 2 * 10000 # effective popsize
r = rho * l # recomb/locus/gen
nsamples = 10000
samples = [arglib.sample_coal_recomb(k, n, r) for i in range(nsamples)]
events = dict(
(event, count / float(nsamples))
for event, count in util.hist_dict(util.cget(samples, 0)).items())
expected = {'coal': 0.88146, 'recomb': 0.11854}
for key, value in events.items():
self.assertAlmostEqual(value, expected[key], places=2)
def test_sample_coal_recomb(self):
rho = 1.5e-8 # recomb/site/gen
l = 2000 # length of locus
k = 10 # number of lineages
n = 2*10000 # effective popsize
r = rho * l # recomb/locus/gen
nsamples = 10000
samples = [arglib.sample_coal_recomb(k, n, r)
for i in range(nsamples)]
events = dict(
(event, count / float(nsamples))
for event, count in util.hist_dict(util.cget(samples, 0)).items())
expected = {'coal': 0.88146, 'recomb': 0.11854}
for key, value in events.items():
self.assertAlmostEqual(value, expected[key], places=2)
def find_four_fold(aln):
"""Returns index of all columns in alignment that are completely
fourfold degenerate
Assumes that columns are already filtered for aligned codons
"""
# create peptide alignment
pepAln = mapalign(aln, valfunc=translate)
# find peptide conservation
pepcons = []
pep = []
for i in xrange(pepAln.alignlen()):
# get a column from the peptide alignment
col = [seq[i] for seq in pepAln.itervalues()]
# compute the histogram of the column.
# ignore gaps '-' and non-translated 'X'
hist = util.hist_dict(col)
if "-" in hist:
del hist["-"]
if "X" in hist:
del hist["X"]
# column is conserved if only one AA appears
if len(hist) == 1:
pepcons.append(True)
pep.append(hist.keys()[0])
else:
pepcons.append(False)
pep.append("X")
# find four-fold sites in conserved peptides
ind = []
for i in range(0, len(aln.values()[0]), 3):
# process only those columns that are conserved at the peptide level
if pepcons[i//3]:
degen = AA_DEGEN[pep[i//3]]
for j in range(3):
if degen[j] == 4:
ind.append(i+j)
return ind
def find_four_fold(aln):
"""Returns index of all columns in alignment that are completely
fourfold degenerate
Assumes that columns are already filtered for aligned codons
"""
# create peptide alignment
pepAln = mapalign(aln, valfunc=translate)
# find peptide conservation
pepcons = []
pep = []
for i in xrange(pepAln.alignlen()):
# get a column from the peptide alignment
col = [seq[i] for seq in pepAln.itervalues()]
# compute the histogram of the column.
# ignore gaps '-' and non-translated 'X'
hist = util.hist_dict(col)
if "-" in hist:
del hist["-"]
if "X" in hist:
del hist["X"]
# column is conserved if only one AA appears
if len(hist) == 1:
pepcons.append(True)
pep.append(hist.keys()[0])
else:
pepcons.append(False)
pep.append("X")
# find four-fold sites in conserved peptides
ind = []
for i in range(0, len(aln.values()[0]), 3):
# process only those columns that are conserved at the peptide level
if pepcons[i//3]:
degen = AA_DEGEN[pep[i//3]]
for j in range(3):
if degen[j] == 4:
ind.append(i+j)
return ind
def debug_test3():
stree = treelib.read_tree('examples/nbin.stree') # run from ../ of this directory
for node in stree:
node.dist *= 1e7 # gen per myr
popsize = 2e7
freq = 1e0
dr = .0000012 / 1e7 #.0012/1e7
lr = .0000011 / 1e7 #.0006/1e7
freqdup = freqloss = .05
forcetime = 1e7
for node in stree:
print node.name, node.dist, len(node.children)
print
locus_tree, locus_extras = sim_DLILS_gene_tree(stree, popsize, freq, \
dr, lr, \
freqdup, freqloss, \
forcetime)
for node in locus_tree:
print node.name, node.dist, len(node.children)
print
logged_locus_tree, logged_extras = locus_to_logged_tree(locus_tree, popsize)
daughters = logged_extras[0]
pops = logged_extras[1]
coal_tree, coal_recon = dlcoal.sample_locus_coal_tree(logged_locus_tree,
n=pops, daughters=daughters,
namefunc=lambda x: logged_extras[2][x] + '_' + str(x))
#begin debug
print coal_tree.leaf_names()
try:
# print set(coal_tree) - set(coal_tree.postorder())
treelib.assert_tree(coal_tree)
except AssertionError:
print 'assertion error thrown on coal_tree being a proper tree'
from rasmus import util
hd= util.hist_dict(x.name for x in coal_tree.postorder())
for key in hd.keys():
print key if hd[key]>1 else '',
print
print len(coal_tree.nodes) - len(list(coal_tree.postorder()))
def find_xenologs(gtree,
stree,
recon,
events,
trans,
counts=True,
species_branch=False):
"""Find all xenolog pairs within a gene tree
NOTE: THIS HAS NOT BEEN TESTED!!!
"""
xenos = []
for node, event in events.items():
if event == "trans":
assert len(node.children) == 2
if trans[node] == node.children[0]:
children = (node.children[1], node.children[0])
else:
children = node.children
leavesmat = [x.leaves() for x in children]
sp_counts = [
util.hist_dict(util.mget(recon, row)) for row in leavesmat
]
for i in range(len(leavesmat)):
for j in range(i + 1, len(leavesmat)):
for gene1 in leavesmat[i]:
for gene2 in leavesmat[j]:
g1, g2 = gene1, gene2
a, b = i, j
xeno = [g1.name, g2.name]
if counts:
xeno.extend([
sp_counts[a][recon[g1]],
sp_counts[b][recon[g2]]
])
if species_branch:
xeno.append(recon[node])
xenos.append(tuple(xenos))
return xenos
def calc_conservation(aln):
"""Returns a list of percent matching in each column of an alignment"""
length = len(aln.values()[0])
seqs = aln.values()
percids = []
# find identity positions
identity = ""
for i in xrange(length):
chars = util.hist_dict(util.cget(seqs, i))
if "-" in chars: del chars["-"]
if len(chars) == 0:
percids.append(0.0)
else:
pid = max(chars.values()) / float(len(aln))
percids.append(pid)
return percids
def calc_conservation(aln):
"""Returns a list of percent matching in each column of an alignment"""
length = len(aln.values()[0])
seqs = aln.values()
percids = []
# find identity positions
for i in xrange(length):
chars = util.hist_dict(util.cget(seqs, i))
if "-" in chars:
del chars["-"]
if len(chars) == 0:
percids.append(0.0)
else:
pid = max(chars.values()) / float(len(aln))
percids.append(pid)
return percids
def getFamDescription(self, descriptions):
# TODO: remove this hardcoding
rmdesc = set([
"", "Predicted ORF from Assembly 19",
"Predicted ORF in Assemblies 19 and 20",
"ORF Predicted by Annotation Working Group",
"possibly spurious ORF (Annotation Working Group prediction)"
])
descs = []
for d in descriptions:
descs.extend(d.split("; "))
descs = filter(lambda x: x not in rmdesc, descs)
items = util.hist_dict(descs).items()
items.sort(key=lambda x: x[1], reverse=True)
desc = "; ".join(["%s[%d]" % item for item in items])
return desc
def getFamDescription(self, descriptions):
# TODO: remove this hardcoding
rmdesc = set([
"",
"Predicted ORF from Assembly 19",
"Predicted ORF in Assemblies 19 and 20",
"ORF Predicted by Annotation Working Group",
"possibly spurious ORF (Annotation Working Group prediction)"])
descs = []
for d in descriptions:
descs.extend(d.split("; "))
descs = filter(lambda x: x not in rmdesc, descs)
items = util.hist_dict(descs).items()
items.sort(key=lambda x: x[1], reverse=True)
desc = "; ".join(["%s[%d]" % item for item in items])
return desc
def histtab(items, headers=["item", "count", "percent"]):
h = util.hist_dict(items)
tab = Table(headers=headers)
tot = float(sum(h.itervalues()))
if len(headers) == 2:
for key, val in h.items():
tab.append({headers[0]: key,
headers[1]: val})
elif len(headers) == 3:
for key, val in h.items():
tab.append({headers[0]: key,
headers[1]: val,
headers[2]: val / tot})
else:
raise Exception("Wrong number of headers (2 or 3 only)")
tab.sort(col=headers[1], reverse=True)
return tab
def histtab(items, headers=["item", "count", "percent"]):
h = util.hist_dict(items)
tab = Table(headers=headers)
tot = float(len(items))
if len(headers) == 2:
for key, val in h.items():
tab.append({headers[0]: key,
headers[1]: val})
elif len(headers) == 3:
for key, val in h.items():
tab.append({headers[0]: key,
headers[1]: val,
headers[2]: val / tot})
else:
raise Exception("Wrong number of headers (2 or 3 only)")
tab.sort(col=headers[1], reverse=True)
return tab
def num_redundant_topology(node, gene2species, leaves=None, all_leaves=False):
"""Returns the number of 'redundant' topologies"""
if leaves is None:
leaves = node.leaves()
leaves = set(leaves)
colors = {}
nmirrors = [0]
def walk(node):
if node in leaves:
colors[node] = phylo.hash_tree(node, gene2species)
else:
# recurse
for child in node.children:
walk(child)
childHashes = util.mget(colors, node.children)
if len(childHashes) > 1 and util.equal(*childHashes):
nmirrors[0] += 1
childHashes.sort()
colors[node] = phylo.hash_tree_compose(childHashes)
walk(node)
colorsizes = util.hist_dict(util.mget(colors, leaves)).values()
if all_leaves:
val = stats.factorial(len(leaves))
else:
val = 1
for s in colorsizes:
if s > 1:
val *= stats.factorial(s)
#print "py val=", val, "nmirrors=", nmirrors[0]
return val / (2**nmirrors[0])
def num_redundant_topology(node, gene2species, leaves=None, all_leaves=False):
"""Returns the number of 'redundant' topologies"""
if leaves is None:
leaves = node.leaves()
leaves = set(leaves)
colors = {}
nmirrors = [0]
def walk(node):
if node in leaves:
colors[node] = phylo.hash_tree(node, gene2species)
else:
# recurse
for child in node.children:
walk(child)
childHashes = util.mget(colors, node.children)
if len(childHashes) > 1 and util.equal(*childHashes):
nmirrors[0] += 1
childHashes.sort()
colors[node] = phylo.hash_tree_compose(childHashes)
walk(node)
colorsizes = util.hist_dict(util.mget(colors, leaves)).values()
if all_leaves:
val = stats.factorial(len(leaves))
else:
val = 1
for s in colorsizes:
if s > 1:
val *= stats.factorial(s)
# print "py val=", val, "nmirrors=", nmirrors[0]
return val / (2 ** nmirrors[0])
def isOne2one(part, gene2species):
counts = util.hist_dict(map(gene2species, part))
return (max(counts.values()) == 1)
def isOne2one(part, gene2species):
counts = util.hist_dict(map(gene2species, part))
return (max(counts.values()) == 1)
def gcContent(seq):
hist = util.hist_dict(seq)
total = hist["A"] + hist["C"] + hist["T"] + hist["G"]
return (hist["C"] + hist["G"]) / float(total)
def gcContent(seq):
hist = util.hist_dict(seq)
total = hist["A"] + hist["C"] + hist["T"] + hist["G"]
return (hist["C"] + hist["G"]) / float(total)
