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genome.py
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genome.py
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"""
Perform manipulations as in tskit documentation:
https://tskit.readthedocs.io/en/stable/data-model.html#node-text-format
Manipulate forward-simulation outputs as in msprime documentation:
https://msprime.readthedocs.io/en/stable/tutorial.html#completing-forwards-simulations
"""
import os
from collections import defaultdict
import numpy as np
import pandas as pd
import tskit
import msprime
output_directory = 'output'
genome_length = 300
N = 100 # historical haploid effective population
def parse_transmission_events():
df = pd.read_csv(os.path.join('output', 'transmission.csv'))
df.loc[pd.isnull(df.transmitter), 'generation'] = -1 # tskit requires time[parent] > time[child]
df['time'] = df.generation.max() - df.generation # coalescent models count time backward from present
return df
def transmission_sampled(df, afp_rate=1/200., es_rate=0.1, es_villages=[]):
sample_s = np.random.random(size=len(df)) < afp_rate # AFP
es_slice = df.loc[df.village.isin(es_villages)]
sample_s[es_slice.index] = np.random.random(size=len(es_slice)) < es_rate # ES
sample_s[pd.isnull(df.transmitter)] = False # ensure initial infections not sampled?
return sample_s
def dump_nodes(df, sample_s, path=os.path.join(output_directory, 'node_table.txt')):
df['is_sample'] = sample_s.astype(int) # tskit.load_text expects 0,1 not False, True
df[['is_sample', 'time']].to_csv(path, index=False, sep='\t')
def populate_node_table(tc, df, sample_s):
df['is_sample'] = sample_s.astype(int) # tskit.load_text expects 0,1 not False, True
for _, row in df.iterrows():
tc.nodes.add_row(flags=int(row.is_sample), time=float(row.time)) # IS_SAMPLE = 1 in bitmask
def dump_edges(df, path=os.path.join(output_directory, 'edge_table.txt')):
valid_edges = df[pd.notnull(df.transmitter)].copy() # exclude "orphan" edges from initial infections
# reshape to names and types expected by tskit.load_text
valid_edges = valid_edges[['transmitter', 'infected']]
valid_edges.columns = ['parent', 'child']
valid_edges['parent'] = valid_edges.parent.astype(int)
# using the whole genome as the transmitted unit (i.e. no recombination)
valid_edges['left'] = 0
valid_edges['right'] = genome_length
valid_edges.to_csv(path, index=False, sep='\t')
def populate_edge_table(tc, df):
for _, row in df[pd.notnull(df.transmitter)].iterrows(): # exclude "orphan" edges
tc.edges.add_row(left=0, right=genome_length, parent=int(row.transmitter), child=int(row.infected))
def load_tree_collection(node_path=os.path.join(output_directory, 'node_table.txt'),
edge_path=os.path.join(output_directory, 'edge_table.txt')):
with open(node_path) as nodes:
with open(edge_path) as edges:
ts = tskit.load_text(nodes=nodes, edges=edges)
return ts
def simplify_tree(ts):
tree = ts.first()
# print(tree.draw(format='unicode')) # don't even think about it!
print('Tree nodes before pruning unsampled lineages: %d' % tree.num_nodes)
simplified_ts = ts.simplify()
simplified_tree = simplified_ts.first()
# print(simplified_tree.draw(format='unicode')) # almost visualizable, but not quite
print('Tree nodes after pruning unsampled lineages: %d' % simplified_tree.num_nodes)
return simplified_ts
def inspect_tree_collection(ts):
print(f"The tree sequence has {ts.num_trees} trees on a genome of length {ts.sequence_length},"
f" {ts.num_individuals} individuals, {ts.num_samples} 'sample' genomes,"
f" and {ts.num_mutations} mutations.")
def draw_tree_to_file(ts, path=os.path.join(output_directory, 'sampled_tree.txt')):
tree = ts.first()
with open(path, 'w') as f:
f.write(tree.draw(format='unicode'))
def dump_tree_to_newick(ts, path=os.path.join(output_directory, 'recapitated_newick.txt')):
if ts.num_trees != 1:
raise Exception('Expecting only to write a single tree.')
tree = ts.first()
with open(path, 'w') as f:
f.write(tree.newick())
def dump_binary_tree(ts, path=os.path.join(output_directory, 'sampled_tree_sequence.trees')):
ts.dump(path)
def load_binary_tree(path=os.path.join(output_directory, 'sampled_tree_sequence.trees')):
return tskit.load(path)
def inspect_degenerate_mutations(ts):
mutations_by_node_position = defaultdict(int) # count mutations by (node, integer_genome_position)
if ts.num_trees != 1:
raise Exception('Not yet implemented loop over trees')
tree = ts.first()
for site in tree.sites():
if len(site.mutations) != 1:
raise Exception('How in infinite-site mode?!')
for mutation in site.mutations:
# print('Mutation @ position {:.2f} over node {} with parent {}'.format(site.position, mutation.node))
mutations_by_node_position[(mutation.node, int(site.position))] += 1
mutations_df = pd.DataFrame.from_dict(mutations_by_node_position, orient='index')
mutations_df.columns = ['n_mutations']
mutations_df.reset_index(inplace=True)
mutations_df[['node', 'site']] = pd.DataFrame(mutations_df['index'].tolist(), index=mutations_df.index)
n_mutations = mutations_df.set_index(['site', 'node']).n_mutations
odd_mutations = n_mutations[n_mutations % 2 == 1].sort_values(ascending=False)
# print(odd_mutations.head(20))
return odd_mutations
def squash_finite_site_mutations(ts, odd_mutations):
"""
Remake SiteTable and MutationTable
based on non-redundant (i.e. odd-numbered) mutations on each edge
by recursively traversing full transmission tree.
Note requirements: https://tskit.readthedocs.io/en/latest/data-model.html#mutation-table
"""
print('Original TreeSequence with %d sites and %d mutations' % (ts.num_sites, ts.num_mutations))
tables = ts.tables # mutable copy of tree sequence
# clear sites + mutations, which we'll discretize + squash
tables.sites.clear()
tables.mutations.clear()
if ts.num_trees != 1:
raise Exception('Not yet implemented loop over trees')
tree = ts.first()
if len(tree.roots) != 1:
raise Exception('Not yet implemented loop over roots')
site_ids = {}
def iter_tree(node, mutation_by_parent):
# get sites of mutations for node
mutations = odd_mutations[odd_mutations.node == node].site.values
# get state of ancestor and record updated state
for position in mutations:
if position not in site_ids:
site_id = tables.sites.add_row(position=position, ancestral_state='0')
site_ids[position] = site_id
# print('Site: position=%d, ancestral_state=%s' % (position, '0'))
parent_state_tuple = mutation_by_parent.get(position, None)
if parent_state_tuple is None:
parent = -1 # NULL parent
child_state = '1'
else:
parent, parent_state = parent_state_tuple
child_state = '1' if parent_state == '0' else '0'
site = site_ids[position] # ID of position in SiteTable
mutation_id = tables.mutations.add_row(site=site, node=node, parent=parent, derived_state=child_state)
# print('Mutation: site=%d, node=%d, parent=%d, derived_state=%s' % (site, node, parent, child_state))
mutation_by_parent[position] = (mutation_id, child_state) # pass updated mutation info to children
if tree.is_leaf(node):
return # dead end
else:
for child in tree.children(node):
iter_tree(child, mutation_by_parent.copy()) # pass copy to not overwrite
iter_tree(tree.root, {}) # begin iteration
tables.sort()
squashed = tables.tree_sequence()
print('Squashed TreeSequence with %d sites and %d mutations' % (squashed.num_sites, squashed.num_mutations))
return squashed
def inspect_variants(ts):
""" Look at sample genotypes by variant """
for variant in ts.variants():
print(variant.site.id, variant.site.position, variant.alleles, variant.genotypes, sep='\t')
if __name__ == '__main__':
# get the output of the toy model
transmissions_df = parse_transmission_events()
# impose AFP + ES random sampling
is_sampled = transmission_sampled(transmissions_df, es_villages=[136, 163])
# print(transmissions_df[is_sampled].head(20))
# ------------
# We can write out files and read them back in as tskit.TreeCollection
# # dump to file
# dump_nodes(transmissions_df, is_sampled)
# dump_edges(transmissions_df)
#
# # load back from file as tskit.TreeCollection
# tree_sequence = load_tree_collection()
# ------------
# Alternatively, we can build it dynamically using the TableCollection API
# (i.e. add_node, add_row)
tables = tskit.TableCollection(sequence_length=genome_length)
populate_node_table(tables, transmissions_df, is_sampled)
populate_edge_table(tables, transmissions_df)
# At least one population for recapitation
tables.populations.add_row()
tables.nodes.set_columns(
flags=tables.nodes.flags,
time=tables.nodes.time,
population=np.zeros_like(tables.nodes.population))
tables.sort() # requires (time[parent], child, left) order
tree_sequence = tables.tree_sequence()
# ------------
# Let's see what we did?
# inspect_tree_collection(tree_sequence)
# Simplify tree based down to sampled lineages
sampled_ts = simplify_tree(tree_sequence)
# Draw ASCII tree to file + view with really tiny font
# draw_tree_to_file(sampled_ts)
# Dump to binary tree format
# dump_binary_tree(sampled_ts)
# Reload binary file to tskit.TreeCollection
# reloaded_ts = load_binary_tree()
# reloaded_tree = reloaded_ts.first()
# print('Reloaded tree has nodes: %d' % reloaded_tree.num_nodes)
# ------------
# Recapitate -- run coalescent model on un-coalesced trees from forward-simulation model
uncoalesced_trees = sum([t.num_roots > 1 for t in sampled_ts.trees()])
print('There are %d of %d uncoalesced trees' % (uncoalesced_trees, sampled_ts.num_trees))
# We are simulating haploid, but msprime Ne is diploid Wright-Fischer, so use N/2
coalesced_ts = msprime.simulate(Ne=N/2, from_ts=sampled_ts)
# draw_tree_to_file(coalesced_ts, path=os.path.join(output_directory, 'recapitated_tree.txt'))
# Write to NEWICK format
# dump_tree_to_newick(coalesced_ts)
# ------------
# Run a mutation model over our full tree to generate genomes
mutated_ts = msprime.mutate(coalesced_ts, rate=0.1) # mutations (in genome of interest) per generation
# Currently only InfiniteSiteMutation model, but open issue with some traction to implement finite genome:
# https://github.com/tskit-dev/msprime/issues/553
print(f"The tree sequence now has {mutated_ts.num_trees} trees,"
f" and {mutated_ts.num_mutations} mutations.")
odd_count_mutations = inspect_degenerate_mutations(mutated_ts)
squashed_ts = squash_finite_site_mutations(mutated_ts, odd_count_mutations.reset_index())
# inspect_variants(squashed_ts)
# with open(os.path.join(output_directory, 'mutated_genomes.fasta'), 'w') as fasta_file:
# squashed_ts.write_fasta(fasta_file) # only in >=0.2.3dev0 (not in 0.2.2)
with open(os.path.join(output_directory, 'mutated_genomes.vcf'), 'w') as vcf_file:
squashed_ts.write_vcf(vcf_file)