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( Exception( "\n\n#ERROR : " + str(sample_nb_sampled) + " sequences cannot be sampled in sample '" + str(sample_name) + "'. It only contains " + str(sample_seq) + " sequences.\n\n" ))
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 selected_observation_id not in observations_already_added:
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 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 to_biom( clusters_file, count_file, output_biom, size_separator ):
"""
@summary : Write a biom file from swarm results.
@param clusters_file : [str] path to the '.clstr' file.
@param count_file : [str] path to the count file. It contains the count of
sequences by sample of each preclusters.
Line format : "Precluster_id nb_in_sampleA nb_in_sampleB"
@param output_biom : [str] path to the output file.
@param size_separator : [str] the pre-cluster abundance separator.
"""
biom = Biom( generated_by='swarm', matrix_type="sparse" )
# Preclusters count by sample
preclusters_count = dict()
count_fh = open( count_file )
samples = count_fh.readline().strip().split()[1:]
for line in count_fh:
precluster_id, count_str = line.strip().split(None, 1)
preclusters_count[precluster_id] = count_str # For large dataset store count into a string consumes minus RAM than a sparse count
count_fh.close()
# Add samples
for sample_name in samples:
biom.add_sample( sample_name )
# Process count
cluster_idx = 1
clusters_fh = open( clusters_file )
for line in clusters_fh:
seed_id = line.strip().split()[0]
if "FROGS_combined" in seed_id:
cluster_name = "Cluster_" + str(cluster_idx) + "_FROGS_combined"
comment = "WARNING"
else:
cluster_name = "Cluster_" + str(cluster_idx)
comment = "na"
cluster_count = {key:0 for key in samples}
line_fields = line.strip().split()
# Retrieve count by sample
for seq_id in line_fields:
real_seq_id = seq_id.rsplit(size_separator, 1)[0]
sample_counts = preclusters_count[real_seq_id].split()
for sample_idx, sample_name in enumerate(samples):
cluster_count[sample_name] += int(sample_counts[sample_idx])
preclusters_count[real_seq_id] = None
# Add cluster on biom
biom.add_observation( cluster_name, {'comment': comment, 'seed_id':line_fields[0].rsplit(size_separator, 1)[0]} )
observation_idx = biom.find_idx("observation", cluster_name)
for sample_idx, sample_name in enumerate(samples):
if cluster_count[sample_name] > 0:
biom.data.change( observation_idx, sample_idx, cluster_count[sample_name] )
# Next cluster
cluster_idx += 1
# Write
BiomIO.write( output_biom, biom )
def to_biom(clusters_file, count_file, output_biom, size_separator):
"""
@summary : Write a biom file from swarm results.
@param clusters_file : [str] path to the '.clstr' file.
@param count_file : [str] path to the count file. It contains the count of
sequences by sample of each preclusters.
Line format : "Precluster_id nb_in_sampleA nb_in_sampleB"
@param output_biom : [str] path to the output file.
@param size_separator : [str] the pre-cluster abundance separator.
"""
biom = Biom(generated_by='swarm', matrix_type="sparse")
# Preclusters count by sample
preclusters_count = dict()
count_fh = open(count_file)
samples = count_fh.readline().strip().split()[1:]
for line in count_fh:
line_fields = line.strip().split()
count_by_sample = {}
for idx, val in enumerate(line_fields[1:]):
if val > 0:
count_by_sample[samples[idx]] = int(val)
preclusters_count[line_fields[0]] = count_by_sample
count_fh.close()
# Add samples
for sample_name in samples:
biom.add_sample(sample_name)
# Process count
cluster_idx = 1
clusters_fh = open(clusters_file)
for line in clusters_fh:
cluster_name = "Cluster_" + str(cluster_idx)
cluster_count = {key: 0 for key in samples}
line_fields = line.strip().split()
# Retrieve count by sample
for seq_id in line_fields:
real_seq_id = seq_id.rsplit(size_separator, 1)[0]
for preclust_sample in preclusters_count[real_seq_id]:
cluster_count[preclust_sample] += preclusters_count[
real_seq_id][preclust_sample]
preclusters_count[real_seq_id] = None
# Add cluster on biom
biom.add_observation(
cluster_name,
{'seed_id': line_fields[0].rsplit(size_separator, 1)[0]})
for sample_name in samples:
if cluster_count[sample_name] > 0:
biom.add_count(cluster_name, sample_name,
cluster_count[sample_name])
# Next cluster
cluster_idx += 1
# Write
BiomIO.write(output_biom, biom)
def to_biom( clusters_file, count_file, output_biom, size_separator ):
"""
@summary : Write a biom file from swarm results.
@param clusters_file : [str] path to the '.clstr' file.
@param count_file : [str] path to the count file. It contains the count of
sequences by sample of each preclusters.
Line format : "Precluster_id nb_in_sampleA nb_in_sampleB"
@param output_biom : [str] path to the output file.
@param size_separator : [str] the pre-cluster abundance separator.
"""
biom = Biom( generated_by='swarm', matrix_type="sparse" )
# Preclusters count by sample
preclusters_count = dict()
count_fh = open( count_file )
samples = count_fh.readline().strip().split()[1:]
for line in count_fh:
precluster_id, count_str = line.strip().split(None, 1)
preclusters_count[precluster_id] = count_str # For large dataset store count into a string consumes minus RAM than a sparse count
count_fh.close()
# Add samples
for sample_name in samples:
biom.add_sample( sample_name )
# Process count
cluster_idx = 1
clusters_fh = open( clusters_file )
for line in clusters_fh:
cluster_name = "Cluster_" + str(cluster_idx)
cluster_count = {key:0 for key in samples}
line_fields = line.strip().split()
# Retrieve count by sample
for seq_id in line_fields:
real_seq_id = seq_id.rsplit(size_separator, 1)[0]
sample_counts = preclusters_count[real_seq_id].split()
for sample_idx, sample_name in enumerate(samples):
cluster_count[sample_name] += int(sample_counts[sample_idx])
preclusters_count[real_seq_id] = None
# Add cluster on biom
biom.add_observation( cluster_name, {'seed_id':line_fields[0].rsplit(size_separator, 1)[0]} )
observation_idx = biom.find_idx("observation", cluster_name)
for sample_idx, sample_name in enumerate(samples):
if cluster_count[sample_name] > 0:
biom.data.change( observation_idx, sample_idx, cluster_count[sample_name] )
# Next cluster
cluster_idx += 1
# Write
BiomIO.write( output_biom, biom )
parser.add_argument( '-v', '--version', action='version', version=__version__ )
# Inputs
group_input = parser.add_argument_group( 'Inputs' )
group_input.add_argument( '-s', '--samples', type=str, action=SampleParameter, metavar=("SAMPLE_NAME:SAMPLE_PATH"), nargs='+', help="Samples names and grinder rank files." )
group_input.add_argument( '-a', '--affiliation', required=True, help='Path to the databank source for simulated sequence (format: fasta). The description of sequences must be the taxonomy.' )
# Outputs
group_output = parser.add_argument_group( 'Outputs' )
group_output.add_argument( '-o', '--output', required=True, help='The output BIOM (format: BIOM).' )
args = parser.parse_args()
taxonomy_key = "real_taxonomy"
biom = Biom( generated_by="grinder", matrix_type="sparse" )
# Set observations count
for sample_name in args.samples:
biom.add_sample( sample_name )
fh_abund = open( args.samples[sample_name] )
for line in fh_abund: # Content format: "# rank<TAB>seq_id<TAB>rel_abund_perc"
if not line.startswith('#'):
fields = line.strip().split()
try:
biom.add_observation( fields[1] )
except: # already exist
pass
biom.change_count( fields[1], sample_name, int(float(fields[2])*100000000000000) )################## depend de la precision grinder
fh_abund.close()
# Set taxonomy metadata
fh_classif = FastaIO( args.affiliation )
for record in fh_classif:
try:
def tsv_to_biom( input_tsv, multi_hit_dict, fields, samples_names, output_biom, output_fasta ):
"""
@summary: Convert TSV file to Biom file.
@param input_tsv: [str] Path to the TSV file.
@param multi_hit_dict: [dict] Dictionnary describing equivalent multi blast hit :
dict[observation_name]=[ {"blast_taxonomy":taxonomy, "blast_subject":subject, "blast_perc_identity": per_id, "blast_perc_query_coverage":per_cov, "blast_evalue":eval, "blast_aln_length":aln}]
@param fields: [list] column name to include as metadata (must at least contain observation_name): observation_sum and seed_sequence will be excluded, rdp_tax_and_bootstrap will be split in two metadata
@param samples_names: [list] list of sample names.
@param output_biom: [str] Path to the output file (format : BIOM).
@param output_fasta: [str] Path to the output file (format : fasta).
"""
# biom = Biom( generated_by='frogs', matrix_type="sparse" )
biom = Biom( matrix_type="sparse" )
seed_seq_idx = -1
metadata_index = dict()
sample_index = dict()
clusters_count = dict()
clusters_metadata = dict()
in_fh = open( input_tsv )
if not output_fasta is None:
Fasta_fh=FastaIO(output_fasta , "w" )
# parse header and store column index
header=in_fh.readline()
if header.startswith("#"):
header=header[1:]
header = header.strip()
seed_seq_idx, metadata_index, sample_index = header_line_dict(fields,header,samples_names)
if not output_fasta is None and seed_seq_idx == -1:
raise Exception("\nYou want to extract seed fasta sequence but there is no seed_sequence column in your TSV file\n\n")
# count by sample, and metadata
for line in in_fh:
cluster_name=""
line_list=line.strip().split("\t")
count_by_sample = {}
metadata_dict = {}
# parse columns
for idx,val in enumerate(line_list):
# recover metadata
if idx in metadata_index:
if metadata_index[idx]=="observation_name" :
cluster_name = val
else:
metadata_dict[metadata_index[idx]] = val
# recover samples count
elif idx in sample_index and val > 0:
count_by_sample[sample_index[idx]] = int(val)
# recover seed sequence
elif idx == seed_seq_idx:
seed_seq = val
# if fasta output file => store de seed sequence
if not output_fasta is None:
seq = Sequence( cluster_name, seed_seq)
Fasta_fh.write(seq)
if "taxonomy" in metadata_dict:
metadata_dict["taxonomy"] = metadata_dict["taxonomy"].split(";")
# format rdp taxonomy to fit BIOM format
if "rdp_tax_and_bootstrap" in metadata_dict:
metadata_dict["rdp_taxonomy"]=[]
metadata_dict["rdp_bootstrap"]=[]
tax = metadata_dict["rdp_tax_and_bootstrap"].rstrip(";").split(";")
for i in range(0,len(tax),2):
metadata_dict["rdp_taxonomy"].append(tax[i])
metadata_dict["rdp_bootstrap"].append(tax[i+1].replace("(","").replace(")",""))
metadata_dict.pop("rdp_tax_and_bootstrap")
# format blast taxonomy to fit BIOM format (one consensus blast_taxonomy and possible multiples blast_affiliation detailed
if "blast_taxonomy" in metadata_dict:
metadata_dict["blast_taxonomy"] = metadata_dict["blast_taxonomy"].split(";")
# check multihit blast : filter non consistent taxonomy hit with blast_taxonomy (if TSV modified), and compute consensus tax (if multihit line suppressed)
if metadata_dict["blast_subject"] == "multi-subject" and not multi_hit_dict is None:
if not cluster_name in multi_hit_dict:
raise Exception("\n"+cluster_name+" has multi-subject tag but is not present in your multi-hit TSV file. Please, provide the original multi-hit TSV file.\n\n")
else:
metadata_dict["blast_taxonomy"], metadata_dict["blast_affiliations"] = observation_blast_parts(metadata_dict, multi_hit_dict[cluster_name])
if metadata_dict["blast_affiliations"] == []:
raise Exception("\nyour multihit TSV file is no more consistent with your abundance TSV file for (at least) "+cluster_name+"\n\n")
# no multi tag= blast affiliation is equal to blast_taxonomy
else:
blast_dict={key.replace("blast_",""):metadata_dict[key] for key in metadata_dict if key.startswith("blast")}
metadata_dict["blast_affiliations"]=[blast_dict]
# filter blast metadata which are moved to blast_affiliations
for metadata in metadata_dict["blast_affiliations"][0]:
if not metadata == "taxonomy":
metadata_dict.pop("blast_"+metadata)
# add cluster and count to clusters_count dict
clusters_count[cluster_name] = count_by_sample
# ok print clusters_count[cluster_name].keys(), "CDT0#LOT05" in clusters_count[cluster_name], "CDT0#LOT02" in clusters_count[cluster_name]
# add cluster and metadata to clusters_metadata dict
clusters_metadata[cluster_name] = metadata_dict
if not output_fasta is None:
Fasta_fh.close()
in_fh.close()
#add samples to biom
for sample_name in samples_names:
biom.add_sample( sample_name )
# add to cluster to biom
for cluster_name in clusters_count:
biom.add_observation( cluster_name, clusters_metadata[cluster_name] )
for sample_name in samples_names:
if clusters_count[cluster_name][sample_name] > 0:
biom.add_count( cluster_name, sample_name, clusters_count[cluster_name][sample_name] )
# Write
BiomIO.write( output_biom, biom )
'Path to the databank source for simulated sequence (format: fasta). The description of sequences must be the taxonomy.'
)
# Outputs
group_output = parser.add_argument_group('Outputs')
group_output.add_argument('-o',
'--output',
required=True,
help='The output BIOM (format: BIOM).')
args = parser.parse_args()
taxonomy_key = "real_taxonomy"
biom = Biom(generated_by="grinder", matrix_type="sparse")
# Set observations count
for sample_name in args.samples:
biom.add_sample(sample_name)
fh_abund = open(args.samples[sample_name])
for line in fh_abund: # Content format: "# rank<TAB>seq_id<TAB>rel_abund_perc"
if not line.startswith('#'):
fields = line.strip().split()
try:
biom.add_observation(fields[1])
except: # already exist
pass
biom.change_count(
fields[1], sample_name,
int(float(fields[2]) * 100000000000000
)) ################## depend de la precision grinder
fh_abund.close()
# Set taxonomy metadata