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import numpy as np
import pandas as pd
def residual_filter(tt, n_iqd):
terminals = tt.tree.get_terminals()
clock_rate = tt.clock_model['slope']
icpt = tt.clock_model['intercept']
res = {}
for node in terminals:
if hasattr(node, 'raw_date_constraint') and (node.raw_date_constraint is not None):
res[node] = node.dist2root - clock_rate*np.mean(node.raw_date_constraint) - icpt
residuals = np.array(list(res.values()))
iqd = np.percentile(residuals,75) - np.percentile(residuals,25)
outliers = {}
for node,r in res.items():
if abs(r)>n_iqd*iqd and node.up.up is not None:
node.bad_branch=True
outliers[node.name] = {'tau':(node.dist2root - icpt)/clock_rate, 'avg_date': np.mean(node.raw_date_constraint),
'exact_date': node.raw_date_constraint if type(node) is float else None,
'residual': r/iqd}
else:
node.bad_branch=False
tt.outliers=None
if len(outliers):
outlier_df = pd.DataFrame(outliers).T.loc[:,['avg_date', 'tau', 'residual']]\
.rename(columns={'avg_date':'given_date', 'tau':'apparent_date'})
tt.logger("Clock_filter.residual_filter marked the following outliers:", 2, warn=True)
if tt.verbose>=2:
print(outlier_df)
tt.outliers = outlier_df
return len(outliers)
def local_filter(tt, z_score_threshold):
tt.logger(f"TreeTime.ClockFilter: starting local_outlier_detection", 2)
node_info = collect_node_info(tt)
node_info, z_scale = calculate_node_timings(tt, node_info)
tt.logger(f"TreeTime.ClockFilter: z-scale {z_scale:1.2f}", 2)
outliers = flag_outliers(tt, node_info, z_score_threshold, z_scale)
for n in tt.tree.get_terminals():
if n.name in outliers:
n.bad_branch = True
tt.outliers=None
if len(outliers):
outlier_df = pd.DataFrame(outliers).T.loc[:,['avg_date', 'tau', 'z', 'diagnosis']]\
.rename(columns={'avg_date':'given_date', 'tau':'apparent_date', 'z':'z-score'})
tt.logger("Clock_filter.local_filter marked the following outliers", 2, warn=True)
if tt.verbose>=2:
print(outlier_df)
tt.outliers = outlier_df
return len(outliers)
def flag_outliers(tt, node_info, z_score_threshold, z_scale):
def add_outlier_info(z, n, n_info, parent_tau, mu):
n_info['z'] = z
diagnosis=''
# muts = n_info["nmuts"] if n.is_terminal() else 0.0
# parent_tau = node_info[n.up.name]['tau'] if n.is_terminal() else n_info['tau']
if z<0:
if np.abs(n_info['avg_date']-parent_tau) > n_info["nmuts"]/mu:
diagnosis='date_too_early'
else:
diagnosis = 'excess_mutations'
else:
diagnosis = 'date_too_late'
n_info['diagnosis'] = diagnosis
return n_info
outliers = {}
mu = tt.clock_model['slope']*tt.data.full_length
for n in tt.tree.get_terminals():
if n.up.up is None:
continue # do not label children of the root as bad -- typically a problem with root choice that will be fixed anyway
n_info = node_info[n.name]
parent_tau = node_info[n.up.name]['tau']
if n_info['exact_date']:
z = (n_info['avg_date'] - n_info['tau'])/z_scale
if np.abs(z) > z_score_threshold:
outliers[n.name] = add_outlier_info(z, n, n_info, parent_tau, mu)
elif n.raw_date_constraint and len(n.raw_date_constraint):
zs = [(n_info['tau'] - x)/z_scale for x in n.raw_date_constraint]
if zs[0]*zs[1]>0 and np.min(np.abs(zs))>z_score_threshold:
outliers[n.name] = add_outlier_info(zs[0] if np.abs(zs[0])<np.abs(zs[1]) else zs[1],
n, n_info, parent_tau, mu)
return outliers
def calculate_node_timings(tt, node_info, eps=0.2):
mu = tt.clock_model['slope']*tt.data.full_length
sigma_sq = (3/mu)**2
tt.logger(f"Clockfilter.calculate_node_timings: mu={mu:1.3e}/y, sigma={3/mu:1.3e}y", 2)
for n in tt.tree.find_clades(order='postorder'):
p = node_info[n.name]
if not p['exact_date'] or p['skip']:
continue
if n.is_terminal():
prefactor = (p["observations"]/sigma_sq + mu**2/(p["nmuts"]+eps))
p["a"] = (p["avg_date"]/sigma_sq + mu*p["nmuts"]/(p["nmuts"]+eps))/prefactor
else:
children = [node_info[c.name] for c in n if (not node_info[c.name]['skip']) and node_info[c.name]['exact_date']]
if n==tt.tree.root:
tmp_children_1 = mu*np.sum([(mu*c["a"]-c["nmuts"])/(eps+c["nmuts"]) for c in children])
tmp_children_2 = mu**2*np.sum([(1-c["b"])/(eps+c["nmuts"]) for c in children])
prefactor = (p["observations"]/sigma_sq + tmp_children_2)
p["a"] = (p["observations"]*p["avg_date"]/sigma_sq + tmp_children_1)/prefactor
else:
tmp_children_1 = mu*np.sum([(mu*c["a"]-c["nmuts"])/(eps+c["nmuts"]) for c in children])
tmp_children_2 = mu**2*np.sum([(1-c["b"])/(eps+c["nmuts"]) for c in children])
prefactor = (p["observations"]/sigma_sq + mu**2/(p["nmuts"]+eps) + tmp_children_2)
p["a"] = (p["observations"]*p["avg_date"]/sigma_sq + mu*p["nmuts"]/(p["nmuts"]+eps)+tmp_children_1)/prefactor
p["b"] = mu**2/(p["nmuts"]+eps)/prefactor
node_info[tt.tree.root.name]["tau"] = node_info[tt.tree.root.name]["a"]
## need to deal with tips without exact dates below.
dev = []
for n in tt.tree.get_nonterminals(order='preorder'):
p = node_info[n.name]
for c in n:
c_info = node_info[c.name]
if c_info['skip']:
c_info['tau']=p['tau']
else:
if c_info['exact_date']:
c_info["tau"] = c_info["a"] + c_info["b"]*p["tau"]
else:
c_info["tau"] = p["tau"] + c_info['nmuts']/mu
if c.is_terminal() and c_info['exact_date']:
dev.append(c_info['avg_date']-c_info['tau'])
return node_info, np.std(dev)
def collect_node_info(tt, percentile_for_exact_date=90):
node_info = {}
aln = tt.aln or False
if aln and (not tt.sequence_reconstruction):
tt.infer_ancestral_sequences(infer_gtr=False)
L = tt.data.full_length
date_uncertainty = [np.abs(n.raw_date_constraint[1]-n.raw_date_constraint[0])
if type(n.raw_date_constraint)!=float else 0.0
for n in tt.tree.get_terminals()
if n.raw_date_constraint is not None]
from scipy.stats import scoreatpercentile
uncertainty_cutoff = scoreatpercentile(date_uncertainty, percentile_for_exact_date)*1.01
for n in tt.tree.get_nonterminals(order='postorder'):
parent = {"dates": [], "tips": {}, "skip":False}
exact_dates = 0
for c in n:
if c.is_terminal():
child = {'skip':False}
child["nmuts"] = len([m for m in c.mutations if m[-1] in 'ACGT']) if aln \
else np.round(c.branch_length*L)
if c.raw_date_constraint is None:
child['exact_date'] = False
elif type(c.raw_date_constraint)==float:
child['exact_date'] = True
else:
child['exact_date'] = np.abs(c.raw_date_constraint[1]-c.raw_date_constraint[0])<=uncertainty_cutoff
if child['exact_date']:
exact_dates += 1
if child["nmuts"]==0:
child['skip'] = True
parent["tips"][c.name]={'date': np.mean(c.raw_date_constraint),
'exact_date':child['exact_date']}
else:
child['skip'] = False
child['observations'] = 1
if c.raw_date_constraint is not None:
child["avg_date"] = np.mean(c.raw_date_constraint)
node_info[c.name] = child
else:
if node_info[c.name]['exact_date']:
exact_dates += 1
parent['exact_date'] = exact_dates>0
parent["nmuts"] = len([m for m in n.mutations if m[-1] in 'ACGT']) if aln else np.round(n.branch_length*L)
d = [v['date'] for v in parent['tips'].values() if v['exact_date']]
parent["observations"] = len(d)
parent["avg_date"] = np.mean(d) if len(d) else 0.0
node_info[n.name] = parent
return node_info
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