def create_new_header(infile, mappings, outfile):
"""Create new header in BigWig, with UCSC chromosome names."""
with pyBigWig.open(infile) as bw:
if set(bw.chroms().keys()).issubset(mappings.values()):
# If chromosome names are already UCSC, just rename input file to output name.
# Exit with status 0 since this is normal behavior.
os.rename(infile, outfile)
sys.exit(0)
hdr = [(mappings[chrom], length) for chrom, length in bw.chroms().items() if chrom in mappings]
if not hdr:
msg = "Neither of the chromosomes in the input file has a valid UCSC pair. No mapping will be done."
print(warning(msg))
os.rename(infile, outfile)
sys.exit(0)
seq_num = 0
with pyBigWig.open(outfile, 'w') as bw_output:
bw_output.addHeader(hdr)
for chrom, length in bw.chroms().items():
ints = bw.intervals(chrom, 0, length)
if ints and chrom in mappings:
bw_output.addEntries([mappings[chrom]] * len(ints),
[x[0] for x in ints],
ends=[x[1] for x in ints],
values=[x[2] for x in ints])
elif chrom not in mappings:
seq_num += 1
print('UCSC chromosome/conting mapping for {} is missing'.format(chrom))
if seq_num > 0:
print(warning("UCSC chromosome/conting mapping for {} sequence(s) is missing. "
"This sequence(s) will not be included in the bigWig file.".format(seq_num)))
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
validate_inputs(args)
exp_type = args.exp_types[0]
spikeins_mix = args.spikeins_mix
expected = get_expected(spikeins_mix, log2=True)
min_one_has_spikeins = False # At least one sample has spikeins = False
warnings = []
for sample_name, sample_exp in zip(args.sample_names, args.sample_exps):
measured_zero = get_measured(sample_exp,
sample_name,
exp_type,
only_zero=True)
measured_nonzero = get_measured(sample_exp,
sample_name,
exp_type,
only_nonzero=True,
log2=True)
merged_zero = merge_expected_measured(expected, measured_zero)
merged_nonzero = merge_expected_measured(expected, measured_nonzero)
# Get only ERCC spike-in's and plot the histogram-scatter figure.
if merged_nonzero.iloc[
merged_nonzero.index.str.startswith('ERCC'), :].empty:
warnings.append(
'All ERCC spike-ins have zero expression in sample {}'.format(
sample_name))
continue
min_one_has_spikeins = True
plot_histogram_scatter(
expected=expected.iloc[expected.index.str.startswith('ERCC')],
zero=merged_zero.iloc[merged_zero.index.str.startswith('ERCC'), :],
nonzero=merged_nonzero.iloc[
merged_nonzero.index.str.startswith('ERCC'), :],
spikein_type='ERCC',
sample_name=sample_name,
exp_type=exp_type,
)
if min_one_has_spikeins:
for message in warnings:
print(warning(message))
else:
# In case all samples have zero expression for all spikeins,
# rather print one warning that says so (instead of printing
# warning for each of the samples).
print(
warning('All ERCC spike-ins in all samples have zero expression.'))
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
res = resdk.Resolwe()
with open(args.feature_ids) as gene_file:
genes = [gene.strip() for gene in gene_file]
org_features = res.feature.filter(source=args.source_db, species=args.species, feature_id=genes)
if len(org_features) == 0:
print(error("No genes were fetched from the knowledge base."))
exit(1)
if args.source_db == args.target_db:
target_ids = genes
else:
mapping_res = res.mapping.filter(
source_db=args.source_db,
source_species=args.species,
target_db=args.target_db,
target_species=args.species,
source_id=genes,
)
if len(mapping_res) == 0:
print(error("Failed to map features."))
exit(1)
mappings = {}
for m in mapping_res:
if m.source_id in genes:
if m.source_id not in mappings:
mappings[m.source_id] = m.target_id
else:
print(warning("Mapping {} returned multiple times.".format(m)))
if len(genes) > len(mappings):
print(warning("Not all features could be mapped."))
target_ids = mappings.values()
with tempfile.NamedTemporaryFile() as input_genes:
input_genes.write(' '.join(target_ids).encode("UTF-8"))
input_genes.flush()
process = Popen(['processor', str(args.pval), str(args.min_genes), args.obo, args.gaf, input_genes.name],
stdout=PIPE,
stderr=DEVNULL
)
out, err = process.communicate()
with open('terms.json', 'w') as f:
f.write(out.decode("UTF-8"))
def iterate_snpeff_file(file_handle, filename):
"""Iterate entries in file produced by SnpSift extractFields."""
for row in file_handle:
# One line can contain two or more ALT values (and consequently two or more AF/AD values) Such "multiple"
# entries are split into one ALT/AF/AD value per row. Lofreq data does not contain AD value, this is why
# ``ad_s`` generation might appear messy (to cover the case with or without AD column).
alts = row["ALT"].strip().split(",")
afqs = row["AF"].strip().split(",")
default_ad_s = ",".join([""] * (len(alts) + 1))
# First entry is AD of REF allele, and the rest of them are for ALT alleles.
ad_s = row.get("GEN[0].AD", default_ad_s).strip().split(",")[1:]
if not (len(alts) == len(afqs) == len(ad_s)):
print(
warning(
"Inconsistency for entry {} in file {}. Skipping this entry."
.format(row, os.path.basename(filename))))
continue
if len(ad_s) == 1:
row["AD"] = ad_s[0]
yield row
else:
for alt, afq, ad_ in zip(alts, afqs, ad_s):
row_copy = copy.deepcopy(row)
row_copy["ALT"] = alt
row_copy["AF"] = afq
if ad_:
row_copy["AD"] = ad_
yield row_copy
def iterate_snpeff_file(file_handle, filename):
"""Iterate entries in file produced by SnpSift extractFields."""
for row in file_handle:
# One line can contain two or more ALT values (and consequently two or more AF/AD values) Such "multiple"
# entries are split into one ALT/AF/AD value per row. Lofreq data does not contain AD value, this is why
# ``ad_s`` generation might appear messy (to cover the case with or without AD column).
alts = row['ALT'].strip().split(',')
afqs = row['AF'].strip().split(',')
default_ad_s = ','.join([''] * (len(alts) + 1))
# First entry is AD of REF allele, and the rest of them are for ALT alleles.
ad_s = row.get('GEN[0].AD', default_ad_s).strip().split(',')[1:]
if not (len(alts) == len(afqs) == len(ad_s)):
print(warning('Inconsistency for entry {} in file {}. Skipping this entry.'.format(
row, os.path.basename(filename))))
continue
if len(ad_s) == 1:
row['AD'] = ad_s[0]
yield row
else:
for alt, afq, ad_ in zip(alts, afqs, ad_s):
row_copy = copy.deepcopy(row)
row_copy['ALT'] = alt
row_copy['AF'] = afq
if ad_:
row_copy['AD'] = ad_
yield row_copy
def test_string(self):
expected = {
'type': 'COMMAND',
'type_data': 'process_log',
'data': {'warning': 'Some warning'},
}
self.assertEqual(warning('Some warning'), expected)
def get_pca(expressions=pd.DataFrame(), n_components=2, gene_labels=[]):
"""Compute PCA."""
if not gene_labels:
gene_labels = expressions.index
skipped_gene_labels = list(set(gene_labels).difference(expressions.index))
if expressions.shape[0] < 2 or expressions.shape[1] < 2:
coordinates = [[0.0, 0.0] for i in range(expressions.shape[1])]
all_components = [[], []]
all_explained_variance_ratios = [0.0, 0.0]
else:
pca = PCA(n_components=n_components, whiten=True)
pca_expressions = pca.fit_transform(expressions.transpose())
coordinates = [t[:2].tolist() if len(t) > 1 else [t[0], 0.0] for t in pca_expressions]
all_components = [component_top_factors(component, gene_labels) for component in pca.components_]
if np.isnan(pca.explained_variance_ratio_).any():
all_explained_variance_ratios = [0.0 for _ in pca.explained_variance_ratio_]
else:
all_explained_variance_ratios = pca.explained_variance_ratio_.tolist()
result = {
'coordinates': coordinates,
'all_components': all_components,
'all_explained_variance_ratios': all_explained_variance_ratios,
'skipped_gene_labels': skipped_gene_labels,
'warning': None
}
if expressions.empty:
print(warning('Gene selection and filtering resulted in no genes. Please select different samples or genes.'))
return result
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
validate_inputs(args)
exp_type = args.exp_types[0]
spikeins_mix = args.spikeins_mix
expected = get_expected(spikeins_mix, log2=True)
for sample_name, sample_exp in zip(args.sample_names, args.sample_exps):
measured_zero = get_measured(sample_exp, sample_name, exp_type, only_zero=True)
measured_nonzero = get_measured(sample_exp, sample_name, exp_type, only_nonzero=True, log2=True)
merged_zero = merge_expected_measured(expected, measured_zero)
merged_nonzero = merge_expected_measured(expected, measured_nonzero)
# Get only ERCC spike-in's and plot the histogram-scatter figure.
if merged_nonzero.iloc[merged_nonzero.index.str.startswith('ERCC'), :].empty:
print(warning('All ERCC spike-ins have zero expression in sample {}'.format(sample_name)))
continue
plot_histogram_scatter(
expected=expected.iloc[expected.index.str.startswith('ERCC')],
zero=merged_zero.iloc[merged_zero.index.str.startswith('ERCC'), :],
nonzero=merged_nonzero.iloc[merged_nonzero.index.str.startswith('ERCC'), :],
spikein_type='ERCC',
sample_name=sample_name,
exp_type=exp_type,
)
def get_clustering(expressions,
distance_metric='euclidean',
linkage_method='average',
ordering_method=None,
n_keep=None,
n_trials=1000):
"""Compute linkage, order, and produce a dendrogram."""
if len(expressions.columns) < 2:
return np.array([]), {'leaves': list(range(len(expressions.columns)))}
try:
distances = pdist(np.transpose(np.array(expressions)),
metric=distance_metric)
if np.isnan(distances).any():
distances = np.nan_to_num(distances, copy=False)
warning(
'Distances between some samples were undefined and were set to zero.'
)
except:
msg = 'Cannot compute distances between samples.'
print(error(msg))
raise ValueError(msg)
try:
link = linkage(y=distances, method=linkage_method)
except:
msg = 'Cannot compute linkage.'
print(error(msg))
raise ValueError(msg)
if ordering_method:
if ordering_method == 'knn':
link = knn(link, distances)
elif ordering_method == 'optimal':
link = optimal(link, distances, n_keep)
elif ordering_method == 'sa':
link = simulated_annealing(link, distances, n_trials)
else:
msg = 'Unknown ordering method {}'.format(ordering_method)
print(error(msg))
raise ValueError(msg)
try:
dend = dendrogram(link, no_plot=True)
except:
msg = 'Cannot compute dendrogram.'
print(error(msg))
raise ValueError(msg)
return link, dend
def create_new_header(infile, mappings, outfile):
"""Create new header in BigWig, with UCSC chromosome names."""
with pyBigWig.open(infile) as bw:
if set(bw.chroms().keys()).issubset(mappings.values()):
# If chromosome names are already UCSC, just rename input file to output name.
# Exit with status 0 since this is normal behavior.
os.rename(infile, outfile)
sys.exit(0)
hdr = [(mappings[chrom], length)
for chrom, length in bw.chroms().items() if chrom in mappings]
if not hdr:
msg = "Neither of the chromosomes in the input file has a valid UCSC pair. No mapping will be done."
send_message(warning(msg))
os.rename(infile, outfile)
sys.exit(0)
seq_num = 0
with pyBigWig.open(outfile, "w") as bw_output:
bw_output.addHeader(hdr)
for chrom, length in bw.chroms().items():
ints = bw.intervals(chrom, 0, length)
if ints and chrom in mappings:
bw_output.addEntries(
[mappings[chrom]] * len(ints),
[x[0] for x in ints],
ends=[x[1] for x in ints],
values=[x[2] for x in ints],
)
elif chrom not in mappings:
seq_num += 1
print("UCSC chromosome/conting mapping for {} is missing".
format(chrom))
if seq_num > 0:
send_message(
warning(
"UCSC chromosome/conting mapping for {} sequence(s) is missing. "
"This sequence(s) will not be included in the bigWig file."
.format(seq_num)))
def validate_inputs(args):
"""Validate inputs."""
# Validate that all expression types are equal.
exp_type_set = set(args.exp_types)
if len(exp_type_set) != 1:
msg = "All samples should have the same expression type, but multiple expression types were given: {}."
msg = msg.format(", ".join(exp_type_set))
send_message(warning(msg))
# Validate that same number of sample names, expression files and
# expression types are given.
assert len(args.sample_names) == len(args.sample_exps) == len(args.exp_types)
def validate_inputs(args):
"""Validate inputs."""
# Validate that all expression types are equal.
exp_type_set = set(args.exp_types)
if len(exp_type_set) != 1:
msg = "All samples should have the same expression type, but multiple expression types were given: {}."
msg = msg.format(', '.join(exp_type_set))
print(warning(msg))
# Validate that same number of sample names, expression files and
# expression types are given.
assert len(args.sample_names) == len(args.sample_exps) == len(args.exp_types)
def parse_mappings(species, infile, outfile):
"""Parse file with chromosome mappings."""
mappings = dict()
# if species doesn't have prepared mapping file the script should exit with status 0 and return BigWig file
# with output name and warining
if species not in MAPPINGS_FILES:
msg = 'Chromosome mappings for Species "{}" are not supported.'.format(species)
print(warning(msg))
os.rename(infile, outfile)
sys.exit(0)
for basename in MAPPINGS_FILES[species]:
filename = os.path.join(MAPPINGS_DIR, basename)
mappings.update(parse_mapping_file(filename))
return mappings
def parse_mappings(species, infile, outfile):
"""Parse file with chromosome mappings."""
mappings = dict()
# if species doesn't have prepared mapping file the script should exit with status 0 and return BigWig file
# with output name and warining
if species not in MAPPINGS_FILES:
msg = 'Chromosome mappings for Species "{}" are not supported.'.format(
species)
send_message(warning(msg))
os.rename(infile, outfile)
sys.exit(0)
for basename in MAPPINGS_FILES[species]:
filename = os.path.join(MAPPINGS_DIR, basename)
mappings.update(parse_mapping_file(filename))
return mappings
def get_pca(expressions=pd.DataFrame(), n_components=2, gene_labels=[]):
"""Compute PCA."""
if not gene_labels:
gene_labels = expressions.index
skipped_gene_labels = list(set(gene_labels).difference(expressions.index))
if expressions.shape[0] < 2 or expressions.shape[1] < 2:
coordinates = [[0, 0] for i in range(len(expressions))]
all_components = [[], []]
all_explained_variance_ratios = [0.0, 0.0]
else:
pca = PCA(n_components=n_components, whiten=True)
pca_expressions = pca.fit_transform(expressions.transpose())
coordinates = [
t[:2].tolist() if len(t) > 1 else [t[0], 0.0]
for t in pca_expressions
]
all_components = [
component_top_factors(component, gene_labels)
for component in pca.components_
]
if np.isnan(pca.explained_variance_ratio_).any():
all_explained_variance_ratios = [
0.0 for _ in pca.explained_variance_ratio_
]
else:
all_explained_variance_ratios = pca.explained_variance_ratio_.tolist(
)
result = {
'coordinates': coordinates,
'all_components': all_components,
'all_explained_variance_ratios': all_explained_variance_ratios,
'skipped_gene_labels': skipped_gene_labels,
'warning': None
}
if expressions.empty:
print(
warning(
'Gene selection and filtering resulted in no genes. Please select different samples or genes.'
))
return result
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
gene_sets = create_gene_sets(args.dge_file, args.logfc, args.fdr)
fname_prefix = generate_name(args.analysis_name, args.tool, args.logfc,
args.fdr)
out_dir = Path(args.out_dir)
if not out_dir.exists():
out_dir.mkdir()
for name, data in gene_sets.items():
if data.empty:
send_message(
warning(
f"No {name}-regulated genes. Gene set was not created."))
else:
save_genes(data, out_dir / f"{fname_prefix}_{name}.tab.gz")
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
validate_inputs(args)
exp_type = args.exp_types[0]
spikeins_mix = args.spikeins_mix
expected = get_expected(spikeins_mix, log2=True)
for sample_name, sample_exp in zip(args.sample_names, args.sample_exps):
measured_zero = get_measured(sample_exp,
sample_name,
exp_type,
only_zero=True)
measured_nonzero = get_measured(sample_exp,
sample_name,
exp_type,
only_nonzero=True,
log2=True)
merged_zero = merge_expected_measured(expected, measured_zero)
merged_nonzero = merge_expected_measured(expected, measured_nonzero)
# Get only ERCC spike-in's and plot the histogram-scatter figure.
if merged_nonzero.iloc[
merged_nonzero.index.str.startswith('ERCC'), :].empty:
print(
warning('All ERCC spike-ins have zero expression in sample {}'.
format(sample_name)))
continue
plot_histogram_scatter(
expected=expected.iloc[expected.index.str.startswith('ERCC')],
zero=merged_zero.iloc[merged_zero.index.str.startswith('ERCC'), :],
nonzero=merged_nonzero.iloc[
merged_nonzero.index.str.startswith('ERCC'), :],
spikein_type='ERCC',
sample_name=sample_name,
exp_type=exp_type,
)
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
with open(args.geneset_file, "rU") as infile:
# skip empty lines in input gene set file
genes = [str(line.strip()) for line in infile if line.strip()]
geneset = sorted(set(genes))
if len(genes) != len(geneset):
send_message(warning("Removed duplicated genes."))
with open(args.output_json, "w") as json_out:
json.dump({"genes": geneset},
json_out,
separators=(",", ":"),
allow_nan=False)
with gzip.open(args.output_file, "w") as file_out:
file_out.write("\n".join(geneset).encode("utf-8"))
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('vcf_file',
help="VCF file (can be compressed using gzip/bgzip).")
parser.add_argument('summary', help="Summary file to append to.")
args = parser.parse_args()
try:
vcf = VariantFile(args.vcf_file)
except (OSError, ValueError) as error_msg:
proc_error = 'Input VCF file does not exist or could not be correctly opened.'
print(error(proc_error))
raise ValueError(error_msg)
vcf_header = vcf.header
header_records = {record.key: record.value for record in vcf_header.records}
with open(args.summary, "a") as out_file:
try:
fasta_name = os.path.basename(header_records['reference'])
except KeyError:
fasta_name = ''
print(
warning(
'Reference sequence (FASTA) name could not be recognized from the VCF header.'
))
out_file.write('\nReference (genome) sequence:\n{}\n'.format(fasta_name))
out_file.write('\nSamples:\n{}'.format('\n'.join(list(
vcf_header.samples))))
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
if args.norm_expressions and args.norm_expressions_type:
if len(args.norm_expressions) != len(args.norm_expressions_type):
print(
error(
'The number of additional expression files must match the number of specified '
'expressions types.'))
sys.exit(1)
if args.norm_expressions_type:
exp_types = [args.expressions_type] + args.norm_expressions_type
if len(exp_types) != len(set(exp_types)):
print(
error(
'The union of the main expression type ({}) and additional normalized expression types {} '
'does not contain unique items.'.format(
args.expressions_type, args.norm_expressions_type)))
sys.exit(1)
res = resdk.Resolwe()
feature_dict = {}
df = parse_expression_file(args.expressions, args.expressions_type)
# Get a list of feature IDs
input_features = df['FEATURE_ID'].tolist()
# Split feature IDs into chunks with max size of 10000 elements
features_sublists = [
input_features[i:i + CHUNK_SIZE]
for i in range(0, len(input_features), CHUNK_SIZE)
]
# Fetch features from KB and add them to {feature_id: feature_name} mapping dict
for fsublist in features_sublists:
features = res.feature.filter(source=args.source_db,
species=args.species,
feature_id=fsublist)
feature_dict.update({f.feature_id: f.name for f in features})
# Map gene symbols to feature IDs
df['GENE_SYMBOL'] = df['FEATURE_ID'].map(feature_dict)
# Check if all of the input feature IDs could be mapped to the gene symbols
if not all(f_id in feature_dict for f_id in input_features):
print(
warning(
'{} feature(s) could not be mapped to the associated feature symbols.'
.format(sum(df.isnull().values.ravel()))))
# Merge additional expression files with the original data frame
if args.norm_expressions and args.norm_expressions_type:
for exp_file, exp_type in zip(args.norm_expressions,
args.norm_expressions_type):
exp_df = parse_expression_file(exp_file, exp_type)
df = df.merge(exp_df, on='FEATURE_ID')
# Reorder the columns in dataframe
columns = ['FEATURE_ID', 'GENE_SYMBOL', args.expressions_type]
if args.norm_expressions_type:
columns = columns + args.norm_expressions_type
df = df[columns]
# Replace NaN values with empty string
df.fillna('', inplace=True)
# Write to file
df.to_csv(args.output_name + '.txt.gz',
header=True,
index=False,
sep='\t',
compression='gzip')
# Write to JSON
df_dict = df.set_index('FEATURE_ID').to_dict(orient='index')
with open(args.output_name + '.json', 'w') as f:
json.dump({'genes': df_dict}, f, allow_nan=False)
def warning(self, *args):
"""Log warning message."""
report = resolwe_runtime_utils.warning(' '.join([str(x) for x in args]))
# TODO: Use the protocol to report progress.
print(report)
import pandas as pd
from resolwe_runtime_utils import send_message, warning
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("-bed",
"--bed_file",
required=True,
help="All splice junctions in BED12 format")
parser.add_argument("-sj",
"--novel_sj",
required=True,
help="Table of annotated novel splice junctions")
if __name__ == "__main__":
args = parser.parse_args()
bed_file = args.bed_file
if os.path.getsize(bed_file) == 0:
send_message(warning("Bed file has no entries."))
os.rename(bed_file, "novel_sj.bed")
sys.exit(0)
bed = pd.read_csv(args.bed_file, delimiter="\t", header=None, dtype=str)
novel_sj = pd.read_csv(args.novel_sj, delimiter="\t", dtype=str)
bed_novel_sj = bed[bed[3].isin(novel_sj["name"])]
bed_novel_sj.to_csv("novel_sj.bed", sep="\t", index=False, header=False)
matrix = np.array(matrix)
matrix_sum = np.sum(matrix, axis=0) # sum of expressions for each gene
genes_zero = np.where(matrix_sum < 0.1)[0]
if args.filter:
matrix = np.delete(matrix, genes_zero, axis=1)
if matrix.shape[1] == 0:
raise ValueError("Expressions of all selected genes are 0")
distance = distance_map[args.dstfunc.lower()]
cluster = linkage(matrix, method=args.linkage.lower(), metric=distance)
distance_sum = cluster[:, 2].sum()
if distance_sum < 0.1:
print(warning('All sample distances are 0.'))
dend = dendrogram(cluster, no_plot=True)
sample_ids = {}
for i, sample_id in enumerate(args.sampleids):
sample_ids[i + 1] = {'id': sample_id}
output = {'cluster': {'linkage': cluster.tolist(),
'samples_names': sample_ids,
'order': dend['leaves']}}
print(json.dumps(output, separators=(',', ':')))
def main():
"""Compute sample hierarchical clustering."""
args = parse_args()
if len(args.sample_files) != len(args.sample_ids):
msg = "The number of sample files does not match the number of sample IDs."
set_error(msg)
if len(args.sample_files) != len(args.sample_names):
msg = "The number of sample files does not match the number of sample names."
set_error(msg)
if len(args.sample_files) < 2:
msg = (
"Select at least two samples to compute hierarchical clustering of samples."
)
set_error(msg)
if len(args.gene_labels) == 1 and args.distance_metric != "euclidean":
msg = (
"Select at least two genes to compute hierarchical clustering of samples with "
"correlation distance metric or use Euclidean distance metric.")
set_error(msg)
expressions, excluded = get_expressions(fnames=args.sample_files,
gene_set=args.gene_labels)
if len(expressions.index) == 0:
if not args.gene_labels:
msg = "The selected samples do not have any common genes."
else:
msg = "None of the selected genes are present in all samples."
set_error(msg)
if len(expressions.index) == 1 and args.distance_metric != "euclidean":
if not args.gene_labels:
msg = (
"The selected samples contain only one common gene ({}). At least two common "
"genes are required to compute hierarchical clustering of samples with "
"correlation distance metric. Select a different set of samples or use Euclidean "
"distance metric.".format(
get_gene_names(list(expressions.index), args.source,
args.species)[0]))
else:
msg = (
"Only one of the selected genes ({}) is present in all samples but at least two "
"such genes are required to compute hierarchical clustering of samples with "
"correlation distance metric. Select more genes or use Euclidean distance "
"metric.".format(
get_gene_names(list(expressions.index), args.source,
args.species)[0]))
set_error(msg)
expressions = transform(expressions, log2=args.log2, z_score=args.z_score)
if args.remove_const:
expressions, matches = remove_const_samples(expressions)
if len(expressions.columns) == 0:
msg = (
"All of the selected samples have constant expression across genes. Hierarchical "
"clustering of samples cannot be computed.")
set_error(msg)
if len(expressions.columns) == 1:
sample_name = [
id for i, id in enumerate(args.sample_names) if matches[i]
][0]
msg = (
"Only one of the selected samples ({}) has a non-constant expression across "
"genes. However, hierarchical clustering of samples cannot be computed with "
"just one sample.".format(sample_name))
set_error(msg)
removed = [
name for i, name in enumerate(args.sample_names) if not matches[i]
]
suffix = "" if len(removed) <= 3 else ", ..."
if removed:
msg = (
"{} of the selected samples ({}) have constant expression across genes. "
"Those samples are excluded from the computation of hierarchical clustering of "
"samples with correlation distance "
"metric.".format(len(removed),
", ".join(removed[:3]) + suffix))
send_message(warning(msg))
else:
matches = [True] * len(args.sample_files)
suffix = "" if len(excluded) <= 3 else ", ..."
if excluded:
excluded_names = get_gene_names(excluded[:3], args.source,
args.species)
if len(excluded) == 1:
if not args.gene_labels:
msg = (
"Gene {} is present in some but not all of the selected samples. This "
"gene is excluded from the computation of hierarchical clustering of "
"samples.".format(", ".join(excluded_names)))
else:
msg = (
"{} of the selected genes ({}) is missing in at least one of the selected "
"samples. This gene is excluded from the computation of hierarchical "
"clustering of samples.".format(len(excluded),
", ".join(excluded_names)))
send_message(warning(msg))
if len(excluded) > 1:
if not args.gene_labels:
msg = (
"{} genes ({}) are present in some but not all of the selected samples. Those "
"genes are excluded from the computation of hierarchical clustering of "
"samples.".format(len(excluded), ", ".join(excluded_names)))
else:
msg = (
"{} of the selected genes ({}) are missing in at least one of the selected "
"samples. Those genes are excluded from the computation of hierarchical "
"clustering of samples.".format(len(excluded),
", ".join(excluded_names)))
send_message(warning(msg))
linkage, dendrogram = get_clustering(
expressions,
distance_metric=get_distance_metric(args.distance_metric),
linkage_method=args.linkage_method,
order=args.order,
)
sample_ids = [
sample_id for i, sample_id in enumerate(args.sample_ids) if matches[i]
]
result = {
"sample_ids":
{i: {
"id": sample_id
}
for i, sample_id in enumerate(sample_ids)},
"linkage": linkage.tolist(),
"order": dendrogram["leaves"],
}
output_json(result, args.output)
import pandas as pd
from resolwe_runtime_utils import warning
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("-bed",
"--bed_file",
required=True,
help="All splice junctions in BED12 format")
parser.add_argument("-sj",
"--novel_sj",
required=True,
help="Table of annotated novel splice junctions")
if __name__ == "__main__":
args = parser.parse_args()
bed_file = args.bed_file
if os.path.getsize(bed_file) == 0:
print(warning("Bed file has no entries."))
os.rename(bed_file, "novel_sj.bed")
sys.exit(0)
bed = pd.read_csv(args.bed_file, delimiter="\t", header=None, dtype=str)
novel_sj = pd.read_csv(args.novel_sj, delimiter="\t", dtype=str)
bed_novel_sj = bed[bed[3].isin(novel_sj["name"])]
bed_novel_sj.to_csv("novel_sj.bed", sep="\t", index=False, header=False)
"""Filter novel splice junctions in BED12 format."""
import argparse
import os
import sys
import pandas as pd
from resolwe_runtime_utils import warning
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('-bed', '--bed_file', required=True, help="All splice junctions in BED12 format")
parser.add_argument('-sj', '--novel_sj', required=True, help="Table of annotated novel splice junctions")
if __name__ == "__main__":
args = parser.parse_args()
bed_file = args.bed_file
if os.path.getsize(bed_file) == 0:
print(warning('Bed file has no entries.'))
os.rename(bed_file, 'novel_sj.bed')
sys.exit(0)
bed = pd.read_csv(args.bed_file, delimiter='\t', header=None, dtype=str)
novel_sj = pd.read_csv(args.novel_sj, delimiter='\t', dtype=str)
bed_novel_sj = bed[bed[3].isin(novel_sj["name"])]
bed_novel_sj.to_csv('novel_sj.bed', sep='\t', index=False, header=False)
def warning(self, *args):
"""Log warning message."""
report = resolwe_runtime_utils.warning(' '.join([str(x) for x in args]))
# TODO: Use the protocol to report progress.
print(report)
def test_string(self):
self.assertEqual(warning('Some warning'), '{"proc.warning": "Some warning"}')
def main():
"""Compute sample hierarchical clustering."""
args = parse_args()
if len(args.sample_files) != len(args.sample_ids):
msg = 'The number of sample files does not match the number of sample IDs.'
set_error(msg)
if len(args.sample_files) != len(args.sample_names):
msg = 'The number of sample files does not match the number of sample names.'
set_error(msg)
if len(args.sample_files) < 2:
msg = 'Select at least two samples to compute hierarchical clustering of samples.'
set_error(msg)
if len(args.gene_labels) == 1 and args.distance_metric != 'euclidean':
msg = ('Select at least two genes to compute hierarchical clustering of samples with '
'correlation distance metric or use Euclidean distance metric.')
set_error(msg)
expressions, excluded = get_expressions(fnames=args.sample_files, gene_set=args.gene_labels)
if len(expressions.index) == 0:
if not args.gene_labels:
msg = 'The selected samples do not have any common genes.'
else:
msg = 'None of the selected genes are present in all samples.'
set_error(msg)
if len(expressions.index) == 1 and args.distance_metric != 'euclidean':
if not args.gene_labels:
msg = ('The selected samples contain only one common gene ({}). At least two common '
'genes are required to compute hierarchical clustering of samples with '
'correlation distance metric. Select a different set of samples or use Euclidean '
'distance metric.'.format(get_gene_names(list(expressions.index), args.source, args.species)[0]))
else:
msg = ('Only one of the selected genes ({}) is present in all samples but at least two '
'such genes are required to compute hierarchical clustering of samples with '
'correlation distance metric. Select more genes or use Euclidean distance '
'metric.'.format(get_gene_names(list(expressions.index), args.source, args.species)[0]))
set_error(msg)
expressions = transform(expressions, log2=args.log2, z_score=args.z_score)
if args.remove_const:
expressions, matches = remove_const_samples(expressions)
if len(expressions.columns) == 0:
msg = ('All of the selected samples have constant expression across genes. Hierarchical '
'clustering of samples cannot be computed.')
set_error(msg)
if len(expressions.columns) == 1:
sample_name = [id for i, id in enumerate(args.sample_names) if matches[i]][0]
msg = ('Only one of the selected samples ({}) has a non-constant expression across '
'genes. However, hierarchical clustering of samples cannot be computed with '
'just one sample.'.format(sample_name))
set_error(msg)
removed = [name for i, name in enumerate(args.sample_names) if not matches[i]]
suffix = '' if len(removed) <= 3 else ', ...'
if removed:
msg = ('{} of the selected samples ({}) have constant expression across genes. '
'Those samples are excluded from the computation of hierarchical clustering of '
'samples with correlation distance '
'metric.'.format(len(removed), ', '.join(removed[:3]) + suffix))
print(warning(msg))
else:
matches = [True] * len(args.sample_files)
suffix = '' if len(excluded) <= 3 else ', ...'
if excluded:
excluded_names = get_gene_names(excluded[:3], args.source, args.species)
if len(excluded) == 1:
if not args.gene_labels:
msg = ('Gene {} is present in some but not all of the selected samples. This '
'gene is excluded from the computation of hierarchical clustering of '
'samples.'.format(len(excluded), ', '.join(excluded_names)))
else:
msg = ('{} of the selected genes ({}) is missing in at least one of the selected '
'samples. This gene is excluded from the computation of hierarchical '
'clustering of samples.'.format(len(excluded), ', '.join(excluded_names)))
print(warning(msg))
if len(excluded) > 1:
if not args.gene_labels:
msg = ('{} genes ({}) are present in some but not all of the selected samples. Those '
'genes are excluded from the computation of hierarchical clustering of '
'samples.'.format(len(excluded), ', '.join(excluded_names)))
else:
msg = ('{} of the selected genes ({}) are missing in at least one of the selected '
'samples. Those genes are excluded from the computation of hierarchical '
'clustering of samples.'.format(len(excluded), ', '.join(excluded_names)))
print(warning(msg))
linkage, dendrogram = get_clustering(
expressions,
distance_metric=get_distance_metric(args.distance_metric),
linkage_method=args.linkage_method,
order=args.order
)
sample_ids = [sample_id for i, sample_id in enumerate(args.sample_ids) if matches[i]]
result = {
'sample_ids': {i: {'id': sample_id} for i, sample_id in enumerate(sample_ids)},
'linkage': linkage.tolist(),
'order': dendrogram['leaves'],
}
output_json(result, args.output)
def test_string(self):
self.assertEqual(warning('Some warning'),
'{"proc.warning": "Some warning"}')
if args.filter:
matrix = np.delete(matrix, genes_zero, axis=1)
if matrix.shape[1] == 0:
msg = "Expressions of selected genes are 0. Please select additional genes."
print(error(msg))
raise ValueError(msg)
distance = distance_map[args.dstfunc]
cluster = linkage(matrix, method=args.linkage, metric=distance)
distance_sum = cluster[:, 2].sum()
if distance_sum < 0.1:
msg = 'All sample distances are 0.'
print(warning(msg))
dend = dendrogram(cluster, no_plot=True)
sample_ids = {}
for i, sample_id in enumerate(args.sampleids):
sample_ids[i + 1] = {'id': int(sample_id)}
output = {
'cluster': {
'linkage': cluster.tolist(),
'samples_names': sample_ids,
'order': dend['leaves']
}
}
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
res = resdk.Resolwe()
with open(args.feature_ids) as gene_file:
genes = [gene.strip() for gene in gene_file]
org_features = res.feature.filter(source=args.source_db,
species=args.species,
feature_id=genes)
if len(org_features) == 0:
print(error("No genes were fetched from the knowledge base."))
exit(1)
if args.source_db == args.target_db:
target_ids = genes
else:
mapping_res = res.mapping.filter(
source_db=args.source_db,
source_species=args.species,
target_db=args.target_db,
target_species=args.species,
source_id=genes,
)
if len(mapping_res) == 0:
print(error("Failed to map features."))
exit(1)
mappings = {}
for m in mapping_res:
if m.source_id in genes:
if m.source_id not in mappings:
mappings[m.source_id] = m.target_id
else:
print(
warning(
"Mapping {} returned multiple times.".format(m)))
if len(genes) > len(mappings):
print(warning("Not all features could be mapped."))
target_ids = mappings.values()
with tempfile.NamedTemporaryFile() as input_genes:
input_genes.write(' '.join(target_ids).encode("UTF-8"))
input_genes.flush()
process = Popen([
'processor',
str(args.pval),
str(args.min_genes), args.obo, args.gaf, input_genes.name
],
stdout=PIPE,
stderr=DEVNULL)
out, err = process.communicate()
with open('terms.json', 'w') as f:
f.write(out.decode("UTF-8"))
def main():
"""Compute gene hierarchical clustering."""
args = parse_args()
if len(args.sample_files) != len(args.sample_names):
msg = 'The number of sample files does not match the number of sample names.'
set_error(msg)
if len(args.gene_labels) == 1:
msg = 'Select at least two genes to compute hierarchical clustering of genes.'
set_error(msg)
if len(args.sample_files) == 1 and args.distance_metric != 'euclidean':
msg = (
'Select at least two samples to compute hierarchical clustering of genes with '
'correlation distance metric or use Euclidean distance metric.')
set_error(msg)
expressions, excluded = get_expressions(fnames=args.sample_files,
gene_set=args.gene_labels)
if len(expressions.index) == 0:
if not args.gene_labels:
msg = 'The selected samples do not have any common genes.'
else:
msg = 'None of the selected genes are present in all samples.'
set_error(msg)
if len(expressions.index) == 1 and args.distance_metric != 'euclidean':
if not args.gene_labels:
msg = (
'The selected samples contain only one common gene ({}). At least two common '
'genes are required to compute hierarchical clustering of genes with '
'correlation distance metric. Select a different set of samples or use Euclidean '
'distance metric.'.format(
get_gene_names(list(expressions.index), args.source,
args.species)[0]))
else:
msg = (
'Only one of the selected genes ({}) is present in all samples but at least two '
'such genes are required to compute hierarchical clustering of genes with '
'correlation distance metric. Select more genes or use Euclidean distance '
'metric.'.format(
get_gene_names(list(expressions.index), args.source,
args.species)[0]))
set_error(msg)
expressions = transform(expressions, log2=args.log2, z_score=args.z_score)
if args.remove_const:
expressions, matches = remove_const_genes(expressions)
if len(expressions.index) == 0:
msg = (
'All of the selected genes have constant expression across samples. '
'Hierarchical clustering of genes cannot be computed.')
set_error(msg)
if len(expressions.index) == 1:
gene_names = get_gene_names(list(expressions.index), args.source,
args.species)
msg = (
'Only one of the selected genes ({}) has a non-constant expression across '
'samples. However, hierarchical clustering of genes cannot be computed with '
'just one gene.'.format(gene_names[0]))
set_error(msg)
removed = [
name for i, name in enumerate(expressions.index) if not matches[i]
]
suffix = '' if len(removed) <= 3 else ', ...'
if removed:
removed_names = get_gene_names(removed[:3], args.source,
args.species)
msg = (
'{} of the selected genes ({}) have constant expression across samples. '
'Those genes are excluded from the computation of hierarchical clustering of '
'genes with correlation distance '
'metric.'.format(len(removed),
', '.join(removed_names) + suffix))
print(warning(msg))
else:
matches = [True] * len(expressions.index)
suffix = '' if len(excluded) <= 3 else ', ...'
if excluded:
excluded_names = get_gene_names(excluded[:3], args.source,
args.species)
if len(excluded) == 1:
if not args.gene_labels:
msg = (
'Gene {} is present in some but not all of the selected samples. This '
'gene is excluded from the computation of hierarchical clustering of '
'genes.'.format(len(excluded), ', '.join(excluded_names)))
else:
msg = (
'{} of the selected genes ({}) is missing in at least one of the selected '
'samples. This gene is excluded from the computation of hierarchical '
'clustering of genes.'.format(len(excluded),
', '.join(excluded_names)))
print(warning(msg))
if len(excluded) > 1:
if not args.gene_labels:
msg = (
'{} genes ({}) are present in some but not all of the selected samples. Those '
'genes are excluded from the computation of hierarchical clustering of '
'genes.'.format(len(excluded), ', '.join(excluded_names)))
else:
msg = (
'{} of the selected genes ({}) are missing in at least one of the selected '
'samples. Those genes are excluded from the computation of hierarchical '
'clustering of genes.'.format(len(excluded),
', '.join(excluded_names)))
print(warning(msg))
linkage, dendrogram = get_clustering(expressions,
distance_metric=get_distance_metric(
args.distance_metric),
linkage_method=args.linkage_method,
order=args.order)
result = {
'gene_symbols':
{i: {
'gene': gene
}
for i, gene in enumerate(expressions.index)},
'linkage': linkage.tolist(),
'order': dendrogram['leaves'],
}
output_json(result, args.output)
def main():
"""Invoke when run directly as a program."""
args = parse_arguments()
if args.norm_expressions and args.norm_expressions_type:
if len(args.norm_expressions) != len(args.norm_expressions_type):
print(error('The number of additional expression files must match the number of specified '
'expressions types.'))
sys.exit(1)
if args.norm_expressions_type:
exp_types = [args.expressions_type] + args.norm_expressions_type
if len(exp_types) != len(set(exp_types)):
print(error('The union of the main expression type ({}) and additional normalized expression types {} '
'does not contain unique items.'.format(args.expressions_type, args.norm_expressions_type)))
sys.exit(1)
res = resdk.Resolwe()
feature_dict = {}
df = parse_expression_file(args.expressions, args.expressions_type)
# Get a list of feature IDs
input_features = df['FEATURE_ID'].tolist()
# Split feature IDs into chunks with max size of 10000 elements
features_sublists = [input_features[i:i + CHUNK_SIZE] for i in range(0, len(input_features), CHUNK_SIZE)]
# Fetch features from KB and add them to {feature_id: feature_name} mapping dict
for fsublist in features_sublists:
features = res.feature.filter(source=args.source_db, species=args.species, feature_id=fsublist)
feature_dict.update({f.feature_id: f.name for f in features})
# Map gene symbols to feature IDs
df['GENE_SYMBOL'] = df['FEATURE_ID'].map(feature_dict)
# Check if all of the input feature IDs could be mapped to the gene symbols
if not all(f_id in feature_dict for f_id in input_features):
print(warning('{} feature(s) could not be mapped to the associated feature symbols.'.format(
sum(df.isnull().values.ravel())))
)
# Merge additional expression files with the original data frame
if args.norm_expressions and args.norm_expressions_type:
for exp_file, exp_type in zip(args.norm_expressions, args.norm_expressions_type):
exp_df = parse_expression_file(exp_file, exp_type)
df = df.merge(exp_df, on='FEATURE_ID')
# Reorder the columns in dataframe
columns = ['FEATURE_ID', 'GENE_SYMBOL', args.expressions_type]
if args.norm_expressions_type:
columns = columns + args.norm_expressions_type
df = df[columns]
# Replace NaN values with empty string
df.fillna('', inplace=True)
# Write to file
df.to_csv(args.output_name + '.txt.gz', header=True, index=False, sep='\t', compression='gzip')
# Write to JSON
df_dict = df.set_index('FEATURE_ID').to_dict(orient='index')
with open(args.output_name + '.json', 'w') as f:
json.dump({'genes': df_dict}, f, allow_nan=False)
matrix_sum = np.sum(matrix, axis=0) # sum of expressions for each gene
genes_zero = np.where(matrix_sum < 0.1)[0]
if args.filter:
matrix = np.delete(matrix, genes_zero, axis=1)
if matrix.shape[1] == 0:
msg = "Expressions of selected genes are 0. Please select additional genes."
print(error(msg))
raise ValueError(msg)
distance = distance_map[args.dstfunc]
cluster = linkage(matrix, method=args.linkage, metric=distance)
distance_sum = cluster[:, 2].sum()
if distance_sum < 0.1:
msg = 'All sample distances are 0.'
print(warning(msg))
dend = dendrogram(cluster, no_plot=True)
sample_ids = {}
for i, sample_id in enumerate(args.sampleids):
sample_ids[i + 1] = {'id': int(sample_id)}
output = {'cluster': {'linkage': cluster.tolist(),
'samples_names': sample_ids,
'order': dend['leaves']}}
print(json.dumps(output, separators=(',', ':')))
import pandas as pd
from resolwe_runtime_utils import warning
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('-bed',
'--bed_file',
required=True,
help="All splice junctions in BED12 format")
parser.add_argument('-sj',
'--novel_sj',
required=True,
help="Table of annotated novel splice junctions")
if __name__ == "__main__":
args = parser.parse_args()
bed_file = args.bed_file
if os.path.getsize(bed_file) == 0:
print(warning('Bed file has no entries.'))
os.rename(bed_file, 'novel_sj.bed')
sys.exit(0)
bed = pd.read_csv(args.bed_file, delimiter='\t', header=None, dtype=str)
novel_sj = pd.read_csv(args.novel_sj, delimiter='\t', dtype=str)
bed_novel_sj = bed[bed[3].isin(novel_sj["name"])]
bed_novel_sj.to_csv('novel_sj.bed', sep='\t', index=False, header=False)