import time, sys
import gc
import numpy as np
from Bio import Phylo
from Bio.Phylo.BaseTree import Clade
from . import config as ttconf
from . import MissingDataError,UnknownMethodError
from .seq_utils import seq2prof, prof2seq, normalize_profile, extend_profile
from .gtr import GTR
from .gtr_site_specific import GTR_site_specific
from .sequence_data import SequenceData
def compressed_sequence(node):
if node.name in node.tt.data.compressed_alignment and (not node.tt.reconstructed_tip_sequences):
return node.tt.data.compressed_alignment[node.name]
elif hasattr(node, '_cseq'):
return node._cseq
elif node.is_terminal(): # node without sequence when tip-reconstruction is off.
return None
elif hasattr(node, '_cseq'):
return node._cseq
else:
raise ValueError('Ancestral sequences are not yet inferred')
def mutations(node):
"""
Get the mutations on a tree branch. Take compressed sequences from both sides
of the branch (attached to the node), compute mutations between them, and
expand these mutations to the positions in the real sequences.
"""
if node.up is None:
return []
elif (not node.tt.reconstructed_tip_sequences) and node.name in node.tt.data.aln:
return node.tt.data.differences(node.up.cseq, node.tt.data.aln[node.name], seq2_compressed=False, mask=node.mask)
elif node.is_terminal() and (node.name not in node.tt.data.aln):
return []
else:
return node.tt.data.differences(node.up.cseq, node.cseq, mask=node.mask)
string_types = [str] if sys.version_info[0]==3 else [str, unicode]
Clade.sequence = property(lambda x: x.tt.sequence(x, as_string=False))
Clade.cseq = property(compressed_sequence)
Clade.mutations = property(mutations)
[docs]class TreeAnc(object):
"""
Class defines simple tree object with basic interface methods: reading and
saving from/to files, initializing leaves with sequences from the
alignment, making ancestral state inference
"""
[docs] def __init__(self, tree=None, aln=None, gtr=None, fill_overhangs=True,
ref=None, verbose = ttconf.VERBOSE, ignore_gaps=True,
convert_upper=True, seq_multiplicity=None, log=None,
compress=True, seq_len=None, ignore_missing_alns=False,
keep_node_order=False, rng_seed=None, **kwargs):
"""
TreeAnc constructor. It prepares the tree, attaches sequences to the leaf nodes,
and sets some configuration parameters.
Parameters
----------
tree : str, Bio.Phylo.Tree
Phylogenetic tree. String passed is interpreted as a filename with
a tree in a standard format that can be parsed by the Biopython Phylo module.
Branch length should be in units of average number of nucleotide or protein
substitutions per site. Use on trees with longer branches (>4) is not recommended.
aln : str, Bio.Align.MultipleSequenceAlignment, dict
Sequence alignment. If a string passed, it is interpreted as the
filename to read Biopython alignment from. If a dict is given,
this is assumed to be the output of vcf_utils.read_vcf which
specifies for each sequence the differences from a reference
gtr : str, GTR
GTR model object. If string passed, it is interpreted as the type of
the GTR model. A new GTR instance will be created for this type.
fill_overhangs : bool, default True
In some cases, the missing data on both ends of the alignment is
filled with the gap sign('-'). If set to True, the end-gaps are converted to "unknown"
characters ('N' for nucleotides, 'X' for aminoacids). Otherwise, the alignment is treated as-is
ref : None, optional
Reference sequence used in VCF mode
verbose : int, default 3
Verbosity level as number from 0 (lowest) to 10 (highest).
ignore_gaps : bool, default True
Ignore gaps in branch length calculations
convert_upper : bool, default True
Convert all sequences to upper case
seq_multiplicity : dict
If individual nodes in the tree correspond to multiple sampled sequences
(i.e. read count in a deep sequencing experiment), these can be
specified as a dictionary. This currently only affects rooting and
can be used to weigh individual tips by abundance or important during root search.
compress : bool, default True
reduce identical alignment columns to one (not useful when
inferring site specific GTR models).
seq_len : int, optional
length of the sequence. this is inferred from the input alignment or the reference
sequence in most cases but can be specified for other applications.
ignore_missing_alns : bool, default False
**kwargs
Keyword arguments to construct the GTR model
.. Note::
Some GTR types require additional configuration parameters.
If the new GTR is being instantiated, these parameters are expected
to be passed as kwargs. If nothing is passed, the default values are
used, which might cause unexpected results.
Raises
------
AttributeError
If no tree is passed in
"""
if tree is None:
raise TypeError("TreeAnc requires a tree!")
self.t_start = time.time()
self.verbose = verbose
self.log = log
self.ok = False
self.data = None
self.log_messages = set()
self.logger("TreeAnc: set-up",1)
self._internal_node_count = 0
self.use_mutation_length = False
self.ignore_gaps = ignore_gaps
self.reconstructed_tip_sequences = False
self.sequence_reconstruction = None
self.ignore_missing_alns = ignore_missing_alns
self.keep_node_order = keep_node_order
self.rng = np.random.default_rng(seed=rng_seed)
self._tree = None
self.tree = tree
if tree is None:
raise MissingDataError("TreeAnc: tree loading failed! exiting")
# set up GTR model
self._gtr = None
self.set_gtr(gtr or 'JC69', **kwargs)
# set alignment and attach sequences to tree on success.
# otherwise self.data.aln will be None
self.data = SequenceData(aln, ref=ref, logger=self.logger, compress=compress,
convert_upper=convert_upper, fill_overhangs=fill_overhangs, ambiguous=self.gtr.ambiguous,
sequence_length=seq_len)
if self.gtr.is_site_specific and self.data.compress:
raise TypeError("TreeAnc: sequence compression and site specific gtr models are incompatible!" )
if self.data.aln and self.tree:
self._check_alignment_tree_gtr_consistency()
[docs] def logger(self, msg, level, warn=False, only_once=False):
"""
Print log message *msg* to stdout.
Parameters
-----------
msg : str
String to print on the screen
level : int
Log-level. Only the messages with a level higher than the
current verbose level will be shown.
warn : bool
Warning flag. If True, the message will be displayed
regardless of its log-level.
"""
if only_once and msg in self.log_messages:
return
self.log_messages.add(msg)
lw=80
if level<self.verbose or (warn and level<=self.verbose):
from textwrap import fill
dt = time.time() - self.t_start
outstr = '\n' if level<2 else ''
initial_indent = format(dt, '4.2f')+'\t' + level*'-'
subsequent_indent = " "*len(format(dt, '4.2f')) + "\t" + " "*level
outstr += fill(msg, width=lw, initial_indent=initial_indent, subsequent_indent=subsequent_indent)
print(outstr, file=sys.stdout)
####################################################################
## SET-UP
####################################################################
@property
def leaves_lookup(self):
"""
The :code:`{leaf-name:leaf-node}` dictionary. It enables fast
search of a tree leaf object by its name.
"""
return self._leaves_lookup
@property
def gtr(self):
"""
:setter: Sets the GTR object passed in
:getter: Returns the current GTR object
"""
return self._gtr
@gtr.setter
def gtr(self, value):
"""
Parameters
-----------
value : GTR
the new GTR object
"""
if not isinstance(value, (GTR, GTR_site_specific)):
raise TypeError("GTR instance expected")
self._gtr = value
[docs] def set_gtr(self, in_gtr, **kwargs):
"""
Create new GTR model if needed, and set the model as an attribute of the
TreeAnc class
Parameters
-----------
in_gtr : str, GTR
The gtr model to be assigned. If string is passed,
it is taken as the name of a standard GTR model, and is
attempted to be created through :code:`GTR.standard()` interface. If a
GTR instance is passed, it is set directly .
**kwargs
Keyword arguments to construct the GTR model. If none are passed, defaults
are assumed.
"""
if isinstance(in_gtr, str):
self._gtr = GTR.standard(model=in_gtr, **kwargs)
self._gtr.logger = self.logger
elif isinstance(in_gtr, (GTR, GTR_site_specific)):
self._gtr = in_gtr
self._gtr.logger=self.logger
else:
self.logger("TreeAnc.gtr_setter: can't interpret GTR model", 1, warn=True)
raise TypeError("Cannot set GTR model in TreeAnc class: GTR or "
"string expected")
if self._gtr.ambiguous is None:
self.fill_overhangs=False
@property
def aln(self):
'''
:setter: Sets the alignment
:getter: Returns the alignment
'''
return self.data.aln
@aln.setter
def aln(self,in_aln):
self.data.aln=in_aln
if self.tree:
self._check_alignment_tree_gtr_consistency()
@property
def tree(self):
"""
The phylogenetic tree currently used by the TreeAnc.
:setter: Sets the tree. Directly if passed as Phylo.Tree, or by reading from \
file if passed as a str.
:getter: Returns the tree as a Phylo.Tree object
"""
return self._tree
@tree.setter
def tree(self, in_tree):
'''
assigns a tree to the internal self._tree variable. The tree is either
loaded from file (if in_tree is str) or assigned (if in_tree is a Phylo.tree)
'''
from os.path import isfile
self._tree = None
if isinstance(in_tree, Phylo.BaseTree.Tree):
self._tree = in_tree
elif type(in_tree) in string_types and isfile(in_tree):
try:
self._tree=Phylo.read(in_tree, 'newick')
except:
fmt = in_tree.split('.')[-1]
if fmt in ['nexus', 'nex']:
self._tree=Phylo.read(in_tree, 'nexus')
else:
raise MissingDataError('TreeAnc: could not load tree, format needs to be nexus or newick! input was '+str(in_tree))
else:
raise MissingDataError('TreeAnc: could not load tree! input was '+str(in_tree))
if self._tree.count_terminals()<3:
raise MissingDataError('TreeAnc: tree in %s as only %d tips. Please check your tree!'%(str(in_tree), self._tree.count_terminals()))
# remove all existing sequence attributes
branch_length_warning = False
for node in self._tree.find_clades():
node.branch_length = node.branch_length if node.branch_length else 0.0
if node.branch_length > ttconf.MAX_BRANCH_LENGTH:
branch_length_warning = True
if hasattr(node, "_cseq"):
node.__delattr__("_cseq")
node.original_length = node.branch_length
node.mutation_length = node.branch_length
if branch_length_warning:
self.logger("WARNING: TreeTime has detected branches that are longer than %d. "
"TreeTime requires trees where branch length is in units of average number "
"of nucleotide or protein substitutions per site. "
"Use on trees with longer branches is not recommended for ancestral sequence reconstruction."%(ttconf.MAX_BRANCH_LENGTH), 0, warn=True)
self.prepare_tree()
if self.data:
self._check_alignment_tree_gtr_consistency()
return ttconf.SUCCESS
@property
def one_mutation(self):
"""
Returns
-------
float
inverse of the uncompressed sequence length - length scale for short branches
"""
return 1.0/self.data.full_length if self.data.full_length else np.nan
@one_mutation.setter
def one_mutation(self,om):
self.logger("TreeAnc: one_mutation can't be set",1)
@property
def seq_len(self):
return self.data.full_length
@property
def sequence_length(self):
return self.data.full_length
def _check_alignment_tree_gtr_consistency(self):
'''
For each node of the tree, check whether there is a sequence available
in the alignment and assign this sequence as a character array
'''
if len(self.tree.get_terminals()) != len(self.data.aln):
self.logger(f"**WARNING: Number of tips in tree ({len(self.tree.get_terminals())}) differs from number of sequences in alignment ({len(self.data.aln)})**", 3, warn=True)
failed_leaves= 0
# loop over leaves and assign multiplicities of leaves (e.g. number of identical reads)
for l in self.tree.get_terminals():
if l.name in self.data.seq_multiplicity:
l.count = self.data.seq_multiplicity[l.name]
else:
l.count = 1.0
# loop over tree, and assign sequences
for l in self.tree.find_clades():
if hasattr(l, 'branch_state'): del l.branch_state
if l.name not in self.data.compressed_alignment and l.is_terminal():
self.logger("***WARNING: TreeAnc._check_alignment_tree_gtr_consistency: NO SEQUENCE FOR LEAF: '%s'" % l.name, 0, warn=True)
failed_leaves += 1
if not self.ignore_missing_alns and failed_leaves > self.tree.count_terminals()/3:
raise MissingDataError("TreeAnc._check_alignment_tree_gtr_consistency: At least 30\\% terminal nodes cannot be assigned a sequence!\n"
"Are you sure the alignment belongs to the tree?")
else: # could not assign sequence for internal node - is OK
pass
if failed_leaves:
self.logger("***WARNING: TreeAnc: %d nodes don't have a matching sequence in the alignment."
" POSSIBLE ERROR."%failed_leaves, 0, warn=True)
# extend profile to contain additional unknown characters
extend_profile(self.gtr, [self.data.ref] if self.data.is_sparse
else self.data.aln.values(), logger=self.logger)
self.ok = True
[docs] def prepare_tree(self):
"""
Set link to parent and calculate distance to root for all tree nodes.
Should be run once the tree is read and after every rerooting,
topology change or branch length optimizations.
"""
self.sequence_reconstruction = False
self.tree.root.branch_length = 0.001
self.tree.root.mask = None
self.tree.root.mutation_length = self.tree.root.branch_length
if not self.keep_node_order:
self.tree.ladderize()
self._prepare_nodes()
self._leaves_lookup = {node.name:node for node in self.tree.get_terminals()}
def _prepare_nodes(self):
"""
Set auxilliary parameters to every node of the tree.
"""
self.tree.root.up = None
self.tree.root.tt = self
self.tree.root.bad_branch=self.tree.root.bad_branch if hasattr(self.tree.root, 'bad_branch') else False
name_set = {n.name for n in self.tree.find_clades() if n.name}
internal_node_count = 0
for clade in self.tree.get_nonterminals(order='preorder'): # parents first
if clade.name is None:
tmp = "NODE_" + format(internal_node_count, '07d')
while tmp in name_set:
internal_node_count += 1
tmp = "NODE_" + format(internal_node_count, '07d')
clade.name = tmp
name_set.add(clade.name)
internal_node_count+=1
for c in clade.clades:
c.up = clade
c.tt = self
for clade in self.tree.find_clades(order='postorder'): # children first
if clade.is_terminal():
clade.bad_branch = clade.bad_branch if hasattr(clade, 'bad_branch') else False
else:
clade.bad_branch = all([c.bad_branch for c in clade])
if not hasattr(clade, "mask"):
clade.mask = None
self._calc_dist2root()
self._internal_node_count = max(internal_node_count, self._internal_node_count)
def _calc_dist2root(self):
"""
For each node in the tree, set its root-to-node distance as dist2root
attribute
"""
self.tree.root.dist2root = 0.0
for clade in self.tree.get_nonterminals(order='preorder'): # parents first
for c in clade.clades:
c.dist2root = clade.dist2root + c.mutation_length
####################################################################
## END SET-UP
####################################################################
###################################################################
### ancestral reconstruction
###################################################################
def reconstruct_anc(self,*args, **kwargs):
"""Shortcut for :py:meth:`treetime.TreeAnc.infer_ancestral_sequences`
"""
return self.infer_ancestral_sequences(*args,**kwargs)
[docs] def infer_ancestral_sequences(self, method='probabilistic', infer_gtr=False,
marginal=False, reconstruct_tip_states=False, **kwargs):
"""Reconstruct ancestral sequences
Parameters
----------
method : str
Method to use. Supported values are "parsimony", "fitch", "probabilistic" and "ml"
infer_gtr : bool
Infer a GTR model before reconstructing the sequences
marginal : bool
Assign sequences that are most likely after averaging over all other nodes
instead of the jointly most likely sequences.
reconstruct_tip_states : bool, optional
Reconstruct sequences of terminal nodes/leaves, thereby replacing ambiguous
characters with the inferred base/state. default: False
**kwargs
additional keyword arguments that are passed down to :py:meth:`TreeAnc.infer_gtr` and :py:meth:`TreeAnc._ml_anc`
Returns
-------
N_diff : int
Number of nucleotides different from the previous
reconstruction. If there were no pre-set sequences, returns N*L
"""
if not self.ok:
raise MissingDataError("TreeAnc.infer_ancestral_sequences: ERROR, sequences or tree are missing")
self.logger("TreeAnc.infer_ancestral_sequences with method: %s, %s"%(method, 'marginal' if marginal else 'joint'), 1)
if method.lower() in ['ml', 'probabilistic']:
if marginal:
_ml_anc = self._ml_anc_marginal
else:
_ml_anc = self._ml_anc_joint
elif method.lower() in ['fitch', 'parsimony']:
_ml_anc = self._fitch_anc
else:
raise UnknownMethodError("Reconstruction method needs to be in ['ml', 'probabilistic', 'fitch', 'parsimony'], got '{}'".format(method))
if infer_gtr:
self.infer_gtr(marginal=marginal, **kwargs)
N_diff = _ml_anc(reconstruct_tip_states=reconstruct_tip_states, **kwargs)
else:
N_diff = _ml_anc(reconstruct_tip_states=reconstruct_tip_states, **kwargs)
return N_diff
###################################################################
### FITCH
###################################################################
def _fitch_anc(self, **kwargs):
"""
Reconstruct ancestral states using Fitch's algorithm. It implements
the iteration from leaves to the root constructing the Fitch profiles for
each character of the sequence, and then by propagating from the root
to the leaves, reconstructs the sequences of the internal nodes.
Keyword Args
------------
Returns
-------
Ndiff : int
Number of the characters that changed since the previous
reconstruction. These changes are determined from the pre-set
sequence attributes of the nodes. If there are no sequences available
(i.e., no reconstruction has been made before), returns the total
number of characters in the tree.
"""
# set fitch profiiles to each terminal node
for l in self.tree.get_terminals():
l.state = [[k] for k in l.cseq]
L = self.data.compressed_length
self.logger("TreeAnc._fitch_anc: Walking up the tree, creating the Fitch profiles",2)
for node in self.tree.get_nonterminals(order='postorder'):
node.state = [self._fitch_state(node, k) for k in range(L)]
ambs = [i for i in range(L) if len(self.tree.root.state[i])>1]
if len(ambs) > 0:
for amb in ambs:
self.logger("Ambiguous state of the root sequence "
"in the position %d: %s, "
"choosing %s" % (amb, str(self.tree.root.state[amb]),
self.tree.root.state[amb][0]), 4)
self.tree.root._cseq = np.array([k[self.rng.randint(len(k)) if len(k)>1 else 0]
for k in self.tree.root.state])
self.logger("TreeAnc._fitch_anc: Walking down the self.tree, generating sequences from the "
"Fitch profiles.", 2)
N_diff = 0
for node in self.tree.get_nonterminals(order='preorder'):
if node.up != None: # not root
sequence = np.array([node.up._cseq[i]
if node.up._cseq[i] in node.state[i]
else node.state[i][0] for i in range(L)])
if self.sequence_reconstruction:
N_diff += (sequence!=node.cseq).sum()
else:
N_diff += L
node._cseq = sequence
del node.state # no need to store Fitch states
self.sequence_reconstruction = 'parsimony'
self.logger("Done ancestral state reconstruction",3)
return N_diff
def _fitch_state(self, node, pos):
"""
Determine the Fitch profile for a single character of the node's sequence.
The profile is essentially the intersection between the children's
profiles or, if the former is empty, the union of the profiles.
Parameters
----------
node : PhyloTree.Clade:
Internal node which the profiles are to be determined
pos : int
Position in the node's sequence which the profiles should
be determinedf for.
Returns
-------
state : numpy.array
Fitch profile for the character at position pos of the given node.
"""
state = self._fitch_intersect([k.state[pos] for k in node.clades])
if len(state) == 0:
state = np.concatenate([k.state[pos] for k in node.clades])
return state
def _fitch_intersect(self, arrays):
"""
Find the intersection of any number of 1D arrays.
Return the sorted, unique values that are in all of the input arrays.
Adapted from numpy.lib.arraysetops.intersect1d
"""
def pairwise_intersect(arr1, arr2):
s2 = set(arr2)
b3 = [val for val in arr1 if val in s2]
return b3
arrays = list(arrays) # allow assignment
N = len(arrays)
while N > 1:
arr1 = arrays.pop()
arr2 = arrays.pop()
arr = pairwise_intersect(arr1, arr2)
arrays.append(arr)
N = len(arrays)
return arrays[0]
###################################################################
### Maximum Likelihood
###################################################################
[docs] def sequence_LH(self, pos=None, full_sequence=False):
"""return the likelihood of the observed sequences given the tree
Parameters
----------
pos : int, optional
position in the sequence, if none, the sum over all positions will be returned
full_sequence : bool, optional
does the position refer to the full or compressed sequence, by default compressed sequence is assumed.
Returns
-------
float
likelihood
"""
if not hasattr(self.tree, "total_sequence_LH"):
self.logger("TreeAnc.sequence_LH: you need to run marginal ancestral inference first!", 1)
self.infer_ancestral_sequences(marginal=True)
if pos is not None:
if full_sequence:
compressed_pos = self.data.full_to_compressed_sequence_map[pos]
else:
compressed_pos = pos
return self.tree.sequence_LH[compressed_pos]
else:
return self.tree.total_sequence_LH
def ancestral_likelihood(self):
"""
Calculate the likelihood of the given realization of the sequences in
the tree
Returns
-------
log_lh : float
The tree likelihood given the sequences
"""
log_lh = np.zeros(self.data.multiplicity().shape[0])
for node in self.tree.find_clades(order='postorder'):
if node.up is None: # root node
# 0-1 profile
profile = seq2prof(node.cseq, self.gtr.profile_map)
# get the probabilities to observe each nucleotide
profile *= self.gtr.Pi
profile = profile.sum(axis=1)
log_lh += np.log(profile) # product over all characters
continue
t = node.branch_length
indices = np.array([(self.gtr.state_index[a], self.gtr.state_index[b])
for a, b in zip(node.up.cseq, node.cseq)])
logQt = np.log(self.gtr.expQt(t))
lh = logQt[indices[:, 1], indices[:, 0]]
log_lh += lh
return log_lh
def _branch_length_to_gtr(self, node):
"""
Set branch lengths to either mutation lengths of given branch lengths.
The assigend values are to be used in the following ML analysis.
"""
if self.use_mutation_length:
return max(ttconf.MIN_BRANCH_LENGTH*self.one_mutation, node.mutation_length)
else:
return max(ttconf.MIN_BRANCH_LENGTH*self.one_mutation, node.branch_length)
def _ml_anc_marginal(self, sample_from_profile=False,
reconstruct_tip_states=False, debug=False, **kwargs):
"""
Perform marginal ML reconstruction of the ancestral states. In contrast to
joint reconstructions, this needs to access the probabilities rather than only
log probabilities and is hence handled by a separate function.
Parameters
----------
sample_from_profile : bool or str
assign sequences probabilistically according to the inferred probabilities
of ancestral states instead of to their ML value. This parameter can also
take the value 'root' in which case probabilistic sampling will happen
at the root but at no other node.
reconstruct_tip_states : bool, default False
reconstruct sequence assigned to leaves, will replace ambiguous characters
with the most likely definite character. Note that this will affect the mutations
assigned to branches.
"""
self.logger("TreeAnc._ml_anc_marginal: type of reconstruction: Marginal", 2)
self.postorder_traversal_marginal()
# choose sequence characters from this profile.
# treat root node differently to avoid piling up mutations on the longer branch
if sample_from_profile=='root':
root_sample_from_profile = True
other_sample_from_profile = False
elif isinstance(sample_from_profile, bool):
root_sample_from_profile = sample_from_profile
other_sample_from_profile = sample_from_profile
self.total_LH_and_root_sequence(sample_from_profile=root_sample_from_profile,
assign_sequence=True)
N_diff = self.preorder_traversal_marginal(reconstruct_tip_states=reconstruct_tip_states,
sample_from_profile=other_sample_from_profile,
assign_sequence=True)
self.logger("TreeAnc._ml_anc_marginal: ...done", 3)
self.reconstructed_tip_sequences = reconstruct_tip_states
# do clean-up:
if not debug:
for node in self.tree.find_clades():
try:
del node.marginal_log_Lx
del node.marginal_subtree_LH_prefactor
except:
pass
gc.collect()
self.sequence_reconstruction = 'marginal'
return N_diff
def total_LH_and_root_sequence(self, sample_from_profile=False, assign_sequence=False):
self.logger("Computing root node sequence and total tree likelihood...",3)
# Msg to the root from the distant part (equ frequencies)
if len(self.gtr.Pi.shape)==1:
self.tree.root.marginal_outgroup_LH = np.repeat([self.gtr.Pi], self.data.compressed_length, axis=0)
else:
self.tree.root.marginal_outgroup_LH = np.copy(self.gtr.Pi.T)
self.tree.root.marginal_profile, pre = normalize_profile(self.tree.root.marginal_outgroup_LH*self.tree.root.marginal_subtree_LH)
marginal_LH_prefactor = self.tree.root.marginal_subtree_LH_prefactor + pre
self.tree.sequence_LH = marginal_LH_prefactor
self.tree.total_sequence_LH = (self.tree.sequence_LH*self.data.multiplicity()).sum()
self.tree.sequence_marginal_LH = self.tree.total_sequence_LH
if assign_sequence:
seq, prof_vals, idxs = prof2seq(self.tree.root.marginal_profile,
self.gtr, sample_from_prof=sample_from_profile,
normalize=False, rng=self.rng)
self.tree.root._cseq = seq
def postorder_traversal_marginal(self):
L = self.data.compressed_length
n_states = self.gtr.alphabet.shape[0]
self.logger("Attaching sequence profiles to leafs... ", 3)
# set the leaves profiles. This doesn't ever need to be reassigned for leaves
for leaf in self.tree.get_terminals():
if not hasattr(leaf, "marginal_subtree_LH"):
if leaf.name in self.data.compressed_alignment:
leaf.marginal_subtree_LH = seq2prof(self.data.compressed_alignment[leaf.name], self.gtr.profile_map)
else:
leaf.marginal_subtree_LH = np.ones((L, n_states))
if not hasattr(leaf, "marginal_subtree_LH_prefactor"):
leaf.marginal_subtree_LH_prefactor = np.zeros(L)
self.logger("Postorder: computing likelihoods... ", 3)
# propagate leaves --> root, set the marginal-likelihood messages
for node in self.tree.get_nonterminals(order='postorder'): #leaves -> root
# regardless of what was before, set the profile to ones
tmp_log_subtree_LH = np.zeros((L,n_states), dtype=float)
node.marginal_subtree_LH_prefactor = np.zeros(L, dtype=float)
for ch in node.clades:
if ch.mask is None:
ch.marginal_log_Lx = self.gtr.propagate_profile(ch.marginal_subtree_LH,
self._branch_length_to_gtr(ch), return_log=True) # raw prob to transfer prob up
else:
ch.marginal_log_Lx = (self.gtr.propagate_profile(ch.marginal_subtree_LH,
self._branch_length_to_gtr(ch), return_log=True).T*ch.mask).T # raw prob to transfer prob up
tmp_log_subtree_LH += ch.marginal_log_Lx
node.marginal_subtree_LH_prefactor += ch.marginal_subtree_LH_prefactor
node.marginal_subtree_LH, offset = normalize_profile(tmp_log_subtree_LH, log=True)
node.marginal_subtree_LH_prefactor += offset # and store log-prefactor
def preorder_traversal_marginal(self, reconstruct_tip_states=False, sample_from_profile=False, assign_sequence=False):
self.logger("Preorder: computing marginal profiles...",3)
# propagate root -->> leaves, reconstruct the internal node sequences
# provided the upstream message + the message from the complementary subtree
N_diff = 0
for node in self.tree.find_clades(order='preorder'):
if node.up is None: # skip if node is root
continue
if hasattr(node, 'branch_state'): del node.branch_state
# integrate the information coming from parents with the information
# of all children my multiplying it to the prev computed profile
node.marginal_outgroup_LH, pre = normalize_profile(np.log(np.maximum(ttconf.TINY_NUMBER, node.up.marginal_profile)) - node.marginal_log_Lx,
log=True, return_offset=False)
if node.is_terminal() and (not reconstruct_tip_states): # skip remainder unless leaves are to be reconstructed
continue
tmp_msg_from_parent = self.gtr.evolve(node.marginal_outgroup_LH,
self._branch_length_to_gtr(node), return_log=False)
if node.mask is None:
node.marginal_profile, pre = normalize_profile(node.marginal_subtree_LH * tmp_msg_from_parent, return_offset=False)
else:
node.marginal_profile, pre = normalize_profile(node.marginal_subtree_LH * (node.mask*tmp_msg_from_parent.T + (1.0-node.mask)).T,
return_offset=False)
# choose sequence based maximal marginal LH.
if assign_sequence:
seq, prof_vals, idxs = prof2seq(node.marginal_profile, self.gtr,
sample_from_prof=sample_from_profile,
normalize=False, rng=self.rng)
if self.sequence_reconstruction:
N_diff += (seq!=node.cseq).sum()
else:
N_diff += self.data.compressed_length
#assign new sequence
node._cseq = seq
return N_diff
def _ml_anc_joint(self, sample_from_profile=False,
reconstruct_tip_states=False, debug=False, **kwargs):
"""
Perform joint ML reconstruction of the ancestral states. In contrast to
marginal reconstructions, this only needs to compare and multiply LH and
can hence operate in log space.
Parameters
----------
sample_from_profile : str
This parameter can take the value 'root' in which case probabilistic
sampling will happen at the root. otherwise sequences at ALL nodes are
set to the value that jointly optimized the likelihood.
reconstruct_tip_states : bool, default False
reconstruct sequence assigned to leaves, will replace ambiguous characters
with the most likely definite character. Note that this will affect the mutations
assigned to branches.
"""
N_diff = 0 # number of sites differ from perv reconstruction
L = self.data.compressed_length
n_states = self.gtr.alphabet.shape[0]
self.logger("TreeAnc._ml_anc_joint: type of reconstruction: Joint", 2)
self.logger("TreeAnc._ml_anc_joint: Walking up the tree, computing likelihoods... ", 3)
# for the internal nodes, scan over all states j of this node, maximize the likelihood
for node in self.tree.find_clades(order='postorder'):
if hasattr(node, 'branch_state'): del node.branch_state
if node.up is None:
node.joint_Cx=None # not needed for root
continue
branch_len = self._branch_length_to_gtr(node)
# transition matrix from parent states to the current node states.
# denoted as Pij(i), where j - parent state, i - node state
log_transitions = np.log(np.maximum(ttconf.TINY_NUMBER, self.gtr.expQt(branch_len)))
if node.is_terminal():
if node.name in self.data.compressed_alignment:
tmp_prof = seq2prof(self.data.compressed_alignment[node.name], self.gtr.profile_map)
msg_from_children = np.log(np.maximum(tmp_prof, ttconf.TINY_NUMBER))
else:
msg_from_children = np.zeros((L, n_states))
msg_from_children[np.isnan(msg_from_children) | np.isinf(msg_from_children)] = -ttconf.BIG_NUMBER
else:
# Product (sum-Log) over all child subtree likelihoods.
# this is prod_ch L_x(i)
msg_from_children = np.sum(np.stack([c.joint_Lx for c in node.clades], axis=0), axis=0)
if not debug:
# Now that we have calculated the current node's likelihood
# from its children, clean up likelihood matrices attached
# to children to save memory.
for c in node.clades:
del c.joint_Lx
# for every possible state of the parent node,
# get the best state of the current node
# and compute the likelihood of this state
# preallocate storage
node.joint_Lx = np.zeros((L, n_states)) # likelihood array
node.joint_Cx = np.zeros((L, n_states), dtype=np.uint16) # max LH indices
for char_i, char in enumerate(self.gtr.alphabet):
# Pij(i) * L_ch(i) for given parent state j
# if the node has a mask, P_ij is uniformly 1 at masked positions as no info is propagated
if node.mask is None:
msg_to_parent = (log_transitions[:,char_i].T + msg_from_children)
else:
msg_to_parent = ((log_transitions[:,char_i]*np.repeat([node.mask], self.gtr.n_states, axis=0).T) + msg_from_children)
# For this parent state, choose the best state of the current node
node.joint_Cx[:, char_i] = msg_to_parent.argmax(axis=1)
# and compute the likelihood of the best state of the current node
# given the state of the parent (char_i) -- at masked position, there is no contribution
node.joint_Lx[:, char_i] = msg_to_parent.max(axis=1)
if node.mask is not None:
node.joint_Lx[:, char_i] *= node.mask
# root node profile = likelihood of the total tree
msg_from_children = np.sum(np.stack([c.joint_Lx for c in self.tree.root], axis = 0), axis=0)
# Pi(i) * Prod_ch Lch(i)
self.tree.root.joint_Lx = msg_from_children + np.log(self.gtr.Pi).T
normalized_profile = (self.tree.root.joint_Lx.T - self.tree.root.joint_Lx.max(axis=1)).T
# choose sequence characters from this profile.
# treat root node differently to avoid piling up mutations on the longer branch
if sample_from_profile=='root':
root_sample_from_profile = True
elif isinstance(sample_from_profile, bool):
root_sample_from_profile = sample_from_profile
seq, anc_lh_vals, idxs = prof2seq(np.exp(normalized_profile), self.gtr,
sample_from_prof = root_sample_from_profile,
rng=self.rng)
# compute the likelihood of the most probable root sequence
self.tree.sequence_LH = np.choose(idxs, self.tree.root.joint_Lx.T)
self.tree.sequence_joint_LH = (self.tree.sequence_LH*self.data.multiplicity()).sum()
self.tree.root._cseq = seq
self.tree.root.seq_idx = idxs
self.logger("TreeAnc._ml_anc_joint: Walking down the tree, computing maximum likelihood sequences...",3)
# for each node, resolve the conditioning on the parent node
nodes_to_reconstruct = self.tree.get_nonterminals(order='preorder')
if reconstruct_tip_states:
nodes_to_reconstruct += self.tree.get_terminals()
#TODO: Should we add tips without sequence here?
for node in nodes_to_reconstruct:
# root node has no mutations, everything else has been already set
if node.up is None:
continue
# choose the value of the Cx(i), corresponding to the state of the
# parent node i. This is the state of the current node
node.seq_idx = np.choose(node.up.seq_idx, node.joint_Cx.T)
# reconstruct seq, etc
tmp_sequence = np.choose(node.seq_idx, self.gtr.alphabet)
if self.sequence_reconstruction:
N_diff += (tmp_sequence!=node.cseq).sum()
else:
N_diff += L
node._cseq = tmp_sequence
self.logger("TreeAnc._ml_anc_joint: ...done", 3)
self.reconstructed_tip_sequences = reconstruct_tip_states
# do clean-up
if not debug:
for node in self.tree.find_clades(order='preorder'):
# Check for the likelihood matrix, since we might have cleaned
# it up earlier.
if hasattr(node, "joint_Lx"):
del node.joint_Lx
del node.joint_Cx
if hasattr(node, 'seq_idx'):
del node.seq_idx
self.sequence_reconstruction = 'joint'
return N_diff
###############################################################
### sequence and mutation storing
###############################################################
def get_branch_mutation_matrix(self, node, full_sequence=False):
"""uses results from marginal ancestral inference to return a joint
distribution of the sequence states at both ends of the branch.
Parameters
----------
node : Phylo.clade
node of the tree
full_sequence : bool, optional
expand the sequence to the full sequence, if false (default)
the there will be one mutation matrix for each column in the
compressed alignment
Returns
-------
numpy.array
an Lxqxq stack of matrices (q=alphabet size, L (compressed)sequence length)
"""
pp,pc = self.marginal_branch_profile(node)
# calculate pc_i [e^Qt]_ij pp_j for each site
expQt = self.gtr.expQt(self._branch_length_to_gtr(node)) + ttconf.SUPERTINY_NUMBER
if len(expQt.shape)==3: # site specific model
mut_matrix_stack = np.einsum('ai,aj,ija->aij', pc, pp, expQt)
else:
mut_matrix_stack = np.einsum('ai,aj,ij->aij', pc, pp, expQt)
# normalize this distribution
normalizer = mut_matrix_stack.sum(axis=2).sum(axis=1)
mut_matrix_stack = np.einsum('aij,a->aij', mut_matrix_stack, 1.0/normalizer)
# expand to full sequence if requested
if full_sequence:
return mut_matrix_stack[self.data.full_to_compressed_sequence_map]
else:
return mut_matrix_stack
def marginal_branch_profile(self, node):
'''
calculate the marginal distribution of sequence states on both ends
of the branch leading to node,
Parameters
----------
node : PhyloTree.Clade
TreeNode, attached to the branch.
Returns
-------
pp, pc : Pair of vectors (profile parent, pp) and (profile child, pc)
that are of shape (L,n) where L is sequence length and n is alphabet size.
note that this correspond to the compressed sequences.
'''
parent = node.up
if parent is None:
raise Exception("Branch profiles can't be calculated for the root!")
if not hasattr(node, 'marginal_outgroup_LH'):
raise Exception("marginal ancestral inference needs to be performed first!")
pc = node.marginal_subtree_LH
pp = node.marginal_outgroup_LH
return pp, pc
def add_branch_state(self, node):
"""add a dictionary to the node containing tuples of state pairs
and a list of their number across the branch
Parameters
----------
node : tree.node
attaces attribute :branch_state:
"""
seq_pairs, multiplicity = self.gtr.state_pair(
node.up.cseq, node.cseq,
pattern_multiplicity = self.data.multiplicity(mask=node.mask),
ignore_gaps = self.ignore_gaps)
node.branch_state = {'pair':seq_pairs, 'multiplicity':multiplicity}
###################################################################
### Branch length optimization
###################################################################
def optimize_branch_len(self, **kwargs):
"""Deprecated in favor of 'optimize_branch_lengths_joint'"""
return self.optimize_branch_lengths_joint(**kwargs)
def optimize_branch_len_joint(self, **kwargs):
"""Deprecated in favor of 'optimize_branch_lengths_joint'"""
return self.optimize_branch_lengths_joint(**kwargs)
def optimize_branch_lengths_joint(self, **kwargs):
"""
Perform optimization for the branch lengths of the entire tree.
This method only does a single path and needs to be iterated.
**Note** this method assumes that each node stores information
about its sequence as numpy.array object (node.sequence attribute).
Therefore, before calling this method, sequence reconstruction with
either of the available models must be performed.
Parameters
----------
**kwargs :
Keyword arguments
Keyword Args
------------
store_old : bool
If True, the old lengths will be saved in :code:`node._old_dist` attribute.
Useful for testing, and special post-processing.
"""
self.logger("TreeAnc.optimize_branch_length: running branch length optimization using jointML ancestral sequences",1)
if (self.tree is None) or (self.data.aln is None):
raise MissingDataError("TreeAnc.optimize_branch_length: ERROR, alignment or tree are missing.")
store_old_dist = kwargs['store_old'] if 'store_old' in kwargs else False
max_bl = 0
for node in self.tree.find_clades(order='postorder'):
if node.up is None: continue # this is the root
if store_old_dist:
node._old_length = node.branch_length
new_len = max(0,self.optimal_branch_length(node))
self.logger("Optimization results: old_len=%.4e, new_len=%.4e"
" Updating branch length..."%(node.branch_length, new_len), 5)
node.branch_length = new_len
node.mutation_length=new_len
max_bl = max(max_bl, new_len)
if max_bl>0.15:
self.logger("TreeAnc.optimize_branch_lengths_joint: THIS TREE HAS LONG BRANCHES."
" \n\t ****TreeTime's JOINT IS NOT DESIGNED TO OPTIMIZE LONG BRANCHES."
" \n\t ****PLEASE OPTIMIZE BRANCHES USING: "
" \n\t ****branch_length_mode='input' or 'marginal'", 0, warn=True)
# as branch lengths changed, the distance to root etc need to be recalculated
self.tree.root.up = None
self.tree.root.dist2root = 0.0
self._prepare_nodes()
return ttconf.SUCCESS
def optimal_branch_length(self, node):
'''
Calculate optimal branch length given the sequences of node and parent
Parameters
----------
node : PhyloTree.Clade
TreeNode, attached to the branch.
Returns
-------
new_len : float
Optimal length of the given branch
'''
if node.up is None:
return self.one_mutation
if not hasattr(node, 'branch_state'):
if node.cseq is None and node.is_terminal():
raise MissingDataError("TreeAnc.optimal_branch_length: terminal node alignments required; sequence is missing for leaf: '%s'. "
"Missing terminal sequences can be inferred from sister nodes by rerunning with `reconstruct_tip_states=True` or `--reconstruct-tip-states`" % node.name)
self.add_branch_state(node)
return self.gtr.optimal_t_compressed(node.branch_state['pair'],
node.branch_state['multiplicity'])
def optimal_marginal_branch_length(self, node, tol=1e-10):
'''
calculate the marginal distribution of sequence states on both ends
of the branch leading to node,
Parameters
----------
node : PhyloTree.Clade
TreeNode, attached to the branch.
Returns
-------
branch_length : float
branch length of the branch leading to the node.
note: this can be unstable on iteration
'''
if node.up is None:
return self.one_mutation
else:
pp, pc = self.marginal_branch_profile(node)
return self.gtr.optimal_t_compressed((pp, pc), self.data.multiplicity(mask=node.mask), profiles=True, tol=tol)
def optimize_tree_marginal(self, max_iter=10, infer_gtr=False, pc=1.0, damping=0.75,
LHtol=0.1, site_specific_gtr=False, **kwargs):
self.infer_ancestral_sequences(marginal=True, **kwargs)
oldLH = self.sequence_LH()
self.logger("TreeAnc.optimize_tree_marginal: initial, LH=%1.2f, total branch_length %1.4f"%
(oldLH, self.tree.total_branch_length()), 2)
for i in range(max_iter):
if infer_gtr:
self.infer_gtr(site_specific=site_specific_gtr, marginal=True, normalized_rate=True, pc=pc)
self.infer_ancestral_sequences(marginal=True, **kwargs)
old_bl = self.tree.total_branch_length()
tol = 1e-8 + 0.01**(i+1)
for n in self.tree.find_clades():
if n.up is None:
continue
if n.up.up is None and len(n.up.clades)==2:
# children of a bifurcating root!
n1, n2 = n.up.clades
total_bl = n1.branch_length+n2.branch_length
bl_ratio = n1.branch_length/total_bl
prof_c = n1.marginal_subtree_LH
prof_p = normalize_profile(n2.marginal_subtree_LH*self.tree.root.marginal_outgroup_LH)[0]
if n1.mask is None or n2.mask is None:
new_bl = self.gtr.optimal_t_compressed((prof_p, prof_c), self.data.multiplicity(), profiles=True, tol=tol)
else:
new_bl = self.gtr.optimal_t_compressed((prof_p, prof_c), self.data.multiplicity(mask=n1.mask*n2.mask), profiles=True, tol=tol)
update_val = new_bl*(1-damping**(i+1)) + total_bl*damping**(i+1)
n1.branch_length = update_val*bl_ratio
n2.branch_length = update_val*(1-bl_ratio)
n1.mutation_length = n1.branch_length
n2.mutation_length = n2.branch_length
else:
new_val = self.optimal_marginal_branch_length(n, tol=tol)
update_val = new_val*(1-damping**(i+1)) + n.branch_length*damping**(i+1)
n.branch_length = update_val
n.mutation_length = n.branch_length
self.infer_ancestral_sequences(marginal=True, **kwargs)
LH = self.sequence_LH()
deltaLH = LH - oldLH
oldLH = LH
dbl = self.tree.total_branch_length() - old_bl
self.logger("TreeAnc.optimize_tree_marginal: iteration %d, LH=%1.2f (%1.2f), delta branch_length=%1.4f, total branch_length %1.4f"%
(i, LH, deltaLH, dbl, self.tree.total_branch_length()), 2)
if deltaLH<LHtol:
self.logger("TreeAnc.optimize_tree_marginal: deltaLH=%f, stopping iteration."%deltaLH,1)
break
return ttconf.SUCCESS
def optimize_sequences_and_branch_length(self,*args, **kwargs):
"""This method is a shortcut for :py:meth:`treetime.TreeAnc.optimize_tree`
Deprecated in favor of 'optimize_tree'
"""
self.logger("Deprecation warning: 'optimize_sequences_and_branch_length' will be removed and replaced by 'optimize_tree'!", 1, warn=True)
self.optimize_tree(*args,**kwargs)
def optimize_seq_and_branch_len(self,*args, **kwargs):
"""This method is a shortcut for :py:meth:`treetime.TreeAnc.optimize_tree`
Deprecated in favor of 'optimize_tree'
"""
self.logger("Deprecation warning: 'optimize_seq_and_branch_len' will be removed and replaced by 'optimize_tree'!", 1, warn=True)
self.optimize_tree(*args,**kwargs)
[docs] def optimize_tree(self,prune_short=True, marginal_sequences=False, branch_length_mode='joint',
max_iter=5, infer_gtr=False, pc=1.0, method_anc='probabilistic', **kwargs):
"""
Iteratively set branch lengths and reconstruct ancestral sequences until
the values of either former or latter do not change. The algorithm assumes
knowing only the topology of the tree, and requires that sequences are assigned
to all leaves of the tree.
The first step is to pre-reconstruct ancestral
states using Fitch reconstruction algorithm or ML using existing branch length
estimates. Then, optimize branch lengths and re-do reconstruction until
convergence using ML method.
Parameters
-----------
prune_short : bool
If True, the branches with zero optimal length will be pruned from
the tree, creating polytomies. The polytomies could be further
processed using :py:meth:`treetime.TreeTime.resolve_polytomies` from the TreeTime class.
marginal_sequences : bool
Assign sequences to their marginally most likely value, rather than
the values that are jointly most likely across all nodes.
branch_length_mode : str
'joint', 'marginal', or 'input'. Branch lengths are left unchanged in case
of 'input'. 'joint' and 'marginal' cause branch length optimization
while setting sequences to the ML value or tracing over all possible
internal sequence states.
max_iter : int
Maximal number of times sequence and branch length iteration are optimized
infer_gtr : bool
Infer a GTR model from the observed substitutions.
method_anc: str
Which method should be used to reconstruct ancestral sequences.
Supported values are "parsimony", "fitch", "probabilistic" and "ml"
"""
if branch_length_mode=='marginal':
self.optimize_tree_marginal(max_iter=max_iter, infer_gtr=infer_gtr, pc=pc, **kwargs)
if prune_short:
self.prune_short_branches()
return ttconf.SUCCESS
elif branch_length_mode=='input':
N_diff = self.reconstruct_anc(method=method_anc, infer_gtr=infer_gtr, pc=pc,
marginal=marginal_sequences, **kwargs)
if prune_short:
self.prune_short_branches()
return ttconf.SUCCESS
elif branch_length_mode!='joint':
raise UnknownMethodError("TreeAnc.optimize_tree: `branch_length_mode` should be in ['marginal', 'joint', 'input']")
self.logger("TreeAnc.optimize_tree: sequences...", 1)
N_diff = self.reconstruct_anc(method=method_anc, infer_gtr=infer_gtr, pc=pc,
marginal=marginal_sequences, **kwargs)
self.optimize_branch_lengths_joint(store_old=False)
n = 0
while n<max_iter:
n += 1
if prune_short:
self.prune_short_branches()
N_diff = self.reconstruct_anc(method=method_anc, infer_gtr=False,
marginal=marginal_sequences, **kwargs)
self.logger("TreeAnc.optimize_tree: Iteration %d."
" #Nuc changed since prev reconstructions: %d" %(n, N_diff), 2)
if N_diff < 1:
break
self.optimize_branch_lengths_joint(store_old=False)
self.tree.unconstrained_sequence_LH = (self.tree.sequence_LH*self.data.multiplicity()).sum()
self._prepare_nodes() # fix dist2root and up-links after reconstruction
self.logger("TreeAnc.optimize_tree: Unconstrained sequence LH:%f" % self.tree.unconstrained_sequence_LH , 2)
return ttconf.SUCCESS
[docs] def prune_short_branches(self):
"""
If the branch length is less than the minimal value, remove the branch
from the tree. **Requires** ancestral sequence reconstruction
"""
self.logger("TreeAnc.prune_short_branches: pruning short branches (max prob at zero)...", 1)
for node in self.tree.find_clades():
if node.up is None or node.is_terminal():
continue
# probability of the two seqs separated by zero time is not zero
if ((node.branch_length<0.1*self.one_mutation) and
(self.gtr.prob_t(node.up._cseq, node._cseq, 0.0,
pattern_multiplicity=self.data.multiplicity(mask=node.mask)) > 0.1)):
# re-assign the node children directly to its parent
node.up.clades = [k for k in node.up.clades if k != node] + node.clades
for clade in node.clades:
clade.up = node.up
#####################################################################
## GTR INFERENCE
#####################################################################
[docs] def infer_gtr(self, marginal=False, site_specific=False, normalized_rate=True,
fixed_pi=None, pc=5.0, **kwargs):
"""
Calculates a GTR model given the multiple sequence alignment and the tree.
It performs ancestral sequence inferrence (joint or marginal), followed by
the branch lengths optimization. Then, the numbers of mutations are counted
in the optimal tree and related to the time within the mutation happened.
From these statistics, the relative state transition probabilities are inferred,
and the transition matrix is computed.
The result is used to construct the new GTR model of type 'custom'.
The model is assigned to the TreeAnc and is used in subsequent analysis.
Parameters
-----------
print_raw : bool
If True, print the inferred GTR model
marginal : bool
If True, use marginal sequence reconstruction
normalized_rate : bool
If True, sets the mutation rate prefactor to 1.0.
fixed_pi : np.array
Provide the equilibrium character concentrations.
If None is passed, the concentrations will be inferred from the alignment.
pc: float
Number of pseudo counts to use in gtr inference
Returns
-------
gtr : GTR
The inferred GTR model
"""
if site_specific and self.data.compress:
raise TypeError("TreeAnc.infer_gtr(): sequence compression and site specific GTR models are incompatible!" )
if not self.ok:
raise MissingDataError("TreeAnc.infer_gtr: ERROR, sequences or tree are missing", 0)
# if ancestral sequences are not in place, reconstruct them
if marginal and self.sequence_reconstruction!='marginal':
self._ml_anc_marginal(**kwargs)
elif not self.sequence_reconstruction:
self._ml_anc_joint(**kwargs)
n = self.gtr.n_states
L = len(self.tree.root._cseq)
# matrix of mutations n_{ij}: i = derived state, j=ancestral state
n_ija = np.zeros((n,n,L))
T_ia = np.zeros((n,L))
self.logger("TreeAnc.infer_gtr: counting mutations...", 2)
for node in self.tree.get_nonterminals():
for c in node:
if marginal:
mut_stack = np.transpose(self.get_branch_mutation_matrix(c, full_sequence=False), (1,2,0))
T_ia += 0.5*self._branch_length_to_gtr(c) * mut_stack.sum(axis=0) * self.data.multiplicity(mask=c.mask)
T_ia += 0.5*self._branch_length_to_gtr(c) * mut_stack.sum(axis=1) * self.data.multiplicity(mask=c.mask)
n_ija += mut_stack * self.data.multiplicity(mask=c.mask)
else:
for a,pos, d in c.mutations:
try:
i,j = self.gtr.state_index[d], self.gtr.state_index[a]
except:
# ambiguous positions
continue
cpos = self.data.full_to_compressed_sequence_map[pos]
n_ija[i,j,cpos]+=1
T_ia[j,cpos] += 0.5*self._branch_length_to_gtr(c)
T_ia[i,cpos] -= 0.5*self._branch_length_to_gtr(c)
for i, nuc in enumerate(self.gtr.alphabet):
cseq = c.cseq
if cseq is not None:
ind = cseq==nuc
T_ia[i,ind] += self._branch_length_to_gtr(c)*self.data.multiplicity(mask=c.mask)[ind]
self.logger("TreeAnc.infer_gtr: counting mutations...done", 3)
if site_specific:
if marginal:
root_state = self.tree.root.marginal_profile.T
else:
root_state = seq2prof(self.tree.root.cseq, self.gtr.profile_map).T
self._gtr = GTR_site_specific.infer(n_ija, T_ia, pc=pc,
root_state=root_state, logger=self.logger,
alphabet=self.gtr.alphabet, prof_map=self.gtr.profile_map)
else:
root_state = np.array([np.sum((self.tree.root.cseq==nuc)*self.data.multiplicity(mask=self.tree.root.mask))
for nuc in self.gtr.alphabet])
n_ij = n_ija.sum(axis=-1)
self._gtr = GTR.infer(n_ij, T_ia.sum(axis=-1), root_state, fixed_pi=fixed_pi, pc=pc,
alphabet=self.gtr.alphabet, logger=self.logger,
prof_map = self.gtr.profile_map)
if normalized_rate:
self.logger("TreeAnc.infer_gtr: setting overall rate to 1.0...", 2)
if site_specific:
self._gtr.mu /= self._gtr.average_rate().mean()
else:
self._gtr.mu=1.0
return self._gtr
def infer_gtr_iterative(self, max_iter=10, site_specific=False, LHtol=0.1,
pc=1.0, normalized_rate=False):
"""infer GTR model by iteratively estimating ancestral sequences and the GTR model
Parameters
----------
max_iter : int, optional
maximal number of iterations
site_specific : bool, optional
use a site specific model
LHtol : float, optional
stop iteration when LH improvement falls below this cutoff
pc : float, optional
pseudocount to use
normalized_rate : bool, optional
set the overall rate to 1 (makes sense when optimizing branch lengths as well)
Returns
-------
str
success/failure code
"""
self.infer_ancestral_sequences(marginal=True)
old_p = np.copy(self.gtr.Pi)
old_LH = self.sequence_LH()
for i in range(max_iter):
self.infer_gtr(site_specific=site_specific, marginal=True,
normalized_rate=normalized_rate, pc=pc)
self.infer_ancestral_sequences(marginal=True)
dp = np.abs(self.gtr.Pi - old_p).mean() if self.gtr.Pi.shape==old_p.shape else np.nan
deltaLH = self.sequence_LH() - old_LH
old_p = np.copy(self.gtr.Pi)
old_LH = self.sequence_LH()
self.logger("TreeAnc.infer_gtr_iterative: iteration %d, LH=%1.2f (%1.2f), deltaP=%1.4f"%
(i, old_LH, deltaLH, dp), 2)
if deltaLH<LHtol:
self.logger("TreeAnc.infer_gtr_iterative: deltaLH=%f, stopping iteration."%deltaLH,1)
break
return ttconf.SUCCESS
def optimize_gtr_rate(self):
"""Estimate the overal rate of the GTR model by optimizing the full
likelihood of the sequence data.
"""
from scipy.optimize import minimize_scalar
def cost_func(sqrt_mu):
self.gtr.mu = sqrt_mu**2
self.postorder_traversal_marginal()
self.total_LH_and_root_sequence(sample_from_profile=False,
assign_sequence=False)
return -self.sequence_LH()
old_mu = self.gtr.mu
try:
sol = minimize_scalar(cost_func, bracket=[0.01*np.sqrt(old_mu), np.sqrt(old_mu),100*np.sqrt(old_mu)], method='brent')
except:
self.gtr.mu=old_mu
self.logger('treeanc:optimize_gtr_rate: optimization failed, continuing with previous mu',1,warn=True)
return
if sol['success']:
self.gtr.mu = sol['x']**2
self.logger('treeanc:optimize_gtr_rate: optimization successful. Overall rate estimated to be %f'%self.gtr.mu,1)
else:
self.gtr.mu=old_mu
self.logger('treeanc:optimize_gtr_rate: optimization failed, continuing with previous mu',1,warn=True)
###############################################################################
### Utility functions
###############################################################################
def get_reconstructed_alignment(self, reconstruct_tip_states=False):
"""
Get the multiple sequence alignment, including reconstructed sequences for
the internal nodes.
Parameters
----------
reconstructed_tip_sequences : bool, optional
return reconstructed sequences of terminal nodes. If these have not
been reconstructed yet, this will trigger a rerun of `infer_ancestral_sequences`
Returns
-------
new_aln : MultipleSeqAlignment
Alignment including sequences of all internal nodes
"""
from Bio.Align import MultipleSeqAlignment
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
self.logger("TreeAnc.get_reconstructed_alignment ...",2)
if (not self.sequence_reconstruction) or (reconstruct_tip_states != self.reconstructed_tip_sequences):
self.logger("TreeAnc.reconstructed_alignment... reconstruction not yet done",3)
self.infer_ancestral_sequences(reconstruct_tip_states=reconstruct_tip_states)
if self.data.is_sparse:
new_aln = {'sequences': {n.name: self.data.compressed_to_sparse_sequence(n.cseq)
for n in self.tree.find_clades()}}
new_aln['reference'] = self.data.ref
new_aln['positions'] = self.data.nonref_positions
new_aln['inferred_const_sites'] = self.data.inferred_const_sites
else:
new_aln = MultipleSeqAlignment([SeqRecord(id=n.name,
seq=Seq(self.sequence(n, reconstructed=reconstruct_tip_states,
as_string=True, compressed=False)), description="")
for n in self.tree.find_clades()])
return new_aln
def sequence(self, node, reconstructed=False, as_string=True, compressed=False):
"""return the sequence of a node.
Parameters
----------
node : Phylo.node, str
node in tree
reconstructed : bool, optional
return the reconstructed sequence also for terminal nodes. this will replace
ambiguous sites with the most likely sequence state.
as_string : bool, optional
return the sequence as contiguous string rather than a character array
compressed : bool, optional
return the a sequence where unique alignment patterns are reduced to
one alignment column each
Returns
-------
str or np.array
sequence of node
"""
if type(node)==str:
if node in self.leaves_lookup:
nodes = self.leaves_lookup
else:
raise ValueError("TreeAnc.sequence accepts strings are argument only when the node is terminal and present in the leave lookup table")
if reconstructed and not self.reconstructed_tip_sequences:
raise ValueError("TreeAnc.sequence can only return reconstructed terminal nodes if TreeAnc.infer_ancestral_sequences was run with this the flag `reconstruct_tip_states`.")
if compressed:
if (not reconstructed) and (node.name in self.data.compressed_alignment):
tmp_seq = self.data.compressed_alignment[node.name]
else:
tmp_seq = node.cseq
else:
if (not reconstructed) and (node.name in self.data.aln):
tmp_seq = self.data.aln[node.name]
elif node.cseq is not None:
tmp_seq = self.data.compressed_to_full_sequence(node.cseq, as_string=False)
else:
tmp_seq = np.array([self.gtr.ambiguous or 'N']*self.sequence_length)
return "".join(tmp_seq) if as_string else np.copy(tmp_seq)
[docs] def get_tree_dict(self, keep_var_ambigs=False):
return self.get_reconstructed_alignment()
def recover_var_ambigs(self):
self.logger("TreeAnc: recover_var_ambigs: calls to recover_var_ambigs are no longer necessary since tip states are not inferred unless explicitly specified using `reconstruct_tip_states=True`.", 0, warn=True)
if self.reconstructed_tip_sequences:
self.logger("Your code reconstructed tip states, please change the call of ancestral inference in your code",0, warn=True)
else:
self.logger("Your analysis did not reconstructed tip states, you can remove the call of `recover_var_ambigs`",0, warn=True)