def get_retrieved_by_sample( biom_file, reference_by_obs_id, references_by_sample, uniq_id, uniq_id_by_sample ):
counts_by_sample = dict()
biom = BiomIO.from_json( biom_file )
for sample_name in biom.get_samples_names():
nb_detected = 0
retrieved = dict()
expected_retrieved = dict()
for obs in biom.get_observations_by_sample( sample_name ):
nb_detected += 1
if not "," in reference_by_obs_id[obs['id']]: # Is not a chimera
ref_id = reference_by_obs_id[obs['id']]
retrieved[ref_id] = 1
if ref_id in references_by_sample[sample_name]:
expected_retrieved[ref_id] = 1
# Uniq sequence for retrieved
uniq_retrieved = set()
for ref_id in retrieved:
uniq_retrieved.add( uniq_id[ref_id] )
# Uniq sequence for retrieved
uniq_expected_retrieved = set()
for ref_id in expected_retrieved:
uniq_expected_retrieved.add( uniq_id_by_sample[sample_name][ref_id] )
# Results
counts_by_sample[sample_name] = {
"detected": nb_detected,
"retrieved": len(uniq_retrieved),
"expected_retrieved": len(uniq_expected_retrieved)
}
return counts_by_sample
def process( in_biom, out_biom, out_metadata ):
ordered_blast_keys = ["taxonomy", "subject", "evalue", "perc_identity", "perc_query_coverage", "aln_length"] # Keys in blast_affiliations metadata
taxonomy_depth = 0
unclassified_observations = list()
FH_metadata = open( out_metadata, "w" )
FH_metadata.write( "#OTUID\t" + "\t".join([item for item in ordered_blast_keys]) + "\n" )
biom = BiomIO.from_json( in_biom )
for observation in biom.get_observations():
for metadata_key in observation["metadata"].keys():
if metadata_key == "blast_affiliations": # Extract blast_affiliations metadata in metadata_file
if observation["metadata"][metadata_key] is not None:
for current_affi in observation["metadata"][metadata_key]:
if isinstance(current_affi["taxonomy"], list) or isinstance(current_affi["taxonomy"], tuple):
current_affi["taxonomy"] = ";".join( current_affi["taxonomy"] )
FH_metadata.write( observation["id"] + "\t" + "\t".join([str(current_affi[item]) for item in ordered_blast_keys]) + "\n" )
del observation["metadata"][metadata_key]
elif observation["metadata"][metadata_key] is not None: # All list are transformed in string
if isinstance(observation["metadata"][metadata_key], list) or isinstance(observation["metadata"][metadata_key], tuple):
observation["metadata"][metadata_key] = ";".join( map(str, observation["metadata"][metadata_key]) )
if observation["metadata"].has_key( "blast_taxonomy" ):
if observation["metadata"]["blast_taxonomy"] is None:
unclassified_observations.append( observation["id"] )
observation["metadata"]["taxonomy"] = list()
else:
taxonomy_depth = len(observation["metadata"]["blast_taxonomy"].split(";"))
observation["metadata"]["taxonomy"] = observation["metadata"]["blast_taxonomy"].split(";")
# Add "Unclassified" ranks in unclassified observations
if taxonomy_depth > 0:
for observation_id in unclassified_observations:
observation_metadata = biom.get_observation_metadata(observation_id)
observation_metadata["taxonomy"] = ["Unclassified"] * taxonomy_depth
BiomIO.write( out_biom, biom )
def excluded_obs_on_blastMetrics( input_biom, tag, cmp_operator, threshold, excluded_file ):
"""
@summary: Writes the list of the observations with no affiliations with sufficient blast value.
@param input_biom: [str] The path to the BIOM file to check.
@param tag: [str] The metadata checked.
@param cmp_operator: [str] The operator use in comparison (tag_value ">=" thresold or tag_value "<=" thresold ).
@param threshold: [float] The limit for the tag value.
@param excluded_file: [str] The path to the output file.
"""
valid_operators = {
">=": operator.__ge__,
"<=": operator.__le__
}
cmp_func = valid_operators[cmp_operator]
biom = BiomIO.from_json( input_biom )
FH_excluded_file = open( excluded_file, "w" )
for observation in biom.get_observations():
alignments = observation["metadata"]["blast_affiliations"]
is_discarded = True
for current_alignment in alignments:
if cmp_func(float(current_alignment[tag]), threshold):
is_discarded = False
if is_discarded:
FH_excluded_file.write( str(observation["id"]) + "\n" )
FH_excluded_file.close()
def __init__( self, out_tsv, in_biom, in_fasta=None ):
"""
@param in_biom: [str] Path to BIOM file.
@param out_tsv: [str] Path to output TSV file.
"""
# Sequence file option
sequence_file_opt = "" if in_fasta is None else " --input-fasta " + in_fasta
# Check the metadata
biom = BiomIO.from_json( in_biom )
conversion_tags = ""
if biom.has_observation_metadata( 'rdp_taxonomy' ) and biom.has_observation_metadata( 'rdp_bootstrap' ):
conversion_tags += "'@rdp_tax_and_bootstrap' "
if biom.has_observation_metadata( 'blast_taxonomy' ):
conversion_tags += "'blast_taxonomy' "
if biom.has_observation_metadata( 'blast_affiliations' ):
conversion_tags += "'@blast_subject' "
conversion_tags += "'@blast_perc_identity' "
conversion_tags += "'@blast_perc_query_coverage' "
conversion_tags += "'@blast_evalue' "
conversion_tags += "'@blast_aln_length' "
if biom.has_observation_metadata( 'seed_id' ):
conversion_tags += "'seed_id' "
if in_fasta is not None:
conversion_tags += "'@seed_sequence' "
conversion_tags += "'@observation_name' '@observation_sum' '@sample_count'"
# Set command
Cmd.__init__( self,
'biom2tsv.py',
'Converts a BIOM file in TSV file.',
"--input-file " + in_biom + sequence_file_opt + " --output-file " + out_tsv + " --fields " + conversion_tags,
'--version' )
def getRealTaxByRefID( input_biom, taxonomy_key, duplication_groups ):
"""
@summary: Return taxonomy by reference.
@param input_biom: [str] Path to BIOM file.
@param taxonomy_key: [str] The metadata key for taxonomy.
@param duplication_groups: [dict] By reference ID the list of references with the same sequence.
@return: [dict] List of taxonomies by reference ID.
Example:
{
"MVF01000012.1.1317": [
["Root", "Bacteria", "Proteobacteria", "Gammaproteobacteria", "Enterobacteriales", "Enterobacteriaceae", "Cronobacter", "Escherichia coli BIDMC 73"]
],
"JQ607252.1.1437": [
["Root", "Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Staphylococcaceae", "Staphylococcus", "bacterium NLAE-zl-P471"],
["Root", "Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Staphylococcaceae", "Staphylococcus", "Staphylococcus aureus M17299"]
]
}
"""
taxonomy_by_obs_id = dict()
tmp_taxonomy_by_obs_id = dict()
biom = BiomIO.from_json( input_biom )
for observation in biom.get_observations():
taxonomy_clean = getCleanedTaxonomy(observation["metadata"][taxonomy_key])
taxonomy_by_obs_id[observation["id"]] = [taxonomy_clean]
tmp_taxonomy_by_obs_id[observation["id"]] = taxonomy_clean
if duplication_groups is not None:
for obs_id in duplication_groups:
taxonomy_by_obs_id[obs_id] = list()
for id_duplicated_seq in duplication_groups[obs_id]: # For each duplication group member
taxonomy_by_obs_id[obs_id].append(tmp_taxonomy_by_obs_id[id_duplicated_seq])
return taxonomy_by_obs_id
def sampling_by_sample( input_biom, output_biom, nb_sampled=None, sampled_ratio=None ):
"""
@summary: Writes a BIOM after a random sampling in each sample.
@param input_biom: [str] Path to the processed BIOM.
@param output_biom: [str] Path to outputed BIOM.
@param nb_sampled: [int] Number of sampled sequences by sample.
@param sampled_ratio: [float] Ratio of sampled sequences by sample.
@note: nb_sampled and sampled_ratio are mutually exclusive.
"""
initial_biom = BiomIO.from_json( input_biom )
new_biom = Biom(
matrix_type="sparse",
generated_by="Sampling " + (str(nb_sampled) if nb_sampled is not None else str(sampled_ratio) + "%" ) + " elements by sample from " + input_biom
)
observations_already_added = dict()
for sample_name in initial_biom.get_samples_names():
new_biom.add_sample( sample_name, initial_biom.get_sample_metadata(sample_name) )
sample_seq = initial_biom.get_sample_count(sample_name)
sample_nb_sampled = nb_sampled
if nb_sampled is None:
sample_nb_sampled = int(sample_seq * sampled_ratio)
if sample_seq < nb_sampled:
raise Exception( str(sample_nb_sampled) + " sequences cannot be sampled in sample '" + str(sample_name) + "'. It only contains " + str(sample_seq) + " sequences." )
else:
for current_nb_iter in range(sample_nb_sampled):
# Take an observation in initial BIOM
selected_observation = initial_biom.random_obs_by_sample(sample_name)
selected_observation_id = selected_observation['id']
initial_biom.subtract_count( selected_observation_id, sample_name, 1 )
# Put in new BIOM
if not observations_already_added.has_key(selected_observation_id):
new_biom.add_observation( selected_observation_id, initial_biom.get_observation_metadata(selected_observation_id) )
observations_already_added[selected_observation_id] = True
new_biom.add_count( selected_observation_id, sample_name, 1 )
BiomIO.write( output_biom, new_biom )
def get_checked( abund_file, checked_sample, taxonomy_key, expected_by_depth ):
checked_by_depth = dict()
biom = BiomIO.from_json(abund_file)
for current_obs in biom.get_observations():
clean_taxonomy = getCleanedTaxonomy(current_obs["metadata"][taxonomy_key]) if current_obs["metadata"][taxonomy_key] is not None else ["unknown_taxa"]*len(expected_by_depth)
count = biom.get_count(current_obs["id"], checked_sample)
if count > 0:
if clean_taxonomy[len(clean_taxonomy)-1] == "Multi-affiliation":
nb_selected = 0
selected = list()
taxonomies = list()
expected_taxonomies = expected_by_depth[len(clean_taxonomy)-1]
for affi_idx in range(len(current_obs["metadata"]["blast_affiliations"])):
affi_taxonomy = ";".join(getCleanedTaxonomy(current_obs["metadata"]["blast_affiliations"][affi_idx]["taxonomy"]))
if affi_taxonomy not in taxonomies:
taxonomies.append(affi_taxonomy)
if affi_taxonomy in expected_taxonomies:
selected = getCleanedTaxonomy(current_obs["metadata"]["blast_affiliations"][affi_idx]["taxonomy"])
nb_selected += 1
if nb_selected == 1:
clean_taxonomy = selected
else:
warnings.warn( "Multi-affiliation cannot be resolved for " + str((float(count)*100)/biom.get_total_count()) + "% sequences. Possible taxonomies: '" + "', '".join(taxonomies) + "'." )
for rank_depth in range(len(clean_taxonomy)):
rank_taxonomy = ";".join(clean_taxonomy[:rank_depth + 1])
if rank_depth not in checked_by_depth:
checked_by_depth[rank_depth] = dict()
if rank_taxonomy not in checked_by_depth[rank_depth]:
checked_by_depth[rank_depth][rank_taxonomy] = 0
checked_by_depth[rank_depth][rank_taxonomy] += count
return checked_by_depth
def biom_fasta_to_tsv( input_biom, input_fasta, output_tsv, fields, list_separator ):
"""
@summary: Convert BIOM file to TSV file with sequence.
@param input_biom: [str] Path to the BIOM file.
@param input_fasta: [str] Path to the sequences of the observations.
@param output_tsv: [str] Path to the output file (format : TSV).
@param fields: [list] Columns and their order in output. Special columns : '@observation_name', '@observation_sum', '@sample_count', '@rdp_tax_and_bootstrap', '@seed_sequence'. The others columns must be metadata title.
@param list_separator: [str] Separator for complex metadata.
"""
biom = BiomIO.from_json( input_biom )
out_fh = open( output_tsv, "w" )
sequence_idx = fields.index("@seed_sequence")
# Header
header_parts = header_line_parts( fields, biom )
out_fh.write( "#" + "\t".join(header_parts) + "\n" )
# Data
fields_without_seq = fields
del fields_without_seq[sequence_idx]
FH_in = FastaIO( input_fasta )
for record in FH_in:
obs_idx = biom.find_idx("observation", record.id)
count_by_sample = biom.data.get_row_array(obs_idx)
observation_parts = observation_line_parts( biom.rows[obs_idx], count_by_sample, fields_without_seq, list_separator )
observation_parts.insert( sequence_idx, record.string )
out_fh.write( "\t".join(observation_parts) + "\n" )
out_fh.close()
def observations_depth( input_biom, output_depth ):
"""
@summary : Write the depths of the observation in file.
@param input_biom : [str] path to the biom file processed.
@param output_depth : [str] path to the output file.
@note : Example of one output file
#Depth<TAB>Nb_Observ_concerned<TAB>Prct_Observ_concerned
1<TAB>65<TAB>65.000
2<TAB>30<TAB>30.000
3<TAB>0<TAB>0.000
4<TAB>5<TAB>5.000
"""
obs_depth = list()
nb_observ = 0
# Process depth calculation
biom = BiomIO.from_json( input_biom )
for observation_id, observation_count in biom.get_observations_counts():
while len(obs_depth) <= observation_count:
obs_depth.append(0)
obs_depth[observation_count] += 1
if observation_count != 0:
nb_observ += 1
del biom
# Write output
out_fh = open( output_depth, 'w' )
out_fh.write( "#Depth\tNb_Observ_concerned\tPrct_Observ_concerned\n" )
for depth in range(1, len(obs_depth)):
prct = (float(obs_depth[depth])/ nb_observ)*100
out_fh.write( str(depth) + "\t" + str(obs_depth[depth]) + "\t" + ("%.3f" % prct) + "\n" )
out_fh.close()
def process( in_biom, out_biom, out_metadata ):
ordered_blast_keys = ["taxonomy", "subject", "evalue", "perc_identity", "perc_query_coverage", "aln_length"] # Keys in blast_affiliations metadata
taxonomy_depth = 0
unclassified_observations = list()
FH_metadata = open( out_metadata, "w" )
FH_metadata.write( "#OTUID\t" + "\t".join([item for item in ordered_blast_keys]) + "\n" )
biom = BiomIO.from_json( in_biom )
for observation in biom.get_observations():
for metadata_key in observation["metadata"].keys():
if metadata_key == "blast_affiliations": # Extract blast_affiliations metadata in metadata_file
if observation["metadata"][metadata_key] is not None:
for current_affi in observation["metadata"][metadata_key]:
if isinstance(current_affi["taxonomy"], list) or isinstance(current_affi["taxonomy"], tuple):
current_affi["taxonomy"] = ";".join( current_affi["taxonomy"] )
FH_metadata.write( observation["id"] + "\t" + "\t".join([str(current_affi[item]) for item in ordered_blast_keys]) + "\n" )
del observation["metadata"][metadata_key]
elif observation["metadata"][metadata_key] is not None: # All list are transformed in string
if isinstance(observation["metadata"][metadata_key], list) or isinstance(observation["metadata"][metadata_key], tuple):
observation["metadata"][metadata_key] = ";".join( map(str, observation["metadata"][metadata_key]) )
if observation["metadata"].has_key( "blast_taxonomy" ):
if observation["metadata"]["blast_taxonomy"] is None:
unclassified_observations.append( observation["id"] )
observation["metadata"]["taxonomy"] = list()
else:
taxonomy_depth = len(observation["metadata"]["blast_taxonomy"].split(";"))
observation["metadata"]["taxonomy"] = observation["metadata"]["blast_taxonomy"].split(";")
# Add "Unclassified" ranks in unclassified observations
if taxonomy_depth > 0:
for observation_id in unclassified_observations:
observation_metadata = biom.get_observation_metadata(observation_id)
observation_metadata["taxonomy"] = ["Unclassified"] * taxonomy_depth
BiomIO.write( out_biom, biom )
def biom_fasta_to_tsv( input_biom, input_fasta, output_tsv, fields, list_separator ):
"""
@summary: Convert BIOM file to TSV file with sequence.
@param input_biom: [str] Path to the BIOM file.
@param input_fasta: [str] Path to the sequences of the observations.
@param output_tsv: [str] Path to the output file (format : TSV).
@param fields: [list] Columns and their order in output. Special columns : '@observation_name', '@observation_sum', '@sample_count', '@rdp_tax_and_bootstrap', '@seed_sequence'. The others columns must be metadata title.
@param list_separator: [str] Separator for complex metadata.
"""
biom = BiomIO.from_json( input_biom )
observation_list = [ name for name in biom.get_observations_names()]
out_fh = open( output_tsv, "wt" )
sequence_idx = fields.index("@seed_sequence")
# Header
header_parts = header_line_parts( fields, biom )
out_fh.write( "#" + "\t".join(header_parts) + "\n" )
# Data
fields_without_seq = fields
del fields_without_seq[sequence_idx]
FH_in = FastaIO( input_fasta )
for record in FH_in:
try :
obs_idx = biom.find_idx("observation", record.id)
count_by_sample = biom.data.get_row_array(obs_idx)
observation_parts = observation_line_parts( biom.rows[obs_idx], count_by_sample, fields_without_seq, list_separator )
observation_parts.insert( sequence_idx, record.string )
out_fh.write( "\t".join(observation_parts) + "\n" )
observation_list.remove(record.id)
except:
pass
out_fh.close()
if len(observation_list) > 0:
raise_exception(Exception("\n\n##ERROR : your input fasta file (" + input_fasta + ") does not contain sequence for :" + ", ".join(observation_list) + "\n"))
def get_checked( abund_file, checked_sample, taxonomy_key, expected_by_depth ):
checked_by_depth = dict()
biom = BiomIO.from_json(abund_file)
for current_obs in biom.get_observations():
clean_taxonomy = getCleanedTaxonomy(current_obs["metadata"][taxonomy_key])
count = biom.get_count(current_obs["id"], checked_sample)
if count > 0:
if clean_taxonomy[len(clean_taxonomy)-1] == "Multi-affiliation":
nb_selected = 0
selected = list()
taxonomies = list()
expected_taxonomies = expected_by_depth[len(clean_taxonomy)-1]
for affi_idx in range(len(current_obs["metadata"]["blast_affiliations"])):
affi_taxonomy = ";".join(getCleanedTaxonomy(current_obs["metadata"]["blast_affiliations"][affi_idx]["taxonomy"]))
if affi_taxonomy not in taxonomies:
taxonomies.append(affi_taxonomy)
if affi_taxonomy in expected_taxonomies:
selected = getCleanedTaxonomy(current_obs["metadata"]["blast_affiliations"][affi_idx]["taxonomy"])
nb_selected += 1
if nb_selected == 1:
clean_taxonomy = selected
else:
warnings.warn( "Multi-affiliation cannot be resolved for " + str((float(count)*100)/biom.get_total_count()) + "% sequences. Possible taxonomies: '" + "', '".join(taxonomies) + "'." )
for rank_depth in range(len(clean_taxonomy)):
rank_taxonomy = ";".join(clean_taxonomy[:rank_depth + 1])
if rank_depth not in checked_by_depth:
checked_by_depth[rank_depth] = dict()
if rank_taxonomy not in checked_by_depth[rank_depth]:
checked_by_depth[rank_depth][rank_taxonomy] = 0
checked_by_depth[rank_depth][rank_taxonomy] += count
return checked_by_depth
def get_step_size(self, nb_step=35):
"""
@summary: Returns the step size to obtain 'nb_step' steps or more in 3/4 of samples.
@param nb_step: [int] The number of expected steps.
@returns: [int] The step size.
"""
counts = list()
# Get the number of sequences by sample
biom = BiomIO.from_json( self.in_biom )
for sample_name in biom.get_samples_names():
counts.append( biom.get_sample_count(sample_name) )
del biom
counts = sorted(counts)
nb_samples = len(counts)
# Finds the lower quartile number of sequences
lower_quartile_idx = nb_samples/4
nb_seq = counts[lower_quartile_idx]
# If lower quartile sample is empty
if nb_seq == 0:
idx = 1
while (lower_quartile_idx + idx) < nb_samples and counts[lower_quartile_idx + idx] == 0:
idx += 1
if (lower_quartile_idx + idx) < nb_samples:
nb_seq = counts[lower_quartile_idx + idx]
step_size = int(nb_seq/nb_step)
return max(1, step_size)
def get_step_size(self, nb_step=35):
"""
@summary: Returns the step size to obtain 'nb_step' steps or more in 3/4 of samples.
@param nb_step: [int] The number of expected steps.
@returns: [int] The step size.
"""
counts = list()
# Get the number of sequences by sample
biom = BiomIO.from_json(self.in_biom)
for sample_name in biom.get_samples_names():
counts.append(biom.get_sample_count(sample_name))
del biom
counts = sorted(counts)
nb_samples = len(counts)
# Finds the lower quartile number of sequences
lower_quartile_idx = nb_samples / 4
nb_seq = counts[lower_quartile_idx]
# If lower quartile sample is empty
if nb_seq == 0:
idx = 1
while (lower_quartile_idx +
idx) < nb_samples and counts[lower_quartile_idx + idx] == 0:
idx += 1
if (lower_quartile_idx + idx) < nb_samples:
nb_seq = counts[lower_quartile_idx + idx]
step_size = int(nb_seq / nb_step)
return max(1, step_size)
def aff_to_metadata(reference_file,
biom_in,
biom_out,
blast_files=None,
rdp_files=None):
"""
@summary: Add taxonomy metadata on biom file from a blast result.
@param reference_file: [str] The path to the reference file.
@param biom_in: [str] The path to the Biom file to process.
@param biom_out: [str] The path to the biom output file.
@param blast_files: [list] the list of the path to the blast results in tabular format (outfmt 6 with NCBI Blast+).
@param rdp_files: [list] the list of path to the RDPClassifier results.
"""
# Build an hash with the taxonomy for each gene (key=gene_id ; value=gene_taxonomy)
taxonomy_by_reference = get_tax_from_fasta(reference_file)
# Retrieve blast clusters annotations
cluster_blast_annot = dict()
if blast_files is not None:
cluster_blast_annot = get_bests_blast_affi(blast_files,
taxonomy_by_reference)
del taxonomy_by_reference
# Retrieve rdp clusters annotations
cluster_rdp_annot = dict()
if rdp_files is not None:
cluster_rdp_annot = get_rdp_affi(rdp_files)
# Add metadata to biom
biom = BiomIO.from_json(biom_in)
for cluster in biom.get_observations():
cluster_id = cluster["id"]
# Blast
if blast_files is not None:
blast_taxonomy = None
blast_affiliations = list()
if cluster_blast_annot.has_key(
cluster_id): # Current observation has a match
blast_taxonomy = get_tax_consensus([
alignment['taxonomy'] for alignment in
cluster_blast_annot[cluster_id]['alignments']
])
blast_affiliations = cluster_blast_annot[cluster_id][
'alignments']
biom.add_metadata(cluster_id, "blast_affiliations",
blast_affiliations, "observation")
biom.add_metadata(cluster_id, "blast_taxonomy", blast_taxonomy,
"observation")
# RDP
if rdp_files is not None:
rdp_taxonomy = None
rdp_bootstrap = None
if cluster_rdp_annot.has_key(cluster_id):
rdp_taxonomy = cluster_rdp_annot[cluster_id]['taxonomy']
rdp_bootstrap = cluster_rdp_annot[cluster_id]['bootstrap']
biom.add_metadata(cluster_id, "rdp_taxonomy", rdp_taxonomy,
"observation")
biom.add_metadata(cluster_id, "rdp_bootstrap", rdp_bootstrap,
"observation")
BiomIO.write(biom_out, biom)
def write_log(in_biom, out_biom, log):
FH_log=open(log,"w")
FH_log.write("#sample\tnb_otu_before\tnb_otu_after\n")
initial_biom = BiomIO.from_json( in_biom )
new_biom = BiomIO.from_json( out_biom )
for sample_name in initial_biom.get_samples_names():
nb_otu_before = len(initial_biom.get_sample_obs(sample_name))
nb_otu_after = len(new_biom.get_sample_obs(sample_name))
FH_log.write("Sample name: "+sample_name+"\n\tnb initials OTU: "+str(nb_otu_before)+"\n\tnb normalized OTU: "+str(nb_otu_after)+"\n")
nb_initial_otu=len(initial_biom.rows)
nb_new_otu=len(new_biom.rows)
FH_log.write("Sample name: all samples\n\tnb initials OTU: "+str(nb_initial_otu)+"\n\tnb normalized OTU: "+str(nb_new_otu)+"\n")
FH_log.close()
def get_retrieved_by_sample( biom_file, reference_by_obs_id, references_by_sample, uniq_id, uniq_id_by_sample ):
counts_by_sample = dict()
biom = BiomIO.from_json( biom_file )
for sample_name in biom.get_samples_names():
nb_detected = 0
retrieved = dict()
expected_retrieved = dict()
for obs in biom.get_observations_by_sample( sample_name ):
nb_detected += 1
if not "," in reference_by_obs_id[obs['id']]: # Is not a chimera
ref_id = reference_by_obs_id[obs['id']]
retrieved[ref_id] = 1
if ref_id in references_by_sample[sample_name]:
expected_retrieved[ref_id] = 1
# Uniq sequence for retrieved
uniq_retrieved = set()
for ref_id in retrieved:
uniq_retrieved.add( uniq_id[ref_id] )
# Uniq sequence for retrieved
uniq_expected_retrieved = set()
for ref_id in expected_retrieved:
uniq_expected_retrieved.add( uniq_id_by_sample[sample_name][ref_id] )
# Results
counts_by_sample[sample_name] = {
"detected": nb_detected,
"retrieved": len(uniq_retrieved),
"expected_retrieved": len(uniq_expected_retrieved)
}
return counts_by_sample
def getRealTaxByRefID( input_biom, taxonomy_key, duplication_groups ):
"""
@summary: Return taxonomy by reference.
@param input_biom: [str] Path to BIOM file.
@param taxonomy_key: [str] The metadata key for taxonomy.
@param duplication_groups: [dict] By reference ID the list of references with the same sequence.
@return: [dict] List of taxonomies by reference ID.
Example:
{
"MVF01000012.1.1317": [
["Root", "Bacteria", "Proteobacteria", "Gammaproteobacteria", "Enterobacteriales", "Enterobacteriaceae", "Cronobacter", "Escherichia coli BIDMC 73"]
],
"JQ607252.1.1437": [
["Root", "Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Staphylococcaceae", "Staphylococcus", "bacterium NLAE-zl-P471"],
["Root", "Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Staphylococcaceae", "Staphylococcus", "Staphylococcus aureus M17299"]
]
}
"""
taxonomy_by_obs_id = dict()
tmp_taxonomy_by_obs_id = dict()
biom = BiomIO.from_json( input_biom )
for observation in biom.get_observations():
taxonomy_clean = getCleanedTaxonomy(observation["metadata"][taxonomy_key])
taxonomy_by_obs_id[observation["id"]] = [taxonomy_clean]
tmp_taxonomy_by_obs_id[observation["id"]] = taxonomy_clean
if duplication_groups is not None:
for obs_id in duplication_groups:
taxonomy_by_obs_id[obs_id] = list()
for id_duplicated_seq in duplication_groups[obs_id]: # For each duplication group member
taxonomy_by_obs_id[obs_id].append(tmp_taxonomy_by_obs_id[id_duplicated_seq])
return taxonomy_by_obs_id
def observations_depth( input_biom, output_depth ):
"""
@summary : Write the depths of the observation in file.
@param input_biom : [str] path to the biom file processed.
@param output_depth : [str] path to the output file.
@note : Example of one output file
#Depth<TAB>Nb_Observ_concerned<TAB>Prct_Observ_concerned
1<TAB>65<TAB>65.000
2<TAB>30<TAB>30.000
3<TAB>0<TAB>0.000
4<TAB>5<TAB>5.000
"""
obs_depth = list()
nb_observ = 0
# Process depth calculation
biom = BiomIO.from_json( input_biom )
for observation_id, observation_count in biom.get_observations_counts():
while len(obs_depth) <= observation_count:
obs_depth.append(0)
obs_depth[observation_count] += 1
if observation_count != 0:
nb_observ += 1
del biom
# Write output
out_fh = open( output_depth, 'w' )
out_fh.write( "#Depth\tNb_Observ_concerned\tPrct_Observ_concerned\n" )
for depth in range(1, len(obs_depth)):
prct = (float(obs_depth[depth])/ nb_observ)*100
out_fh.write( str(depth) + "\t" + str(obs_depth[depth]) + "\t" + ("%.3f" % prct) + "\n" )
out_fh.close()
def sampling_by_sample( input_biom, output_biom, nb_sampled=None, sampled_ratio=None ):
"""
@summary: Writes a BIOM after a random sampling in each sample.
@param input_biom: [str] Path to the processed BIOM.
@param output_biom: [str] Path to outputed BIOM.
@param nb_sampled: [int] Number of sampled sequences by sample.
@param sampled_ratio: [float] Ratio of sampled sequences by sample.
@note: nb_sampled and sampled_ratio are mutually exclusive.
"""
initial_biom = BiomIO.from_json( input_biom )
new_biom = Biom(
matrix_type="sparse",
generated_by="Sampling " + (str(nb_sampled) if nb_sampled is not None else str(sampled_ratio) + "%" ) + " elements by sample from " + input_biom
)
observations_already_added = dict()
for sample_name in initial_biom.get_samples_names():
new_biom.add_sample( sample_name, initial_biom.get_sample_metadata(sample_name) )
sample_seq = initial_biom.get_sample_count(sample_name)
sample_nb_sampled = nb_sampled
if nb_sampled is None:
sample_nb_sampled = int(sample_seq * sampled_ratio)
if sample_seq < nb_sampled:
raise Exception( str(sample_nb_sampled) + " sequences cannot be sampled in sample '" + str(sample_name) + "'. It only contains " + str(sample_seq) + " sequences." )
else:
for current_nb_iter in range(sample_nb_sampled):
# Take an observation in initial BIOM
selected_observation = initial_biom.random_obs_by_sample(sample_name)
selected_observation_id = selected_observation['id']
initial_biom.subtract_count( selected_observation_id, sample_name, 1 )
# Put in new BIOM
if not observations_already_added.has_key(selected_observation_id):
new_biom.add_observation( selected_observation_id, initial_biom.get_observation_metadata(selected_observation_id) )
observations_already_added[selected_observation_id] = True
new_biom.add_count( selected_observation_id, sample_name, 1 )
BiomIO.write( output_biom, new_biom )
def samples_hclassification( input_biom, output_newick, distance_method, linkage_method, min_count=1 ):
"""
@summary : Process and write an hierarchical classification from Biom.
@param input_biom : [str] Path to the BIOM file to process.
@param output_newick : [str] Path to the newick output file.
@param distance_method : [str] Used distance method for classify.
@param linkage_method : [str] Used linkage method for classify.
@param min_count : [int] Samples with a count lower than this value are not processed.
"""
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, dendrogram
import scipy.cluster.hierarchy
data_array = list()
processed_samples = list()
excluded_samples = list()
nb_samples = None
# Normalisation on count by sample
biom = BiomIO.from_json( input_biom )
for col_idx, current_sample in enumerate(biom.columns):
sum_on_sample = biom.data.get_col_sum( col_idx )
if sum_on_sample < min_count:
excluded_samples.append( current_sample['id'] )
else:
processed_samples.append( current_sample['id'] )
OTUs_norm = list()
for row_idx in range(len(biom.rows)):
OTUs_norm.append( biom.data.nb_at(row_idx, col_idx)/float(sum_on_sample) )
data_array.append( OTUs_norm )
nb_samples = len(biom.columns)
del biom
# Process distance
if len(processed_samples) < 1:
raise Exception("All samples have a count lower than threshold (" + str(min_count) + ").")
elif len(processed_samples) == 1:
# Write newick
out_fh = open( output_newick, "w" )
out_fh.write( "(" + processed_samples[0] + ");\n" )
out_fh.close()
else:
# Computing the distance and linkage
data_dist = pdist( data_array, distance_method )
data_link = linkage( data_dist, linkage_method )
# Write newick
scipy_hc_tree = scipy.cluster.hierarchy.to_tree( data_link , rd=False )
id_2_name = dict( zip(range(len(processed_samples)), processed_samples) )
out_fh = open( output_newick, "w" )
out_fh.write( to_newick(scipy_hc_tree, id_2_name) + "\n" )
out_fh.close()
# Display log
print "# Hierarchical clustering log:\n" + \
"\tNumber of samples in BIOM: " + str(nb_samples) + "\n" + \
"\tNumber of processed samples: " + str(len(processed_samples))
if nb_samples > len(processed_samples):
print "\n\tExcluded samples (count < " + str(min_count) + "): " + ", ".join(sorted(excluded_samples))
def samples_hclassification( input_biom, output_newick, distance_method, linkage_method, min_count=1 ):
"""
@summary : Process and write an hierarchical classification from Biom.
@param input_biom : [str] Path to the BIOM file to process.
@param output_newick : [str] Path to the newick output file.
@param distance_method : [str] Used distance method for classify.
@param linkage_method : [str] Used linkage method for classify.
@param min_count : [int] Samples with a count lower than this value are not processed.
"""
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, dendrogram
import scipy.cluster.hierarchy
data_array = list()
processed_samples = list()
excluded_samples = list()
nb_samples = None
# Normalisation on count by sample
biom = BiomIO.from_json( input_biom )
for col_idx, current_sample in enumerate(biom.columns):
sum_on_sample = biom.data.get_col_sum( col_idx )
if sum_on_sample < min_count:
excluded_samples.append( current_sample['id'] )
else:
processed_samples.append( current_sample['id'] )
OTUs_norm = list()
for row_idx in range(len(biom.rows)):
OTUs_norm.append( biom.data.nb_at(row_idx, col_idx)/float(sum_on_sample) )
data_array.append( OTUs_norm )
nb_samples = len(biom.columns)
del biom
# Process distance
if len(processed_samples) < 1:
raise_exception( Exception("\n\n#ERROR :All samples have a count lower than threshold (" + str(min_count) + ").\n\n"))
elif len(processed_samples) == 1:
# Write newick
out_fh = open( output_newick, "wt" )
out_fh.write( "(" + processed_samples[0] + ");\n" )
out_fh.close()
else:
# Computing the distance and linkage
data_dist = pdist( data_array, distance_method )
data_link = linkage( data_dist, linkage_method )
# Write newick
scipy_hc_tree = scipy.cluster.hierarchy.to_tree( data_link , rd=False )
id_2_name = dict( list(zip(list(range(len(processed_samples))), processed_samples)) )
out_fh = open( output_newick, "wt" )
out_fh.write( to_newick(scipy_hc_tree, id_2_name) + "\n" )
out_fh.close()
# Display log
print(("# Hierarchical clustering log:\n" + \
"\tNumber of samples in BIOM: " + str(nb_samples) + "\n" + \
"\tNumber of processed samples: " + str(len(processed_samples))))
if nb_samples > len(processed_samples):
print(("\n\tExcluded samples (count < " + str(min_count) + "): " + ", ".join(sorted(excluded_samples))))
def filter_biom( removed_observations, in_biom, out_biom ):
"""
@summary: Removed the specified observations from BIOM.
@param removed_observations: [dict] Each key is an observation name.
@param in_biom: [str]: Path to the processed BIOM file.
@param out_biom: [str]: Path to the cleaned BIOM file.
"""
biom = BiomIO.from_json(in_biom)
biom.remove_observations(removed_observations)
BiomIO.write(out_biom, biom)
def filter_biom( removed_observations, in_biom, out_biom ):
"""
@summary: Removed the specified observations from BIOM.
@param removed_observations: [dict] Each key is an observation name.
@param in_biom: [str]: Path to the processed BIOM file.
@param out_biom: [str]: Path to the cleaned BIOM file.
"""
biom = BiomIO.from_json(in_biom)
biom.remove_observations(removed_observations)
BiomIO.write(out_biom, biom)
def remove_observations( removed_observations, input_biom, output_biom ):
"""
@summary: Removes the specified list of observations.
@param removed_observations: [list] The names of the observations to remove.
@param input_biom: [str] The path to the input BIOM.
@param output_biom: [str] The path to the output BIOM.
"""
biom = BiomIO.from_json( input_biom )
biom.remove_observations( removed_observations )
BiomIO.write( output_biom, biom )
def remove_observations(removed_observations, input_biom, output_biom):
"""
@summary: Removes the specified list of observations.
@param removed_observations: [list] The names of the observations to remove.
@param input_biom: [str] The path to the input BIOM.
@param output_biom: [str] The path to the output BIOM.
"""
biom = BiomIO.from_json(input_biom)
biom.remove_observations(removed_observations)
BiomIO.write(output_biom, biom)
def rarefaction( input_biom, interval=10000, ranks=None, taxonomy_key="taxonomy" ):
"""
@summary: Returns the rarefaction by ranks by samples.
@param input_biom: [str] Path to the biom file processed.
@param interval: [int] Size of first sampling.
@param ranks: [list] The rank(s) level for the diversity.
Example :
Sampled set :
Bacteria; Proteobacteria; Alphaproteobacteria; Sphingomonadales; Sphingomonadaceae; Sphingomonas
Bacteria; Proteobacteria; Gammaproteobacteria; Vibrionales; Vibrionaceae; Vibrio; Vibrio halioticoli
Bacteria; Proteobacteria; Gammaproteobacteria; Legionellales; Coxiellaceae; Coxiella; Ornithodoros moubata symbiont A
Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Burkholderiaceae; Limnobacter; Limnobacter thiooxidans
Result for this set
With rank 1 or 2 : 1 group
With rank 3 : 3 different groups
With rank 4 or 5 or 6 : 4 different groups
@param taxonomy_key : [str] The metadata title for the taxonomy in the input.
@return: [dict] By ranks by samples the list of differents taxa for each steps.
Example :
{
1: {
"sampleA" : [10, 20, 22, 23, 24, 25, 25, 25 ],
"sampleB" : [15, 25, 28, 30, 32, 34, 35, 36, 37, 37, 37, 37]
}
}
@warning: The taxa with name starting with unknown used as complete new name 'unknown'.
"""
sample_rarefaction = dict()
biom = BiomIO.from_json( input_biom )
for current_rank in ranks:
sample_rarefaction[current_rank] = dict()
for sample in biom.get_samples_names():
taxa = dict()
for current_rank in ranks:
sample_rarefaction[current_rank][sample] = list()
taxa[current_rank] = dict()
sample_count = biom.get_sample_count( sample )
expected_nb_iter = int(sample_count/interval)
for current_nb_iter in range(expected_nb_iter):
selected_observations = biom.random_obs_extract_by_sample(sample, interval)
for current_selected in selected_observations:
taxonomy = list()
if taxonomy_key in current_selected['observation']["metadata"] and current_selected['observation']["metadata"][taxonomy_key] is not None:
taxonomy = biom.get_observation_taxonomy( current_selected['observation']["id"], taxonomy_key )
for idx, taxon in enumerate(taxonomy):
if taxon.lower().startswith("unknown"):
taxonomy[idx] = "unknown"
while len(taxonomy) < max(ranks):
taxonomy.append("unknown")
for current_rank in ranks:
taxonomy_str = (';'.join(taxonomy[0:current_rank+1])).lower()
taxa[current_rank][taxonomy_str] = True
for current_rank in ranks:
sample_rarefaction[current_rank][sample].append( str(len(taxa[current_rank])) )
return sample_rarefaction
def rarefaction( input_biom, interval=10000, ranks=None, taxonomy_key="taxonomy" ):
"""
@summary: Returns the rarefaction by ranks by samples.
@param input_biom: [str] Path to the biom file processed.
@param interval: [int] Size of first sampling.
@param ranks: [list] The rank(s) level for the diversity.
Example :
Sampled set :
Bacteria; Proteobacteria; Alphaproteobacteria; Sphingomonadales; Sphingomonadaceae; Sphingomonas
Bacteria; Proteobacteria; Gammaproteobacteria; Vibrionales; Vibrionaceae; Vibrio; Vibrio halioticoli
Bacteria; Proteobacteria; Gammaproteobacteria; Legionellales; Coxiellaceae; Coxiella; Ornithodoros moubata symbiont A
Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Burkholderiaceae; Limnobacter; Limnobacter thiooxidans
Result for this set
With rank 1 or 2 : 1 group
With rank 3 : 3 different groups
With rank 4 or 5 or 6 : 4 different groups
@param taxonomy_key : [str] The metadata title for the taxonomy in the input.
@return: [dict] By ranks by samples the list of differents taxa for each steps.
Example :
{
1: {
"sampleA" : [10, 20, 22, 23, 24, 25, 25, 25 ],
"sampleB" : [15, 25, 28, 30, 32, 34, 35, 36, 37, 37, 37, 37]
}
}
@warning: The taxa with name starting with unknown used as complete new name 'unknown'.
"""
sample_rarefaction = dict()
biom = BiomIO.from_json( input_biom )
for current_rank in ranks:
sample_rarefaction[current_rank] = dict()
for sample in biom.get_samples_names():
taxa = dict()
for current_rank in ranks:
sample_rarefaction[current_rank][sample] = list()
taxa[current_rank] = dict()
sample_count = biom.get_sample_count( sample )
expected_nb_iter = sample_count/interval
for current_nb_iter in range(expected_nb_iter):
selected_observations = biom.random_obs_extract_by_sample(sample, interval)
for current_selected in selected_observations:
taxonomy = list()
if current_selected['observation']["metadata"].has_key(taxonomy_key) and current_selected['observation']["metadata"][taxonomy_key] is not None:
taxonomy = biom.get_observation_taxonomy( current_selected['observation']["id"], taxonomy_key )
for idx, taxon in enumerate(taxonomy):
if taxon.lower().startswith("unknown"):
taxonomy[idx] = "unknown"
while len(taxonomy) < max(ranks):
taxonomy.append("unknown")
for current_rank in ranks:
taxonomy_str = (';'.join(taxonomy[0:current_rank+1])).lower()
taxa[current_rank][taxonomy_str] = True
for current_rank in ranks:
sample_rarefaction[current_rank][sample].append( str(len(taxa[current_rank])) )
return sample_rarefaction
def write_log(in_biom, out_biom, log):
FH_log = open(log, "w")
FH_log.write("#sample\tnb_otu_before\tnb_otu_after\n")
initial_biom = BiomIO.from_json(in_biom)
new_biom = BiomIO.from_json(out_biom)
for sample_name in initial_biom.get_samples_names():
nb_otu_before = len(initial_biom.get_sample_obs(sample_name))
nb_otu_after = len(new_biom.get_sample_obs(sample_name))
FH_log.write("Sample name: " + sample_name + "\n\tnb initials OTU: " +
str(nb_otu_before) + "\n\tnb normalized OTU: " +
str(nb_otu_after) + "\n")
nb_initial_otu = len(initial_biom.rows)
nb_new_otu = len(new_biom.rows)
FH_log.write("Sample name: all samples\n\tnb initials OTU: " +
str(nb_initial_otu) + "\n\tnb normalized OTU: " +
str(nb_new_otu) + "\n")
FH_log.close()
def get_obs_from_biom( in_biom ):
"""
@summary: Returns the counts by observation from a BIOM file.
@param in_biom: Path to the BIOM.
@return: [dict] Returns the counts by observation.
"""
observ_dict = dict()
biom = BiomIO.from_json(in_biom)
for observation_name in biom.get_observations_names():
observ_dict[observation_name] = biom.get_observation_count(observation_name)
del biom
return observ_dict
def get_bootstrap_distrib(input_biom, bootstrap_tag, multiple_tag):
"""
@summary: Returns by taxonomic rank the count (seq and clstr) for the different bootstrap categories.
@param input_biom: The path to the processed BIOM.
@param bootstrap_tag: The metadata tag used in BIOM file to store the taxonomy bootstraps.
@param multiple_tag: The metadata tag used in BIOM file to store the list of possible taxonomies.
@returns: [dict] By taxonomic rank the count for the different bootstrap categories.
Example:
{
"Phylum": {
"80": { "clstr": 1, "seq":100 },
"90": { "clstr": 2, "seq":400 },
"100": { "clstr": 50, "seq":20000 },
},
"Genus":{
"80":{ "clstr": 1, "seq":100 },
"90":{ "clstr": 2, "seq":400 },
"100":{ "clstr": 50, "seq":20000 },
}
}
"""
bootstrap_results = dict()
biom = BiomIO.from_json(input_biom)
for observation in biom.get_observations():
observation_metadata = observation['metadata']
bootstrap = None
if multiple_tag is not None:
if multiple_tag in observation_metadata and observation_metadata[
multiple_tag] is not None and len(
observation_metadata[multiple_tag]) > 0:
bootstrap = observation_metadata[multiple_tag][0][
bootstrap_tag]
else:
if bootstrap_tag in observation_metadata:
bootstrap = observation_metadata[bootstrap_tag]
if bootstrap is not None:
for taxonomy_depth, rank_bootstrap in enumerate(bootstrap):
rank_bootstrap = rank_bootstrap * 100
rank = args.taxonomic_ranks[taxonomy_depth]
if rank not in bootstrap_results:
bootstrap_results[rank] = dict()
if rank_bootstrap not in bootstrap_results[rank]:
bootstrap_results[rank][rank_bootstrap] = {
"clstr": 0,
"seq": 0
}
bootstrap_results[rank][rank_bootstrap]["clstr"] += 1
bootstrap_results[rank][rank_bootstrap][
"seq"] += biom.get_observation_count(observation['id'])
del biom
return bootstrap_results
def get_obs_from_biom(in_biom):
"""
@summary: Returns the counts by observation from a BIOM file.
@param in_biom: Path to the BIOM.
@return: [dict] Returns the counts by observation.
"""
observ_dict = dict()
biom = BiomIO.from_json(in_biom)
for observation_name in biom.get_observations_names():
observ_dict[observation_name] = biom.get_observation_count(
observation_name)
del biom
return observ_dict
def excluded_obs_on_nBiggest( input_biom, nb_selected, excluded_file ):
"""
@summary: Writes the list of all the observations without the n most abundant.
@param input_biom: [str] The path to the BIOM file.
@param threshold: [float] The number of the most abundant observations that will not be written in the excluded list.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json( input_biom )
FH_excluded_file = open( excluded_file, "w" )
sorted_obs_counts = sorted( biom.get_observations_counts(), key=lambda observation: observation[1], reverse=True )
for observation_name, observation_count in sorted_obs_counts[nb_selected:]:
FH_excluded_file.write( observation_name + "\n" )
FH_excluded_file.close()
def getCheckedAbunByRank(real_tax, input_biom, sample, taxonomy_key,
multi_affiliation, logfile):
"""
@summary:
@param real_tax: [dict] Taxonomy by reference IDs.
@param input_biom: [str] Path to BIOM file.
@param sample: [str] sample name.
@param taxonomy_key: [str] The metadata key for taxonomy.
@param multi_affiliation: [bool] ************************************************************************************
@return: [dict] The dictionary of count by taxa in dictionary by rank.
"""
abund_by_rank = list()
tax_list = list()
full_tax_list = list()
nb_seq = 0
biom = BiomIO.from_json(input_biom)
for observation in biom.get_observations():
count = biom.get_count(observation["id"], sample)
if count > 0:
nb_seq += 1
# Get taxonomy
if not multi_affiliation: # Standard affiliation
taxonomy_clean = getCleanedTaxonomy(
observation["metadata"][taxonomy_key])
if ";".join(taxonomy_clean) not in full_tax_list:
full_tax_list.append(";".join(taxonomy_clean))
else: # Multi-affiliation
possible_taxonomies = [
getCleanedTaxonomy(affi["taxonomy"])
for affi in observation["metadata"]["blast_affiliations"]
]
for taxonomy_clean in possible_taxonomies:
if ";".join(taxonomy_clean) not in full_tax_list:
full_tax_list.append(";".join(taxonomy_clean))
taxonomy_clean = selectOneMultiaffiliation(
real_tax, observation["id"], possible_taxonomies, logfile)
if ";".join(taxonomy_clean) not in tax_list:
tax_list.append(";".join(taxonomy_clean))
# Store count
for depth in range(len(taxonomy_clean)):
if len(abund_by_rank) < depth + 1:
abund_by_rank.append(dict())
taxon = ";".join(
taxonomy_clean[:depth + 1]
) # prevent bug with same sp name but with different ancestors
if not abund_by_rank[depth].has_key(taxon):
abund_by_rank[depth][taxon] = 0
abund_by_rank[depth][taxon] += count
return nb_seq, full_tax_list, tax_list, abund_by_rank
def get_results(biom_file):
"""
@summary: Returns the results of the affiliation.
@param biom_file: [str] Path to a BIOM file after affiliation.
@return: [dict] The global results and the sample results.
"""
global_results = {
"nb_clstr": 0,
"nb_seq": 0,
"nb_clstr_with_affi": 0,
"nb_seq_with_affi": 0,
"nb_clstr_ambiguous": list(),
"nb_seq_ambiguous": list(),
}
samples_results = dict()
biom = BiomIO.from_json(biom_file)
for cluster in biom.get_observations():
nb_seq = biom.get_observation_count(cluster["id"])
global_results["nb_clstr"] += 1
global_results["nb_seq"] += nb_seq
if cluster["metadata"]["blast_taxonomy"] is not None:
global_results["nb_clstr_with_affi"] += 1
global_results["nb_seq_with_affi"] += nb_seq
for depth, taxon in enumerate(
cluster["metadata"]["blast_taxonomy"]):
if len(global_results["nb_clstr_ambiguous"]) < (depth + 1):
global_results["nb_clstr_ambiguous"].append(0)
global_results["nb_seq_ambiguous"].append(0)
if taxon == "Multi-affiliation":
global_results["nb_clstr_ambiguous"][depth] += 1
global_results["nb_seq_ambiguous"][depth] += nb_seq
# Samples results
for sample in biom.get_samples_by_observation(cluster["id"]):
sample_name = sample["id"]
if not samples_results.has_key(sample_name):
samples_results[sample_name] = {
"nb_clstr": 0,
"nb_seq": 0,
"nb_clstr_with_affi": 0,
"nb_seq_with_affi": 0
}
count = biom.get_count(cluster["id"], sample_name)
if count > 0:
samples_results[sample_name]["nb_clstr"] += 1
samples_results[sample_name]["nb_seq"] += count
if cluster["metadata"]["blast_taxonomy"] is not None:
samples_results[sample_name]["nb_clstr_with_affi"] += 1
samples_results[sample_name]["nb_seq_with_affi"] += count
return global_results, samples_results
def excluded_obs_on_samplePresence(input_biom, min_sample_presence, excluded_file):
"""
@summary: Writes the list of the observations present in an insufficient number of samples.
@param input_biom: [str] The path to the BIOM file to check.
@param min_sample_presence: [int] The observations present in a number of samples inferior than this value are reported in the excluded file.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json( input_biom )
FH_excluded_file = open( excluded_file, "w" )
for observation_name in biom.get_observations_names():
nb_samples = sum(1 for x in biom.get_samples_by_observation(observation_name))
if nb_samples < min_sample_presence:
FH_excluded_file.write( observation_name + "\n" )
FH_excluded_file.close()
def get_alignment_distrib( input_biom, identity_tag, coverage_tag, multiple_tag ):
"""
@summary: Returns by taxonomic rank the count (seq and clstr) for the different identity/coverage.
@param input_biom: The path to the processed BIOM.
@param identity_tag: The metadata tag used in BIOM file to store the alignment identity.
@param coverage_tag: The metadata tag used in BIOM file to store the alignment query coverage.
@param multiple_tag: The metadata tag used in BIOM file to store the list of possible taxonomies.
@returns: [list] By taxonomic rank the count for the different identity/coverage.
Example:
[
[100, 100, { "clstr": 53, "seq": 20500 }],
[99, 100, { "clstr": 35, "seq": 18000 }],
[90, 95, { "clstr": 1, "seq": 10 }],
]
"""
biom = BiomIO.from_json( input_biom )
aln_results = list()
aln_results_hash = dict()
for observation in biom.get_observations():
observation_metadata = observation['metadata']
identity = None
coverage = None
if args.multiple_tag is not None:
if observation_metadata.has_key(multiple_tag) and len(observation_metadata[multiple_tag]) > 0:
identity = observation_metadata[multiple_tag][0][identity_tag]
coverage = observation_metadata[multiple_tag][0][coverage_tag]
else:
if observation_metadata.has_key(identity_tag) and observation_metadata.has_key(coverage_tag):
identity = observation_metadata[identity_tag]
coverage = observation_metadata[coverage_tag]
if identity is not None:
if not aln_results_hash.has_key( identity ):
aln_results_hash[identity] = dict()
if not aln_results_hash[identity].has_key( coverage ):
aln_results_hash[identity][coverage] = {
"clstr": 0,
"seq": 0
}
aln_results_hash[identity][coverage]["clstr"] += 1
aln_results_hash[identity][coverage]["seq"] += biom.get_observation_count( observation['id'] )
for ident in aln_results_hash.keys():
for cover in aln_results_hash[ident].keys():
aln_results.append([
ident,
cover,
aln_results_hash[ident][cover]
])
del biom
return aln_results
def get_results( biom_file ):
"""
@summary: Returns the results of the affiliation.
@param biom_file: [str] Path to a BIOM file after affiliation.
@return: [dict] The global results and the sample results.
"""
global_results = {
"nb_clstr": 0,
"nb_seq": 0,
"nb_clstr_with_affi": 0,
"nb_seq_with_affi": 0,
"nb_clstr_ambiguous": list(),
"nb_seq_ambiguous": list(),
}
samples_results = dict()
biom = BiomIO.from_json( biom_file )
for cluster in biom.get_observations():
nb_seq = biom.get_observation_count( cluster["id"] )
global_results["nb_clstr"] += 1
global_results["nb_seq"] += nb_seq
if cluster["metadata"]["blast_taxonomy"] is not None:
global_results["nb_clstr_with_affi"] += 1
global_results["nb_seq_with_affi"] += nb_seq
for depth, taxon in enumerate(cluster["metadata"]["blast_taxonomy"]):
if len(global_results["nb_clstr_ambiguous"]) < (depth + 1):
global_results["nb_clstr_ambiguous"].append( 0 )
global_results["nb_seq_ambiguous"].append( 0 )
if taxon == "Multi-affiliation":
global_results["nb_clstr_ambiguous"][depth] += 1
global_results["nb_seq_ambiguous"][depth] += nb_seq
# Samples results
for sample in biom.get_samples_by_observation( cluster["id"] ):
sample_name = sample["id"]
if not samples_results.has_key( sample_name ):
samples_results[sample_name] = {
"nb_clstr": 0,
"nb_seq": 0,
"nb_clstr_with_affi": 0,
"nb_seq_with_affi": 0
}
count = biom.get_count(cluster["id"], sample_name)
if count > 0:
samples_results[sample_name]["nb_clstr"] += 1
samples_results[sample_name]["nb_seq"] += count
if cluster["metadata"]["blast_taxonomy"] is not None:
samples_results[sample_name]["nb_clstr_with_affi"] += 1
samples_results[sample_name]["nb_seq_with_affi"] += count
return global_results, samples_results
def excluded_obs_on_nBiggest(input_biom, nb_selected, excluded_file):
"""
@summary: Writes the list of all the observations without the n most abundant.
@param input_biom: [str] The path to the BIOM file.
@param threshold: [float] The number of the most abundant observations that will not be written in the excluded list.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json(input_biom)
FH_excluded_file = open(excluded_file, "w")
sorted_obs_counts = sorted(biom.get_observations_counts(),
key=lambda observation: observation[1],
reverse=True)
for observation_name, observation_count in sorted_obs_counts[nb_selected:]:
FH_excluded_file.write(observation_name + "\n")
FH_excluded_file.close()
def get_bootstrap_distrib( input_biom, bootstrap_tag, multiple_tag ):
"""
@summary: Returns by taxonomic rank the count (seq and clstr) for the different bootstrap categories.
@param input_biom: The path to the processed BIOM.
@param bootstrap_tag: The metadata tag used in BIOM file to store the taxonomy bootstraps.
@param multiple_tag: The metadata tag used in BIOM file to store the list of possible taxonomies.
@returns: [dict] By taxonomic rank the count for the different bootstrap categories.
Example:
{
"Phylum": {
"80": { "clstr": 1, "seq":100 },
"90": { "clstr": 2, "seq":400 },
"100": { "clstr": 50, "seq":20000 },
},
"Genus":{
"80":{ "clstr": 1, "seq":100 },
"90":{ "clstr": 2, "seq":400 },
"100":{ "clstr": 50, "seq":20000 },
}
}
"""
bootstrap_results = dict()
biom = BiomIO.from_json( input_biom )
for observation in biom.get_observations():
observation_metadata = observation['metadata']
bootstrap = None
if multiple_tag is not None:
if observation_metadata.has_key(multiple_tag) and len(observation_metadata[multiple_tag]) > 0:
bootstrap = observation_metadata[multiple_tag][0][bootstrap_tag]
else:
if observation_metadata.has_key(bootstrap_tag):
bootstrap = observation_metadata[bootstrap_tag]
if bootstrap is not None:
for taxonomy_depth, rank_bootstrap in enumerate( bootstrap ):
rank_bootstrap = rank_bootstrap * 100
rank = args.taxonomic_ranks[taxonomy_depth]
if not bootstrap_results.has_key(rank):
bootstrap_results[rank] = dict()
if not bootstrap_results[rank].has_key(rank_bootstrap):
bootstrap_results[rank][rank_bootstrap] = {
"clstr": 0,
"seq": 0
}
bootstrap_results[rank][rank_bootstrap]["clstr"] += 1
bootstrap_results[rank][rank_bootstrap]["seq"] += biom.get_observation_count( observation['id'] )
del biom
return bootstrap_results
def get_alignment_distrib( input_biom, identity_tag, coverage_tag, multiple_tag ):
"""
@summary: Returns by taxonomic rank the count (seq and clstr) for the different identity/coverage.
@param input_biom: The path to the processed BIOM.
@param identity_tag: The metadata tag used in BIOM file to store the alignment identity.
@param coverage_tag: The metadata tag used in BIOM file to store the alignment query coverage.
@param multiple_tag: The metadata tag used in BIOM file to store the list of possible taxonomies.
@returns: [list] By taxonomic rank the count for the different identity/coverage.
Example:
[
[100, 100, { "clstr": 53, "seq": 20500 }],
[99, 100, { "clstr": 35, "seq": 18000 }],
[90, 95, { "clstr": 1, "seq": 10 }],
]
"""
biom = BiomIO.from_json( input_biom )
aln_results = list()
aln_results_hash = dict()
for observation in biom.get_observations():
observation_metadata = observation['metadata']
identity = 0
coverage = 0
if args.multiple_tag is not None:
if observation_metadata.has_key(multiple_tag) and len(observation_metadata[multiple_tag]) > 0:
identity = observation_metadata[multiple_tag][0][identity_tag]
coverage = observation_metadata[multiple_tag][0][coverage_tag]
else:
if observation_metadata.has_key(identity_tag) and observation_metadata.has_key(coverage_tag):
identity = observation_metadata[identity_tag]
coverage = observation_metadata[coverage_tag]
if not aln_results_hash.has_key( identity ):
aln_results_hash[identity] = dict()
if not aln_results_hash[identity].has_key( coverage ):
aln_results_hash[identity][coverage] = {
"clstr": 0,
"seq": 0
}
aln_results_hash[identity][coverage]["clstr"] += 1
aln_results_hash[identity][coverage]["seq"] += biom.get_observation_count( observation['id'] )
for ident in aln_results_hash.keys():
for cover in aln_results_hash[ident].keys():
aln_results.append([
ident,
cover,
aln_results_hash[ident][cover]
])
del biom
return aln_results
def excluded_obs_on_samplePresence(input_biom, min_sample_presence,
excluded_file):
"""
@summary: Writes the list of the observations present in an insufficient number of samples.
@param input_biom: [str] The path to the BIOM file to check.
@param min_sample_presence: [int] The observations present in a number of samples inferior than this value are reported in the excluded file.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json(input_biom)
FH_excluded_file = open(excluded_file, "w")
for observation_name in biom.get_observations_names():
nb_samples = sum(
1 for x in biom.get_samples_by_observation(observation_name))
if nb_samples < min_sample_presence:
FH_excluded_file.write(observation_name + "\n")
FH_excluded_file.close()
def get_tree_with_count( input_biom, compress=False, taxonomy_key="taxonomy" ):
"""
@summary: Returns the tree of taxa and their counts by sample from BIOM.
@param input_biom: [str] Path to the BIOM file processed.
@param compress: [bool] if true the samples names are replaced by samples index.
@param taxonomy_key: [str] The metadata title for the taxonomy in biom.
@return: [list] The tree generated and the ordered list of samples names (usefull to retrieve name by index if you use compress).
"""
ordered_samples_names = list()
tree = Node("root")
biom = BiomIO.from_json( input_biom )
for sample_name in biom.get_samples_names():
ordered_samples_names.append( sample_name )
sample_id = None if not compress else (len(ordered_samples_names)-1)
update_tree_for_sample( biom, tree, sample_name, taxonomy_key, sample_id )
return tree, ordered_samples_names
def get_tree_with_count( input_biom, compress=False, taxonomy_key="taxonomy" ):
"""
@summary: Returns the tree of taxa and their counts by sample from BIOM.
@param input_biom: [str] Path to the BIOM file processed.
@param compress: [bool] if true the samples names are replaced by samples index.
@param taxonomy_key: [str] The metadata title for the taxonomy in biom.
@return: [list] The tree generated and the ordered list of samples names (usefull to retrieve name by index if you use compress).
"""
ordered_samples_names = list()
tree = Node("root")
biom = BiomIO.from_json( input_biom )
for sample_name in biom.get_samples_names():
ordered_samples_names.append( sample_name )
sample_id = None if not compress else (len(ordered_samples_names)-1)
update_tree_for_sample( biom, tree, sample_name, taxonomy_key, sample_id )
return tree, ordered_samples_names
def __init__(self, out_tsv, in_biom, in_fasta=None):
"""
@param in_biom: [str] Path to BIOM file.
@param out_tsv: [str] Path to output TSV file.
"""
# Sequence file option
sequence_file_opt = "" if in_fasta is None else " --input-fasta " + in_fasta
# Check the metadata
biom = BiomIO.from_json(in_biom)
obs = biom.rows[0]
conversion_tags = ""
if biom.has_observation_metadata('comment'):
conversion_tags += "'comment' "
if biom.has_observation_metadata(
'rdp_taxonomy') and biom.has_observation_metadata(
'rdp_bootstrap'):
conversion_tags += "'@rdp_tax_and_bootstrap' "
if biom.has_observation_metadata('blast_taxonomy'):
conversion_tags += "'blast_taxonomy' "
if biom.has_observation_metadata('blast_affiliations'):
conversion_tags += "'@blast_subject' "
conversion_tags += "'@blast_perc_identity' "
conversion_tags += "'@blast_perc_query_coverage' "
conversion_tags += "'@blast_evalue' "
conversion_tags += "'@blast_aln_length' "
if biom.has_observation_metadata('seed_id'):
conversion_tags += "'seed_id' "
if in_fasta is not None:
conversion_tags += "'@seed_sequence' "
frogs_metadata = [
"comment", "rdp_taxonomy", "rdp_bootstrap", "blast_taxonomy",
"blast_affiliations", "seed_id"
]
if biom.get_observation_metadata(obs["id"]) != None:
for metadata in biom.get_observation_metadata(obs["id"]):
if metadata not in frogs_metadata:
conversion_tags += "'" + metadata + "' "
conversion_tags += "'@observation_name' '@observation_sum' '@sample_count'"
# Set command
Cmd.__init__(
self, 'biom2tsv.py', 'Converts a BIOM file in TSV file.',
"--input-file " + in_biom + sequence_file_opt + " --output-file " +
out_tsv + " --fields " + conversion_tags, '--version')
def getCheckedAbunByRank( real_tax, input_biom, sample, taxonomy_key, multi_affiliation, duplication_groups ):
"""
@summary:
@param real_tax: [dict] Taxonomy by reference IDs.
@param input_biom: [str] Path to BIOM file.
@param sample: [str] sample name.
@param taxonomy_key: [str] The metadata key for taxonomy.
@param multi_affiliation: [bool] ************************************************************************************
@param duplication_groups: [dict] By reference ID the list of IDs for references with the same sequence.
@return: [dict] The dictionary of count by taxa in dictionary by rank.
"""
abund_by_rank = list()
biom = BiomIO.from_json( input_biom )
for observation in biom.get_observations():
count = biom.get_count( observation["id"], sample )
if count > 0:
# Get taxonomy
ref_id = observation["metadata"]["grinder_source"]
taxonomy_clean = getCleanedTaxonomy(observation["metadata"][taxonomy_key])
if not multi_affiliation: # Standard affiliation
if not "," in ref_id: # Non chimera
if taxIsRetrieved(real_tax[ref_id], [taxonomy_clean]):
taxonomy_clean = real_tax[ref_id][0]
else: # Multi-affiliation
if not "," in ref_id: # Non chimera
subjects_ids = [affi["subject"] for affi in observation["metadata"]["blast_affiliations"]]
possible_taxonomies = [";".join(getCleanedTaxonomy(affi["taxonomy"])) for affi in observation["metadata"]["blast_affiliations"]]
# Manage ambiguity
if refIDIsRetrieved(ref_id, subjects_ids, duplication_groups):
taxonomy_clean = real_tax[ref_id][0]
elif len(subjects_ids) > 499 and taxIsRetrieved(real_tax[ref_id], possible_taxonomies):
taxonomy_clean = real_tax[ref_id][0]
elif "Multi-affiliation" in taxonomy_clean:
taxonomy_clean = getCleanedTaxonomy(observation["metadata"]["blast_affiliations"][0]["taxonomy"]) # Select one
else: # Chimera
if "Multi-affiliation" in taxonomy_clean:
taxonomy_clean = getCleanedTaxonomy(observation["metadata"]["blast_affiliations"][0]["taxonomy"]) # Select one
# Store count
for depth in range(len(taxonomy_clean)):
if len(abund_by_rank) < depth+1:
abund_by_rank.append(dict())
taxon = ";".join( taxonomy_clean[:depth+1] ) # prevent bug with same sp name but with different ancestors
if not abund_by_rank[depth].has_key(taxon):
abund_by_rank[depth][taxon] = 0
abund_by_rank[depth][taxon] += count
return abund_by_rank
def getCheckedAbunByRank( real_tax, input_biom, sample, taxonomy_key, multi_affiliation, duplication_groups ):
"""
@summary:
@param real_tax: [dict] Taxonomy by reference IDs.
@param input_biom: [str] Path to BIOM file.
@param sample: [str] sample name.
@param taxonomy_key: [str] The metadata key for taxonomy.
@param multi_affiliation: [bool] ************************************************************************************
@param duplication_groups: [dict] By reference ID the list of IDs for references with the same sequence.
@return: [dict] The dictionary of count by taxa in dictionary by rank.
"""
abund_by_rank = list()
biom = BiomIO.from_json( input_biom )
for observation in biom.get_observations():
count = biom.get_count( observation["id"], sample )
if count > 0:
# Get taxonomy
ref_id = observation["metadata"]["grinder_source"]
taxonomy_clean = getCleanedTaxonomy(observation["metadata"][taxonomy_key])
if not multi_affiliation: # Standard affiliation
if not "," in ref_id: # Non chimera
if taxIsRetrieved(real_tax[ref_id], [taxonomy_clean]):
taxonomy_clean = real_tax[ref_id][0]
else: # Multi-affiliation
if not "," in ref_id: # Non chimera
subjects_ids = [affi["subject"] for affi in observation["metadata"]["blast_affiliations"]]
possible_taxonomies = [";".join(getCleanedTaxonomy(affi["taxonomy"])) for affi in observation["metadata"]["blast_affiliations"]]
# Manage ambiguity
if refIDIsRetrieved(ref_id, subjects_ids, duplication_groups):
taxonomy_clean = real_tax[ref_id][0]
elif len(subjects_ids) > 499 and taxIsRetrieved(real_tax[ref_id], possible_taxonomies):
taxonomy_clean = real_tax[ref_id][0]
elif "Multi-affiliation" in taxonomy_clean:
taxonomy_clean = getCleanedTaxonomy(observation["metadata"]["blast_affiliations"][0]["taxonomy"]) # Select one
else: # Chimera
if "Multi-affiliation" in taxonomy_clean:
taxonomy_clean = getCleanedTaxonomy(observation["metadata"]["blast_affiliations"][0]["taxonomy"]) # Select one
# Store count
for depth in range(len(taxonomy_clean)):
if len(abund_by_rank) < depth+1:
abund_by_rank.append(dict())
taxon = ";".join( taxonomy_clean[:depth+1] ) # prevent bug with same sp name but with different ancestors
if not abund_by_rank[depth].has_key(taxon):
abund_by_rank[depth][taxon] = 0
abund_by_rank[depth][taxon] += count
return abund_by_rank
def aff_to_metadata(reference_file, biom_in, biom_out, blast_files=None, rdp_files=None):
"""
@summary: Add taxonomy metadata on biom file from a blast result.
@param reference_file: [str] The path to the reference file.
@param biom_in: [str] The path to the Biom file to process.
@param biom_out: [str] The path to the biom output file.
@param blast_files: [list] the list of the path to the blast results in tabular format (outfmt 6 with NCBI Blast+).
@param rdp_files: [list] the list of path to the RDPClassifier results.
"""
# Build an hash with the taxonomy for each gene (key=gene_id ; value=gene_taxonomy)
taxonomy_by_reference = get_tax_from_fasta( reference_file )
# Retrieve blast clusters annotations
cluster_blast_annot = dict()
if blast_files is not None:
cluster_blast_annot = get_bests_blast_affi( blast_files, taxonomy_by_reference )
del taxonomy_by_reference
# Retrieve rdp clusters annotations
cluster_rdp_annot = dict()
if rdp_files is not None:
cluster_rdp_annot = get_rdp_affi( rdp_files )
# Add metadata to biom
biom = BiomIO.from_json(biom_in)
for cluster in biom.get_observations():
cluster_id = cluster["id"]
# Blast
if blast_files is not None:
blast_taxonomy = None
blast_affiliations = list()
if cluster_blast_annot.has_key(cluster_id): # Current observation has a match
blast_taxonomy = get_tax_consensus( [alignment['taxonomy'] for alignment in cluster_blast_annot[cluster_id]['alignments']] )
blast_affiliations = cluster_blast_annot[cluster_id]['alignments']
biom.add_metadata( cluster_id, "blast_affiliations", blast_affiliations, "observation" )
biom.add_metadata( cluster_id, "blast_taxonomy", blast_taxonomy, "observation" )
# RDP
if rdp_files is not None:
rdp_taxonomy = None
rdp_bootstrap = None
if cluster_rdp_annot.has_key(cluster_id):
rdp_taxonomy = cluster_rdp_annot[cluster_id]['taxonomy']
rdp_bootstrap = cluster_rdp_annot[cluster_id]['bootstrap']
biom.add_metadata(cluster_id, "rdp_taxonomy", rdp_taxonomy, "observation")
biom.add_metadata(cluster_id, "rdp_bootstrap", rdp_bootstrap, "observation")
BiomIO.write(biom_out, biom)
def excluded_obs_on_rdpBootstrap(input_biom, taxonomic_depth, min_bootstrap, excluded_file):
"""
@summary: Writes the list of the observations with an insufficient bootstrap on the specified taxonomic rank.
@param input_biom: [str] The path to the BIOM file to check.
@param taxonomic_depth: [int] The taxonomic rank depth to check (example: 6 for Species in system "Domain, Phylum, Class, Order, Family, Genus, Species").
@param min_bootstrap: [float] The observations with a value inferior to this threshold at the specified taxonomic depth are reported in the excluded file.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json( input_biom )
FH_excluded_file = open( excluded_file, "w" )
for observation in biom.get_observations():
bootstrap = observation["metadata"]["rdp_bootstrap"]
if issubclass(bootstrap.__class__, str):
bootstrap = bootstrap.split(";")
if bootstrap[taxonomic_depth] < min_bootstrap:
FH_excluded_file.write( str(observation["id"]) + "\n" )
FH_excluded_file.close()
def excluded_obs_on_abundance(input_biom, min_abundance, excluded_file):
"""
@summary: Writes the list of the observations with an insufficient abundance.
@param input_biom: [str] The path to the BIOM file to check.
@param min_abundance: [int/float] The observations with an abundance inferior than this value are reported in the excluded file.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json( input_biom )
FH_excluded_file = open( excluded_file, "w" )
min_nb_seq = min_abundance
if type(min_abundance) == float:
min_nb_seq = biom.get_total_count() * min_abundance
for idx, count_by_sample in enumerate(biom.to_count()):
observation = biom.rows[idx]
abundance = sum(count_by_sample)
if abundance < min_nb_seq:
FH_excluded_file.write( str(observation["id"]) + "\n" )
FH_excluded_file.close()
def get_realTax(taxonomy_key, input_biom):
"""
@summary: Returns count by taxa by rank in sample.
@param input_biom: [str] Path to BIOM file.
@return: [dict] The dictionary of count by taxa in dictionary by rank.
"""
tax_list = list()
nb_seq = 0
biom = BiomIO.from_json(input_biom)
for observation in biom.get_observations():
nb_seq += 1
taxonomy_clean = getCleanedTaxonomy(
observation["metadata"][taxonomy_key])
if not taxonomy_clean in tax_list:
tax_list.append(";".join(taxonomy_clean))
return tax_list
def excluded_obs_on_rdpBootstrap(input_biom, taxonomic_depth, min_bootstrap,
excluded_file):
"""
@summary: Writes the list of the observations with an insufficient bootstrap on the specified taxonomic rank.
@param input_biom: [str] The path to the BIOM file to check.
@param taxonomic_depth: [int] The taxonomic rank depth to check (example: 6 for Species in system "Domain, Phylum, Class, Order, Family, Genus, Species").
@param min_bootstrap: [float] The observations with a value inferior to this threshold at the specified taxonomic depth are reported in the excluded file.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json(input_biom)
FH_excluded_file = open(excluded_file, "w")
for observation in biom.get_observations():
bootstrap = observation["metadata"]["rdp_bootstrap"]
if issubclass(bootstrap.__class__, str):
bootstrap = bootstrap.split(";")
if bootstrap[taxonomic_depth] < min_bootstrap:
FH_excluded_file.write(str(observation["id"]) + "\n")
FH_excluded_file.close()
def biom_to_tsv( input_biom, output_tsv, fields, list_separator ):
"""
@summary: Convert BIOM file to TSV file.
@param input_biom: [str] Path to the BIOM file.
@param output_tsv: [str] Path to the output file (format : TSV).
@param fields: [list] Columns and their order in output. Special columns : '@observation_name', '@observation_sum', '@sample_count' '@rdp_tax_and_bootstrap' . The others columns must be metadata title.
@param list_separator: [str] Separator for complex metadata.
"""
biom = BiomIO.from_json( input_biom )
out_fh = open( output_tsv, "w" )
# Header
header_parts = header_line_parts( fields, biom )
out_fh.write( "#" + "\t".join(header_parts) + "\n" )
# Data
for obs_idx, count_by_sample in enumerate(biom.to_count()):
observation_parts = observation_line_parts( biom.rows[obs_idx], count_by_sample, fields, list_separator )
out_fh.write( "\t".join(observation_parts) + "\n" )
out_fh.close()
def excluded_obs_on_abundance(input_biom, min_abundance, excluded_file):
"""
@summary: Writes the list of the observations with an insufficient abundance.
@param input_biom: [str] The path to the BIOM file to check.
@param min_abundance: [int/float] The observations with an abundance inferior than this value are reported in the excluded file.
@param excluded_file: [str] The path to the output file.
"""
biom = BiomIO.from_json(input_biom)
FH_excluded_file = open(excluded_file, "w")
min_nb_seq = min_abundance
if type(min_abundance) == float:
min_nb_seq = biom.get_total_count() * min_abundance
for idx, count_by_sample in enumerate(biom.to_count()):
observation = biom.rows[idx]
abundance = sum(count_by_sample)
if abundance < min_nb_seq:
FH_excluded_file.write(str(observation["id"]) + "\n")
FH_excluded_file.close()
def biom_to_tsv( input_biom, output_tsv, fields, list_separator ):
"""
@summary: Convert BIOM file to TSV file.
@param input_biom: [str] Path to the BIOM file.
@param output_tsv: [str] Path to the output file (format : TSV).
@param fields: [list] Columns and their order in output. Special columns : '@observation_name', '@observation_sum', '@sample_count' '@rdp_tax_and_bootstrap' . The others columns must be metadata title.
@param list_separator: [str] Separator for complex metadata.
"""
biom = BiomIO.from_json( input_biom )
out_fh = open( output_tsv, "w" )
# Header
header_parts = header_line_parts( fields, biom )
out_fh.write( "#" + "\t".join(header_parts) + "\n" )
# Data
for obs_idx, count_by_sample in enumerate(biom.to_count()):
observation_parts = observation_line_parts( biom.rows[obs_idx], count_by_sample, fields, list_separator )
out_fh.write( "\t".join(observation_parts) + "\n" )
out_fh.close()
def process(args):
tmp_files = TmpFiles(os.path.split(args.output_file)[0])
try:
# Add temp taxonomy if multiple and without consensus
tmp_biom = args.input_biom
used_taxonomy_tag = args.taxonomy_tag
if args.multiple_tag is not None:
used_taxonomy_tag = args.tax_consensus_tag
if args.tax_consensus_tag is None:
used_taxonomy_tag = "Used_taxonomy_FROGS-affi"
tmp_biom = tmp_files.add("tax.biom")
biom = BiomIO.from_json(args.input_biom)
for observation in biom.get_observations():
metadata = observation["metadata"]
if metadata[args.multiple_tag] is not None and len(
metadata[args.multiple_tag]) > 0:
metadata[used_taxonomy_tag] = metadata[
args.multiple_tag][0][args.taxonomy_tag]
BiomIO.write(tmp_biom, biom)
del biom
# Rarefaction
tax_depth = [
args.taxonomic_ranks.index(rank) for rank in args.rarefaction_ranks
]
rarefaction_cmd = Rarefaction(tmp_biom, tmp_files, used_taxonomy_tag,
tax_depth)
rarefaction_cmd.submit(args.log_file)
rarefaction_files = rarefaction_cmd.output_files
# Taxonomy tree
tree_count_file = tmp_files.add("taxCount.enewick")
tree_ids_file = tmp_files.add("taxCount_ids.tsv")
TaxonomyTree(tmp_biom, used_taxonomy_tag, tree_count_file,
tree_ids_file).submit(args.log_file)
# Writes summary
write_summary(args.output_file, args.input_biom, tree_count_file,
tree_ids_file, rarefaction_files, args)
finally:
if not args.debug:
tmp_files.deleteAll()
def biom_to_tsv(input_biom, output_tsv, fields, list_separator):
"""
@summary: Convert BIOM file to TSV file.
@param input_biom: [str] Path to the BIOM file.
@param output_tsv: [str] Path to the output file (format : TSV).
@param fields: [list] Columns and their order in output. Special columns : '@observation_name', '@observation_sum', '@sample_count'. The others columns must be metadata title.
@param list_separator: [str] Separator for complex metadata.
"""
biom = BiomIO.from_json(input_biom)
out_fh = open(output_tsv, "w")
# Header
line = list()
for current_field in fields:
if current_field == '@observation_name':
line.append("observation_name")
elif current_field == '@sample_count':
line.append("\t".join(biom.get_samples_names()))
elif current_field == '@observation_sum':
line.append("observation_sum")
else: #metadata
line.append(str(current_field))
out_fh.write("#" + "\t".join(line) + "\n")
# Data
for idx, count_by_sample in enumerate(biom.to_count()):
observation = biom.rows[idx]
line = list()
for current_field in fields:
if current_field == '@observation_name':
line.append(str(observation['id']))
elif current_field == '@sample_count':
line.append("\t".join(map(str, count_by_sample)))
elif current_field == '@observation_sum':
line.append(str(sum(count_by_sample)))
else: #metadata
if issubclass(observation['metadata'][current_field].__class__,
list):
line.append(
list_separator.join(
observation['metadata'][current_field]))
else:
line.append(str(observation['metadata'][current_field]))
out_fh.write("\t".join(line) + "\n")
out_fh.close()
def biom_fasta_update(biom_in, fasta_in, fasta_out, log_file):
FH_in = FastaIO( fasta_in )
FH_out = FastaIO( fasta_out, "w" )
biom = BiomIO.from_json( biom_in )
seq_in=0
seq_out=0
for record in FH_in:
seq_in += 1
try:
biom.find_idx("observation",record.id)
except ValueError:
pass
else:
FH_out.write(record)
seq_out += 1
FH_in.close()
FH_out.close()
FH_log=open(log_file,"w")
FH_log.write("Number of sequence in :" + str(seq_in)+"\n" )
FH_log.write("Number of sequence out :" + str(seq_out) +"\n")
def biom_to_tsv( input_biom, output_tsv, fields, list_separator ):
"""
@summary: Convert BIOM file to TSV file.
@param input_biom: [str] Path to the BIOM file.
@param output_tsv: [str] Path to the output file (format : TSV).
@param fields: [list] Columns and their order in output. Special columns : '@observation_name', '@observation_sum', '@sample_count'. The others columns must be metadata title.
@param list_separator: [str] Separator for complex metadata.
"""
biom = BiomIO.from_json( input_biom )
out_fh = open( output_tsv, "w" )
# Header
line = list()
for current_field in fields:
if current_field == '@observation_name':
line.append( "observation_name" )
elif current_field == '@sample_count':
line.append( "\t".join(biom.get_samples_names()) )
elif current_field == '@observation_sum':
line.append( "observation_sum" )
else: #metadata
line.append( str(current_field) )
out_fh.write( "#" + "\t".join(line) + "\n" )
# Data
for idx, count_by_sample in enumerate(biom.to_count()):
observation = biom.rows[idx]
line = list()
for current_field in fields:
if current_field == '@observation_name':
line.append( str(observation['id']) )
elif current_field == '@sample_count':
line.append( "\t".join(map(str, count_by_sample)) )
elif current_field == '@observation_sum':
line.append( str(sum(count_by_sample)) )
else: #metadata
if issubclass(observation['metadata'][current_field].__class__, list):
line.append( list_separator.join(observation['metadata'][current_field]) )
else:
line.append( str(observation['metadata'][current_field]) )
out_fh.write( "\t".join(line) + "\n" )
out_fh.close()
def samples_hclassification( input_biom, output_newick, distance_method, linkage_method ):
"""
@summary : Process and write an hierarchical classification from Biom.
@param input_biom : [str] Path to the BIOM file to process.
@param output_newick : [str] Path to the newick output file.
@param distance_method : [str] Used distance method for classify.
@param linkage_method : [str] Used linkage method for classify.
"""
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, dendrogram
import scipy.cluster.hierarchy
data_array = list()
samples_names = list()
# Normalisation on count by sample
biom = BiomIO.from_json( input_biom )
for col_idx, current_sample in enumerate(biom.columns):
samples_names.append( current_sample['id'] )
sum_on_sample = biom.data.get_col_sum( col_idx )
OTUs_norm = list()
for row_idx in range(len(biom.rows)):
OTUs_norm.append( biom.data.nb_at(row_idx, col_idx)/float(sum_on_sample) )
data_array.append( OTUs_norm )
del biom
if len(samples_names) == 1 :
# Write newick
out_fh = open( output_newick, "w" )
out_fh.write( "(" + samples_names[0] + ");\n" )
out_fh.close()
else:
# Computing the distance and linkage
data_dist = pdist( data_array, distance_method )
data_link = linkage( data_dist, linkage_method )
# Write newick
scipy_hc_tree = scipy.cluster.hierarchy.to_tree( data_link , rd=False )
id_2_name = dict( zip(range(len(samples_names)), samples_names) )
out_fh = open( output_newick, "w" )
out_fh.write( to_newick(scipy_hc_tree, id_2_name) + "\n" )
out_fh.close()