def insert_additional_fields(self, keys=[]):
if not len(keys):
keys = self.data.keys()
for key in keys:
e = self.data[key]
if self.engine == 'NT':
freqs_list = sorted([(e[nt], nt) for nt in 'ATCGN'], reverse=True)
elif self.engine == 'AA':
aas = set(codon_to_AA.values())
freqs_list = sorted([(e[aa], aa) for aa in aas], reverse=True)
frequency_of_consensus = freqs_list[0][0]
e['n2n1ratio'] = freqs_list[1][0] / frequency_of_consensus if frequency_of_consensus else -1
e['consensus'] = freqs_list[0][1]
total_frequency_of_all_but_the_consensus = sum([tpl[0] for tpl in freqs_list[1:]])
coverage = total_frequency_of_all_but_the_consensus + frequency_of_consensus
e['departure_from_consensus'] = total_frequency_of_all_but_the_consensus / coverage if coverage else -1
def insert_additional_fields(self, keys=[]):
if not len(keys):
keys = self.data.keys()
for key in keys:
e = self.data[key]
if self.engine == 'NT':
freqs_list = sorted([(e[nt], nt) for nt in 'ATCGN'],
reverse=True)
elif self.engine == 'AA':
aas = set(codon_to_AA.values())
freqs_list = sorted([(e[aa], aa) for aa in aas], reverse=True)
frequency_of_consensus = freqs_list[0][0]
e['n2n1ratio'] = freqs_list[1][
0] / frequency_of_consensus if frequency_of_consensus else -1
e['consensus'] = freqs_list[0][1]
total_frequency_of_all_but_the_consensus = sum(
[tpl[0] for tpl in freqs_list[1:]])
coverage = total_frequency_of_all_but_the_consensus + frequency_of_consensus
e['departure_from_consensus'] = total_frequency_of_all_but_the_consensus / coverage if coverage else -1
#
####################################################################################################
clusterings_table_name = 'clusterings'
clusterings_table_structure = ['clustering', 'newick']
clusterings_table_types = ['str', 'str']
states_table_name = 'states'
states_table_structure = ['name', 'content', 'last_modified']
states_table_types = ['text', 'text', 'text']
variable_aas_table_name = 'variable_amino_acid_frequencies'
variable_aas_table_structure = [
'entry_id', 'sample_id', 'corresponding_gene_call', 'codon_order_in_gene',
'consensus', 'departure_from_consensus', 'coverage'
] + sorted(list(set(codon_to_AA.values())))
variable_aas_table_types = [
'numeric', 'text', 'numeric', 'numeric', 'text', 'numeric', 'numeric'
] + ['numeric'] * len(list(set(codon_to_AA.values())))
variable_nts_table_name = 'variable_nucleotide_positions'
variable_nts_table_structure = [
'entry_id', 'sample_id', 'split_name', 'pos', 'pos_in_contig',
'corresponding_gene_call', 'in_partial_gene_call', 'in_complete_gene_call',
'base_pos_in_codon', 'codon_order_in_gene', 'coverage',
'cov_outlier_in_split', 'cov_outlier_in_contig',
'departure_from_consensus', 'competing_nts', 'consensus', 'A', 'T', 'C',
'G', 'N'
]
variable_nts_table_types = [
'numeric', 'text', 'text', 'numeric', 'numeric', 'numeric', 'numeric',
#################################################################################################### # # TABLE DESCRIPTIONS FOR THE PROFILE DATABASE # #################################################################################################### clusterings_table_name = 'clusterings' clusterings_table_structure = ['clustering', 'newick'] clusterings_table_types = [ 'str' , 'str' ] states_table_name = 'states' states_table_structure = ['name', 'content', 'last_modified'] states_table_types = ['text', 'text' , 'text' ] variable_aas_table_name = 'variable_amino_acid_frequencies' variable_aas_table_structure = ['entry_id', 'sample_id', 'corresponding_gene_call', 'codon_order_in_gene', 'reference', 'departure_from_reference', 'coverage'] + sorted(list(set(codon_to_AA.values()))) variable_aas_table_types = [ 'numeric', 'text' , 'numeric' , 'numeric' , 'text' , 'numeric' , 'numeric' ] + ['numeric'] * len(list(set(codon_to_AA.values()))) variable_nts_table_name = 'variable_nucleotide_positions' variable_nts_table_structure = ['entry_id', 'sample_id', 'split_name', 'pos' , 'pos_in_contig', 'corresponding_gene_call', 'in_partial_gene_call', 'in_complete_gene_call', 'base_pos_in_codon', 'codon_order_in_gene', 'coverage', 'cov_outlier_in_split', 'cov_outlier_in_contig', 'departure_from_reference', 'competing_nts', 'reference', 'A' , 'T' , 'C' , 'G' , 'N' ] variable_nts_table_types = [ 'numeric', 'text' , 'text' , 'numeric', 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'bool' , 'bool' , 'numeric' , 'text' , 'text' , 'numeric', 'numeric', 'numeric', 'numeric', 'numeric'] gene_coverages_table_name = 'gene_coverages' gene_coverages_table_structure = ['entry_id', 'gene_callers_id', 'sample_id', 'mean_coverage'] gene_coverages_table_types = [ 'numeric', 'numeric' , 'text' , 'numeric' ] views_table_name = 'views' views_table_structure = ['view_id', 'target_table'] views_table_types = [ 'str' , 'str' ] # notice that atomic data table is the only table that doesn't have a name. because how we use this table is a bit tricky.
def recover_base_frequencies_for_all_samples(self):
self.progress.new('Recovering AA frequencies for all')
samples_wanted = self.samples_of_interest if self.samples_of_interest else self.sample_ids
splits_wanted = self.splits_of_interest if self.splits_of_interest else set(
self.splits_basic_info.keys())
next_available_entry_id = max(self.data.keys()) + 1
unique_pos_identifier_str_to_consenus_codon = {}
unique_pos_identifier_str_to_unique_pos_identifier = {}
for e in self.data.values():
upi = e['unique_pos_identifier_str']
unique_pos_identifier_str_to_consenus_codon[upi] = e['consensus']
unique_pos_identifier_str_to_unique_pos_identifier[upi] = e[
'unique_pos_identifier']
self.progress.update(
'creating a dict to track missing AA frequencies for each sample / split / pos'
)
splits_to_consider = {}
for split_name in splits_wanted:
splits_to_consider[split_name] = {}
self.progress.update(
'populating the dict to track missing AA frequencies for each sample / split / pos'
)
for entry_id in self.data:
v = self.data[entry_id]
gene_codon_key = '%d_%d' % (v['corresponding_gene_call'],
v['codon_order_in_gene'])
d = splits_to_consider[v['split_name']]
if d.has_key(gene_codon_key):
d[gene_codon_key].remove(v['sample_id'])
else:
d[gene_codon_key] = copy.deepcopy(samples_wanted)
d[gene_codon_key].remove(v['sample_id'])
counter = 0
for split_name in splits_to_consider:
counter += 1
self.progress.update(
'accessing split coverages and updating variable positions dict :: %s'
% pp(counter))
split_coverage_across_samples = self.merged_split_coverage_values.get(
split_name)
split_info = self.splits_basic_info[split_name]
contig_name = split_info['parent']
for gene_codon_key in splits_to_consider[split_name]:
corresponding_gene_call, codon_order_in_gene = [
int(k) for k in gene_codon_key.split('_')
]
for sample_name in splits_to_consider[split_name][
gene_codon_key]:
unique_pos_identifier_str = '_'.join([
split_name,
str(corresponding_gene_call),
str(codon_order_in_gene)
])
consensus_codon = unique_pos_identifier_str_to_consenus_codon[
unique_pos_identifier_str]
self.data[next_available_entry_id] = {
'unique_pos_identifier_str':
unique_pos_identifier_str,
'unique_pos_identifier':
unique_pos_identifier_str_to_unique_pos_identifier[
unique_pos_identifier_str],
'sample_id':
sample_name,
'split_name':
split_name,
'contig_name':
contig_name,
'corresponding_gene_call':
corresponding_gene_call,
'codon_order_in_gene':
codon_order_in_gene,
'departure_from_consensus':
0,
'coverage':
None,
'consensus':
consensus_codon
}
# DEALING WITH COVERAGE ##################################################################
# some very cool but expensive shit is going on here, let me break it down for poor souls of the future.
# what we want to do is to learn the coverage of this codon in the sample. all we have is the corresponding
# gene call id, and the order of this codon in the gene. so here how it goes:
#
# learn the gene call
gene_call = self.genes_in_contigs_dict[
corresponding_gene_call]
# the following dict converts codon orders into nt positions in contig for a geven gene call
codon_order_to_nt_positions_in_contig = utils.get_codon_order_to_nt_positions_dict(
gene_call)
# so the nucleotide positions for this codon in the contig is the following:
nt_positions_for_codon_in_contig = codon_order_to_nt_positions_in_contig[
codon_order_in_gene]
# but we need to convert those positions to the context of this split. so here is the start pos:
split_start = self.splits_basic_info[split_name]['start']
# here we map nt positions from the contig context to split context using the start position
nt_positions_for_codon_in_split = [
p - split_start
for p in nt_positions_for_codon_in_contig
]
# we acquire coverages that match to these positions
coverages = split_coverage_across_samples[sample_name][
nt_positions_for_codon_in_split]
coverage = int(round(sum(coverages) / 3))
# and finally update the data table
self.data[next_available_entry_id]['coverage'] = coverage
# DEALING WITH AAs ##################################################################
# here we need to put all the codons into the data table for this sample
for codon in set(codon_to_AA.values()):
self.data[next_available_entry_id][codon] = 0
# and finally update the frequency of the consensus codon with the coverage (WHICH IS VERY BAD,
# WE HAVE NO CLUE WHAT IS THE ACTUAL COVERAGE OF TRIPLICATE LINKMERS):
self.data[next_available_entry_id][
consensus_codon] = coverage
next_available_entry_id += 1
self.progress.end()
def recover_base_frequencies_for_all_samples(self):
self.progress.new('Recovering AA frequencies for all')
samples_wanted = self.samples_of_interest if self.samples_of_interest else self.sample_ids
splits_wanted = self.splits_of_interest if self.splits_of_interest else set(self.splits_basic_info.keys())
next_available_entry_id = max(self.data.keys()) + 1
unique_pos_identifier_str_to_consenus_codon = {}
unique_pos_identifier_str_to_unique_pos_identifier = {}
for e in self.data.values():
upi = e['unique_pos_identifier_str']
unique_pos_identifier_str_to_consenus_codon[upi] = e['reference']
unique_pos_identifier_str_to_unique_pos_identifier[upi] = e['unique_pos_identifier']
self.progress.update('creating a dict to track missing AA frequencies for each sample / split / pos')
splits_to_consider = {}
for split_name in splits_wanted:
splits_to_consider[split_name] = {}
self.progress.update('populating the dict to track missing AA frequencies for each sample / split / pos')
for entry_id in self.data:
v = self.data[entry_id]
gene_codon_key = '%d_%d' % (v['corresponding_gene_call'], v['codon_order_in_gene'])
d = splits_to_consider[v['split_name']]
if gene_codon_key in d:
d[gene_codon_key].remove(v['sample_id'])
else:
d[gene_codon_key] = copy.deepcopy(samples_wanted)
d[gene_codon_key].remove(v['sample_id'])
counter = 0
for split_name in splits_to_consider:
counter += 1
self.progress.update('accessing split coverages and updating variable positions dict :: %s' % pp(counter))
split_coverage_across_samples = self.merged_split_coverage_values.get(split_name)
split_info = self.splits_basic_info[split_name]
contig_name = split_info['parent']
for gene_codon_key in splits_to_consider[split_name]:
corresponding_gene_call, codon_order_in_gene = [int(k) for k in gene_codon_key.split('_')]
for sample_name in splits_to_consider[split_name][gene_codon_key]:
unique_pos_identifier_str = '_'.join([split_name, str(corresponding_gene_call), str(codon_order_in_gene)])
reference_codon = unique_pos_identifier_str_to_consenus_codon[unique_pos_identifier_str]
self.data[next_available_entry_id] = {'unique_pos_identifier_str': unique_pos_identifier_str,
'unique_pos_identifier': unique_pos_identifier_str_to_unique_pos_identifier[unique_pos_identifier_str],
'sample_id': sample_name,
'split_name': split_name,
'contig_name': contig_name,
'corresponding_gene_call': corresponding_gene_call,
'codon_order_in_gene': codon_order_in_gene,
'departure_from_reference': 0,
'coverage': None,
'reference': reference_codon}
# DEALING WITH COVERAGE ##################################################################
# some very cool but expensive shit is going on here, let me break it down for poor souls of the future.
# what we want to do is to learn the coverage of this codon in the sample. all we have is the corresponding
# gene call id, and the order of this codon in the gene. so here how it goes:
#
# learn the gene call
gene_call = self.genes_in_contigs_dict[corresponding_gene_call]
# the following dict converts codon orders into nt positions in contig for a geven gene call
codon_order_to_nt_positions_in_contig = utils.get_codon_order_to_nt_positions_dict(gene_call)
# so the nucleotide positions for this codon in the contig is the following:
nt_positions_for_codon_in_contig = codon_order_to_nt_positions_in_contig[codon_order_in_gene]
# but we need to convert those positions to the context of this split. so here is the start pos:
split_start = self.splits_basic_info[split_name]['start']
# here we map nt positions from the contig context to split context using the start position
nt_positions_for_codon_in_split = [p - split_start for p in nt_positions_for_codon_in_contig]
# we acquire coverages that match to these positions
coverages = split_coverage_across_samples[sample_name][nt_positions_for_codon_in_split]
coverage = int(round(sum(coverages) / 3))
# and finally update the data table
self.data[next_available_entry_id]['coverage'] = coverage
# DEALING WITH AAs ##################################################################
# here we need to put all the codons into the data table for this sample
for codon in set(codon_to_AA.values()):
self.data[next_available_entry_id][codon] = 0
# and finally update the frequency of the reference codon with the coverage (WHICH IS VERY BAD,
# WE HAVE NO CLUE WHAT IS THE ACTUAL COVERAGE OF TRIPLICATE LINKMERS):
self.data[next_available_entry_id][reference_codon] = coverage
# insert additional fields for this newly added data point
self.insert_additional_fields([next_available_entry_id])
next_available_entry_id += 1
self.progress.end()
#################################################################################################### # # TABLE DESCRIPTIONS FOR THE PROFILE DATABASE # #################################################################################################### clusterings_table_name = 'clusterings' clusterings_table_structure = ['clustering', 'newick'] clusterings_table_types = [ 'str' , 'str' ] states_table_name = 'states' states_table_structure = ['name', 'content', 'last_modified'] states_table_types = ['text', 'text' , 'text' ] variable_aas_table_name = 'variable_amino_acid_frequencies' variable_aas_table_structure = ['entry_id', 'sample_id', 'corresponding_gene_call', 'codon_order_in_gene', 'reference', 'departure_from_reference', 'coverage'] + sorted(list(set(codon_to_AA.values()))) variable_aas_table_types = [ 'numeric', 'text' , 'numeric' , 'numeric' , 'text' , 'numeric' , 'numeric' ] + ['numeric'] * len(list(set(codon_to_AA.values()))) variable_nts_table_name = 'variable_nucleotide_positions' variable_nts_table_structure = ['entry_id', 'sample_id', 'split_name', 'pos' , 'pos_in_contig', 'corresponding_gene_call', 'in_partial_gene_call', 'in_complete_gene_call', 'base_pos_in_codon', 'codon_order_in_gene', 'coverage', 'cov_outlier_in_split', 'cov_outlier_in_contig', 'departure_from_reference', 'competing_nts', 'reference', 'A' , 'T' , 'C' , 'G' , 'N' ] variable_nts_table_types = [ 'numeric', 'text' , 'text' , 'numeric', 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'numeric' , 'bool' , 'bool' , 'numeric' , 'text' , 'text' , 'numeric', 'numeric', 'numeric', 'numeric', 'numeric'] views_table_name = 'views' views_table_structure = ['view_id', 'target_table'] views_table_types = [ 'str' , 'str' ] # notice that atomic data table is the only table that doesn't have a name. because how we use this table is a bit tricky. # for single profiles, contents of this table is stored as "atomic data", however, for merged profiles, # each column of the atomic data table becomes its own table, where the row names remain identical, yet columns # become sample names. atomic_data_table_structure = ['contig', 'std_coverage', 'mean_coverage', 'mean_coverage_Q2Q3', 'max_normalized_ratio', 'relative_abundance', 'detection', 'abundance', 'variability', '__parent__']
