def sim_DLILS_gene_tree(stree, popsize, freq, dr, lr, freqdup, freqloss, forcetime):
"""
Runs a relaxed fixation assumption simulation on a species tree.
Some simplifying assumptions are made for this version of the simulator:
1) All branches of the species tree have the same population size
2) All branches of the species tree have the same duplication rate
3) All branches of the species tree have the same loss rate
4) All branches of the species tree have the same duplication effect
5) All branches of the species tree have the same loss effect
6) All branches of the species tree have the same time between forced
frequency changes
7) There is a single allele at the root of the species tree.
A duplication/loss effect is the change in frequency for either event.
Appropriate default values for these effects may need to be determined.
Furture iterations should remove these assumptions by incorporating
dictionaries to allow values for each branch.
stree is the initial species tree; it may be mutated by the simulator
popsize is the population size (assmpt. 1)
freq is the allele frequency (assmpt. 7)
dr is the duplication rate (in events/myr/indiv(?); assmpt. 2)
lr is the loss rate (in events/myr/indiv(?); assmpt. 3)
freqdup is the duplication effect (assmpt. 4)
freqloss is the loss effect (assmpt. 5)
forcetime is the maximum time between frequency changes (assmpt. 6)
Update: 30 July 2010
Will return the gene (locus) tree, as well as extra information including a
reconciliation dictionary and an events dictionary.
"""
## sanity checks before running the simulator; may be removed or relaxed
treelib.assert_tree(stree)
assert popsize > 0
assert 0.0 <= freq and freq <= 1.0
assert dr >= 0.0
assert lr >= 0.0
assert 0.0 <= freqdup and freqdup <= 1.0
assert 0.0 <= freqloss and freqloss <= 1.0
assert forcetime >= 0.0
if dr + lr <= 0.0:
return stree.copy() # no duplications or losses => stree is final tree
# note: the use of < instead of <= is intentional
# if lr==0, duprate/fullrate==1, and random() returns from [0.0,1.0)
def event_is_dup(duprate, fullrate):
return random.random() < duprate / fullrate
def sim_walk(gtree, snode, gnode, p, s_walk_time=0.0, g_walk_time=0.0, \
time_until_force=forcetime, eventlog=[]):
### Most of the variables are obvious from descriptions in sim_tree or similar.
### eventlog is a log of events along the gtree branch; each entry has the form
### (time_on_branch, event_type, frequency, species_node),
### where 0.0 <= time_on_branch <= branch_node.dist
### event_type is one of {'extinction', 'frequency', 'speciation',
### duplication', 'loss', 'root', 'gene'}, where 'root' is a unique event
### not added during the sim_walk process
### frequency is the branch frequency at the event time
### species_node is the name of the node of the species tree branch
### in which the event occurs
if p <= 0.0:
## EXTINCTION EVENT
# gnode is 'parent' of extinct node
# create new_gnode
new_gnode = treelib.TreeNode(gtree.new_name())
new_gnode.dist = g_walk_time
# set new_gnode's frequency
new_gnode.data['freq'] = 0.0
gtree.add_child(gnode, new_gnode)
# add extinction event to the event log
ext_event = (g_walk_time, 'extinction', 0.0, snode.name)
eventlog.append(ext_event)
# set new_gnode's event log
new_gnode.data['log'] = eventlog
eventlog = [] # should have no effect; added for debugging on 18 Oct 2010
else: # put everything else in this block to avoid using returns
p = min(p, 1.0) # sanity check
eff_dr = dr * p # * popsize #??
eff_lr = lr * p # * popsize #??
eff_bothr = eff_dr + eff_lr
event_time = stats.exponentialvariate(eff_bothr)
remaining_s_dist = snode.dist - s_walk_time
if event_time >= min(time_until_force, remaining_s_dist):
# do not process D/L event; determine whether at force or speciation
if time_until_force < remaining_s_dist:
## FREQUENCY UPDATE EVENT
# sample a new frequency (note scaling to years from myr) # edit: not any more
newp = coal.sample_freq_CDF(p, popsize, forcetime) # * 1e6)
# TODO: if we decide not to reset time_until_force at
# speciation events, the newp generation will need to be
# altered in some form (probably using a new variable)
# update walk times
new_s_walk_time = s_walk_time + time_until_force
new_g_walk_time = g_walk_time + time_until_force
# add frequency event to event log
freq_event = (new_g_walk_time, 'frequency', newp, snode.name)
eventlog.append(freq_event)
# continue the walk with a reset forcetime
sim_walk(gtree, snode, gnode, newp, \
s_walk_time=new_s_walk_time, \
g_walk_time=new_g_walk_time, \
eventlog=eventlog)
eventlog = [] # should have no effect; debug add on 18 Oct 2010
else:
## SPECIATION EVENT
# separate into separate root, non-root speciations
# if gnode.parent: # gnode not the root
if gnode.data['log'][-1][1] != 'root':
# sample a new frequency (note scaling to years from myr) # edit: not any more
newp = coal.sample_freq_CDF(p, popsize, remaining_s_dist) # * 1e6)
# create new_gnode for this event
new_gnode = treelib.TreeNode(gtree.new_name())
new_g_walk_time = g_walk_time + remaining_s_dist
new_gnode.dist = new_g_walk_time
# set new node's frequency
new_gnode.data['freq'] = newp
gtree.add_child(gnode, new_gnode)
# add speciation event to event log and set the new node's log
if snode.is_leaf():
gene_event = (new_g_walk_time, 'gene', newp, snode.name)
eventlog.append(gene_event)
new_gnode.data['log'] = eventlog
# end of walk on species branch
eventlog = [] # should have no effect; debug add on 18 Oct 2010
else:
spec_event = (new_g_walk_time, 'speciation', newp, snode.name)
eventlog.append(spec_event)
new_gnode.data['log'] = eventlog
for schild in snode.children:
sim_walk(gtree, schild, new_gnode, newp, eventlog=[])
# TODO: if we decide not to reset time_until_force at
# speciation events, this sim_walk call will need updating
eventlog = [] # should have no effect; debug add on 18 Oct 2010
else: # gnode is the root
spec_event = (0.0, 'speciation', p, snode.name)
eventlog = gnode.data['log']
eventlog.append(spec_event)
gnode.data['log'] = eventlog
# ### debug print
# print
# print 'adding: ', eventlog
# ### end debug
for schild in snode.children:
sim_walk(gtree, schild, gnode, p, eventlog=[])
eventlog = [] # should have no effect; debug add on 18 Oct 2010
else:
# process D/L event
# no WF updates for these events (modelling decision)
new_s_walk_time = s_walk_time + event_time
new_g_walk_time = g_walk_time + event_time
new_time_until_force = time_until_force - event_time
if event_is_dup(eff_dr, eff_bothr):
## DUPLICATION EVENT
# create a node new_gnode for the duplication event
new_gnode = treelib.TreeNode(gtree.new_name())
new_gnode.dist = new_g_walk_time
# set new node's frequency
new_gnode.data['freq'] = p
gtree.add_child(gnode, new_gnode)
# add duplication event to event log and set the new node's log
dup_event = (new_g_walk_time, 'duplication', p, snode.name)
eventlog.append(dup_event)
new_gnode.data['log'] = eventlog
# recurse on remainder of original branch
sim_walk(gtree, snode, new_gnode, p, \
s_walk_time=new_s_walk_time, \
time_until_force = new_time_until_force, \
eventlog=[])
# recurse on dup tree with correct starting frequency
sim_walk(gtree, snode, new_gnode, freqdup, \
s_walk_time=new_s_walk_time, \
time_until_force = new_time_until_force, \
eventlog=[(0.0,'daughter',freqdup,snode.name)]) # added for daughter detection
eventlog = [] # should have no effect; debug add on 18 Oct 2010
else:
## LOSS EVENT
newp = max(p - freqloss, 0.0) # sanity check
# add loss event to event log
loss_event = (new_g_walk_time, 'loss', newp, snode.name)
eventlog.append(loss_event)
sim_walk(gtree, snode, gnode, newp, \
s_walk_time=new_s_walk_time, \
g_walk_time=new_g_walk_time, \
time_until_force=new_time_until_force, \
eventlog=eventlog)
eventlog = [] # should have no effect; debug add on 18 Oct 2010
# main code
# create new gene tree and simulate its evolution
gtree = treelib.Tree()
gtree.make_root()
gtree.root.dist = 0.0
gtree.root.data['freq'] = freq
root_event = (0.0, 'root', freq, stree.root.name)
gtree.root.data['log'] = [root_event]
sim_walk(gtree, stree.root, gtree.root, freq) # should mutate gtree
# # remove dead branches and single children (inside last method)
# # note that the simplifyRoot argument was added to the treelib methods
# # so that gtree.root.dist is always equal to 0.0 (and this allows the
# # root to have a single child)
# # if this behavior is undesired later, we can simply remove the argument
# # and the root will be collapsed (and have >0 dist)
extant_leaves = []
for leaf in gtree.leaves():
if leaf.data['freq'] > 0.0:
extant_leaves.append(leaf.name)
gtree = treelib.subtree_by_leaf_names(gtree, extant_leaves,
keep_single=True)
remove_single_children(gtree) # allows for correct logging of events
extras = generate_extras(stree, gtree)
return gtree, extras
def sim_tree(stree, popsize, freq, dr, lr, freqdup, freqloss, forcetime):
"""
Runs a relaxed fixation assumption simulation on a species tree.
Some simplifying assumptions are made for this version of the simulator:
1) All branches of the species tree have the same population size
2) All branches of the species tree have the same duplication rate
3) All branches of the species tree have the same loss rate
4) All branches of the species tree have the same duplication effect
5) All branches of the species tree have the same loss effect
6) All branches of the species tree have the same time between forced
frequency changes
7) There is a single allele at the root of the species tree.
A duplication/loss effect is the change in frequency for either event.
Appropriate default values for these effects may need to be determined.
Furture iterations should remove these assumptions by incorporating
dictionaries to allow values for each branch.
stree is the initial species tree; it may be mutated by the simulator
popsize is the population size (assmpt. 1)
freq is the allele frequency (assmpt. 7)
dr is the duplication rate (in events/myr/indiv(?); assmpt. 2)
lr is the loss rate (in events/myr/indiv(?); assmpt. 3)
freqdup is the duplication effect (assmpt. 4)
freqloss is the loss effect (assmpt. 5)
forcetime is the maximum time between frequency changes (assmpt. 6)
"""
## sanity checks before running the simulator; may be removed or relaxed
treelib.assert_tree(stree)
assert popsize > 0
assert 0.0 <= freq and freq <= 1.0
assert dr >= 0.0
assert lr >= 0.0
assert 0.0 <= freqdup and freqdup <= 1.0
assert 0.0 <= freqloss and freqloss <= 1.0
assert forcetime >= 0.0
if dr + lr <= 0.0:
return stree.copy() # no duplications or losses => stree is final tree
# note: the use of < instead of <= is intentional
# if lr==0, duprate/fullrate==1, and random() returns from [0.0,1.0)
def event_is_dup(duprate, fullrate):
return random.random() < duprate / fullrate
def sim_walk(gtree, snode, gnode, p, s_walk_time=0.0, g_walk_time=0.0, \
time_until_force=forcetime):
# debugprint(" Sim on branch" + str(node.name) + " with frequency " + str(p) + " and walk time " + str(walk_time))
# debugprint(" walking on " + str(gnode.name))
if p <= 0.0:
# gnode is 'parent' of extinct node
# create new_gnode, set data['freq'] = 0.0
# prune at the end
new_gnode = treelib.TreeNode(gtree.new_name())
new_gnode.dist = g_walk_time
new_gnode.data['freq'] = 0.0
gtree.add_child(gnode, new_gnode)
# debugprint(" extinction on " + str(gnode.name))
else: # put everything else in this block to avoid using returns
p = min(p, 1.0) # sanity check
eff_dr = dr * p # * popsize #??
eff_lr = lr * p # * popsize #??
eff_bothr = eff_dr + eff_lr
event_time = stats.exponentialvariate(eff_bothr)
remaining_s_dist = snode.dist - s_walk_time
if event_time >= min(time_until_force, remaining_s_dist):
# do not process D/L event; determine whether at force or speciation
if time_until_force < remaining_s_dist:
# force new frequency
newp = coal.sample_freq_CDF(p, popsize, forcetime * 1e6)
# scale forcetime to years (in myr)
# debugprint(" Forced new frequency: " + str(newp))
## TODO: may wish to log newp in node.data
new_s_walk_time = s_walk_time + time_until_force
new_g_walk_time = g_walk_time + time_until_force
sim_walk(gtree, snode, gnode, newp, \
s_walk_time=new_s_walk_time, \
g_walk_time=new_g_walk_time)
# continue walk with new frequency
# increase walk_times accordingly
# reset time_until_force to forcetime
else:
# speciation event
newp = coal.sample_freq_CDF(p, popsize, remaining_s_dist * 1e6)
# scale remaining time into years (from myr)
new_gnode = treelib.TreeNode(gtree.new_name())
new_gnode.dist = g_walk_time + remaining_s_dist
new_gnode.data['freq'] = newp
# stores frequency of allele at the speciation event
gtree.add_child(gnode, new_gnode)
# debugprint(" Completed branch; new frequency: " + str(newp))
for schild in snode.children:
sim_walk(gtree, schild, new_gnode, newp)
# return # shouldn't be necessary
else:
# process D/L event
# no WF updates for these events (modelling decision)
new_s_walk_time = s_walk_time + event_time
new_g_walk_time = g_walk_time + event_time
new_time_until_force = time_until_force - event_time
if event_is_dup(eff_dr, eff_bothr):
# perform duplication event
new_gnode = treelib.TreeNode(gtree.new_name())
# create a node new_gnode for the duplication event
new_gnode.dist = new_g_walk_time # set dist to dup
new_gnode.data['freq'] = p # set frequency at dup event
# debugprint(" Duplication occurred at walk time " + str(new_walk_time))
gtree.add_child(gnode, new_gnode)
# debugprint(" starting on orig of " + str(new_gnode.name))
sim_walk(gtree, snode, new_gnode, p, \
s_walk_time=new_s_walk_time, \
time_until_force = new_time_until_force)
# recurse on remainder of original branch
# debugprint(" starting on dup of " + str(new_gnode.name))
sim_walk(gtree, snode, new_gnode, freqdup, \
s_walk_time=new_s_walk_time, \
time_until_force = new_time_until_force)
# recurse on dup tree with correct starting frequency
# return
else:
# perform loss event
newp = max(p - freqloss, 0.0) # sanity check
# debugprint(" Loss occurred at walk time " + str(new_walk_time) + " yielding new frequency " + str(newp))
sim_walk(gtree, snode, gnode, newp, \
s_walk_time=new_s_walk_time, \
g_walk_time=new_g_walk_time, \
time_until_force=new_time_until_force)
# main code
# create new gene tree and simulate its evolution
gtree = treelib.Tree()
gtree.make_root()
gtree.root.dist = 0.0
gtree.root.data['freq'] = freq
sim_walk(gtree, stree.root, gtree.root, freq) # should mutate gtree
# remove dead branches and single children (inside last method)
# note that the simplifyRoot argument was added to the treelib methods
# so that gtree.root.dist is always equal to 0.0 (and this allows the
# root to have a single child)
# if this behavior is undesired later, we can simply remove the argument
# and the root will be collapsed (and have >0 dist)
extant_leaves = []
for leaf in gtree.leaves():
if leaf.data['freq'] > 0.0:
extant_leaves.append(leaf.name)
gtree = treelib.subtree_by_leaf_names(gtree, extant_leaves, \
simplifyRoot=False)
return gtree
def sample_locus_tree_hem(stree, popsize, duprate, lossrate,
freq=1.0, freqdup=.05, freqloss=.05,
steptime=1e6, keep_extinct=False):
"""
Sample a locus tree with birth-death and hemiplasy
Runs a relaxed fixation assumption simulation on a species tree.
Some simplifying assumptions are made for this version of the simulator:
1) All branches of the species tree have the same population size
2) All branches of the species tree have the same duplication rate
3) All branches of the species tree have the same loss rate
4) All branches of the species tree have the same duplication effect
5) All branches of the species tree have the same loss effect
6) All branches of the species tree have the same time between forced
frequency changes
7) There is a single allele at the root of the species tree.
A duplication/loss effect is the change in frequency for either event.
Appropriate default values for these effects may need to be determined.
Furture iterations should remove these assumptions by incorporating
dictionaries to allow values for each branch.
parameters:
stree is the initial species tree; it may be mutated by the simulator
popsize is the population size (assmpt. 1)
freq is the allele frequency (assmpt. 7)
duprate is the duplication rate (in events/myr/indiv(?); assmpt. 2)
lossrate is the loss rate (in events/myr/indiv(?); assmpt. 3)
freqdup is the duplication effect (assmpt. 4)
freqloss is the loss effect (assmpt. 5)
forcetime is the maximum time between frequency changes (assmpt. 6)
Returns the locus tree, as well as extra information
including a reconciliation dictionary and an events dictionary.
"""
## sanity checks before running the simulator; may be removed or relaxed
treelib.assert_tree(stree)
assert popsize > 0
assert 0.0 <= freq and freq <= 1.0
assert duprate >= 0.0
assert lossrate >= 0.0
assert 0.0 <= freqdup and freqdup <= 1.0
assert 0.0 <= freqloss and freqloss <= 1.0
assert steptime > 0.0
# special case: no duplications or losses
if duprate == 0.0 and lossrate == 0.0:
locus_tree = stree.copy()
recon = phylo.reconcile(locus_tree, stree, lambda x: x)
events = phylo.label_events(locus_tree, recon)
return locus_tree, {"recon": recon,
"events": events,
"daughters": set()}
def event_is_dup(duprate, fullrate):
return random.random() <= duprate / fullrate
def sim_walk(gtree, snode, gparent, p,
s_walk_time=0.0, remaining_steptime=steptime,
daughter=False):
"""
eventlog is a log of events along the gtree branch.
Each entry has the form
(time_on_branch, event_type, frequency, species_node),
where
0.0 <= time_on_branch <= branch_node.dist
event_type is one of
{'extinction', 'frequency', 'speciation', duplication',
'loss', 'root', 'gene'},
where 'root' is a unique event not added during the sim_walk process
frequency is the branch frequency at the event time
species_node is the name of the node of the species tree branch in
which the event occurs
"""
# create new node
gnode = treelib.TreeNode(gtree.new_name())
gtree.add_child(gparent, gnode)
gnode.data = {"freq": p,
"log": []}
eventlog = gnode.data["log"]
g_walk_time = 0.0
if daughter:
eventlog.append((0.0, 'daughter', freqdup, snode.name))
# grow this branch, determine next event
event = None
while True:
if p <= 0.0:
event = "extinct"
break
# determine remaing time
remaining_s_dist = snode.dist - s_walk_time
remaining_time = min(remaining_steptime, remaining_s_dist)
# sample next dup/loss event
eff_duprate = duprate * p / freqdup
eff_lossrate = lossrate * p / freqloss
eff_bothrate = eff_duprate + eff_lossrate
event_time = stats.exponentialvariate(eff_bothrate)
# advance times
time_delta = min(event_time, remaining_time)
s_walk_time += time_delta
g_walk_time += time_delta
# sample new frequency
p = coal.sample_freq_CDF(p, popsize, time_delta)
# determine event
if event_time < remaining_time:
# dup/loss occurs
if event_is_dup(eff_duprate, eff_bothrate):
# dup, stop growing
event = "dup"
break
else:
# loss, continue growing
event = "loss"
else:
if remaining_s_dist < remaining_steptime:
# we are at a speciation, stop growing
event = "spec"
break
# process step
if event == "loss":
# LOSS EVENT
p = max(p - freqloss, 0.0)
remaining_steptime -= time_delta
eventlog.append((g_walk_time, 'loss', p, snode.name))
else:
# NEXT TIME STEP
remaining_steptime = steptime
eventlog.append((g_walk_time, 'frequency', p, snode.name))
# process event
if event == "extinct":
# EXTINCTION EVENT (p <= 0)
gnode.dist = g_walk_time
gnode.data['freq'] = 0.0
eventlog.append((g_walk_time, 'extinction', 0.0, snode.name))
elif event == "spec":
# SPECIATION EVENT
gnode.dist = g_walk_time
gnode.data['freq'] = p
# add speciation event to event log and
if snode.is_leaf():
eventlog.append((g_walk_time, 'gene', p, snode.name))
else:
eventlog.append((g_walk_time, 'speciation', p, snode.name))
for schild in snode.children:
sim_walk(gtree, schild, gnode, p)
elif event == "dup":
# DUPLICATION EVENT
gnode.dist = g_walk_time
gnode.data['freq'] = p
eventlog.append((g_walk_time, 'duplication', p, snode.name))
# recurse on mother
sim_walk(gtree, snode, gnode, p,
s_walk_time=s_walk_time,
remaining_steptime=remaining_steptime)
# recurse on daughter
sim_walk(gtree, snode, gnode, freqdup,
s_walk_time=s_walk_time,
remaining_steptime=remaining_steptime,
daughter=True)
else:
raise Exception("unknown event '%s'" % event)
# create new gene tree and simulate its evolution
gtree = treelib.Tree()
gtree.make_root()
gtree.root.dist = 0.0
gtree.root.data['freq'] = freq
gtree.root.data['log'] = [(0.0, 'speciation', freq, stree.root.name)]
# simulate locus tree
sim_walk(gtree, stree.root.children[0], gtree.root, freq)
sim_walk(gtree, stree.root.children[1], gtree.root, freq)
# remove dead branches and single children
extant_leaves = [leaf.name for leaf in gtree.leaves()
if leaf.data['freq'] > 0.0]
extinctions = [leaf for leaf in gtree.leaves()
if leaf.data['freq'] == 0.0]
if keep_extinct:
full_gtree = gtree.copy()
# do deep copy of data
for node in full_gtree:
node2 = gtree.nodes[node.name]
for key, val in node2.data.items():
node.data[key] = copy.copy(val)
treelib.subtree_by_leaf_names(gtree, extant_leaves, keep_single=True)
remove_single_children(gtree)
# determine extra information (recon, events, daughters)
extras = generate_extras(stree, gtree)
if keep_extinct:
extras["full_locus_tree"] = full_gtree
return gtree, extras