def ma_run(partition):
log = task.logger
conf = task.conf
results_port = task.ports("results")
project = partition["project"]
log.info("--- [{0} @ {1}] --------------------------------------------".format(project["id"], partition["index"]))
offline = conf["offline"]
if offline == "yes":
log.info("Running Mutation assessor in local mode.")
ma = MaLocal(conf["ma_cache_path"])
else:
log.info("Running Mutation assessor using web services.")
ma = MaService(project["assembly"], cache_path=os.path.join(conf["cache_path"], "ma.db"))
results_path = os.path.join(partition["base_path"], "{0:08d}.ma".format(partition["index"]))
if not os.path.exists(results_path) or conf.get("consequences_overwrite", True):
log.info("Querying Mutation assessor for 'missense_variant' consequences ...")
projdb = ProjectDb(project["db"])
missense_variants = set()
with open(partition["vep_path"], "r") as f:
for line in f:
fields = line.rstrip().split("\t")
var_id = int(fields[0])
ctypes = fields[3].split(",")
if so.match(ctypes, so.NON_SYNONYMOUS):
missense_variants.add(var_id)
with open(results_path, "w") as mf:
for var_id in missense_variants:
var = projdb.get_variant(var_id)
start, end, ref, alt = var_to_tab(var)
r = ma.get(var.chr, var.strand, start, ref, alt, var_id)
if r is not None:
tsv.write_line(mf, var_id, r.uniprot, r.fi_score, null_value="-")
projdb.close()
else:
log.warn("Skipping MA, results already exist.")
log.debug("MA results: {0}".format(results_path))
ma.close()
# Send results to the next module
partition["ma_path"] = results_path
results_port.send(partition)
def datasets(projects_set):
log = task.logger
conf = task.conf
classifier, projects = projects_set
classifier_id = classifier["id"]
group_values = classifier["group_values"]
short_values = classifier["group_short_values"]
long_values = classifier["group_long_values"]
group_name = classifier["group_name"]
group_short_name = classifier["group_short_name"]
group_long_name = classifier["group_long_name"]
group_file_prefix = normalize_id(classifier_id)
log.info("--- [{0} ({1}) ({2}) ({3})] {4}".format(classifier["name"], group_long_name, group_short_name, group_name, "-" * 30))
log.info("Reading number of samples per project ...")
project_ids = []
total_samples = 0
for project in projects:
project_id = project["id"]
project_ids += [project_id]
log.info(" Project {0}".format(project["id"]))
projdb = ProjectDb(project["db"])
num_samples = projdb.get_total_affected_samples()
total_samples += num_samples
log.debug(" {0} samples".format(num_samples))
projdb.close()
log.debug(" {0} samples in total".format(total_samples))
log.info("Updating ...")
combination_path = get_combination_path(conf)
path = os.path.join(combination_path, "{0}.tsv".format(group_file_prefix))
if not os.path.exists(path):
with open(path, "w") as f:
tsv.write_line(f, "NAME", "SHORT_NAME", "LONG_NAME", "SAMPLES_TOTAL", "PROJECT_IDS")
with open(path, "a") as f:
tsv.write_line(f, group_name, group_short_name, group_long_name, total_samples, ",".join(project_ids))
def variants(project):
log = task.logger
conf = task.conf
log.info("--- [{0}] --------------------------------------------".format(project["id"]))
log.info("Calculating number of variants processed in each step ...")
proj_res = ProjectResults(project)
projdb = ProjectDb(project["db"])
counts = projdb.count_variants()
proj_res.save_quality_control("variants", counts)
projdb.close()
def update_db(project):
log = task.logger
conf = task.conf
projects_out_port = task.ports("projects_out")
project_id = project["id"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
oclust = project["oncodriveclust"]
del project["oncodriveclust"]
if not os.path.exists(oclust["results"]):
log.warn("No results have been found. Skipping it.")
return
log.info("Updating the project database ...")
projdb = ProjectDb(project["db"])
exc_path = os.path.join(project["temp_path"], "oncodriveclust-excluded-cause.tsv")
log.info(" Excluded gene causes ...")
log.debug(" > {0}".format(exc_path))
count = 0
with tsv.open(exc_path, "r") as exf:
for gene, cause in tsv.lines(exf, (str, str), header=True):
projdb.update_gene(Gene(id=gene, clust_exc_cause=cause))
count += 1
log.debug(" {0} genes excluded".format(count))
log.info(" OncodriveCLUST results ...")
with tsv.open(oclust["results"], "r") as f:
types = (str, str, float, float, float)
columns = ("GENE", "CLUST_COORDS", "ZSCORE", "PVALUE", "QVALUE")
for gene, coords, zscore, pvalue, qvalue in tsv.lines(f, types, columns=columns, header=True, null_value="NA"):
projdb.update_gene(Gene(id=gene, clust_coords=coords, clust_zscore=zscore, clust_pvalue=pvalue,
clust_qvalue=qvalue, clust_exc_cause=ProjectDb.NO_GENE_EXC))
projdb.commit()
projdb.close()
projects_out_port.send(project)
def oncodriveclust(project):
log = task.logger
conf = task.conf
log.info("--- [{0}] --------------------------------------------".format(project["id"]))
source_genes = {}
syn_genes = set()
selected_genes = set()
filter_genes = set()
threshold_genes = set()
source_samples = {}
selected_samples = set()
filter_samples = set()
threshold_samples = set()
selected_gene_sample_count = {} # number of samples for each selected gene
filter_gene_sample_count = {} # number of samples per each gene passing the filter
# get configuration
samples_threshold, genes_filter_enabled, genes_filter, filt = get_oncodriveclust_configuration(log, conf, project)
log.info("Retrieving gene alterations ...")
projdb = ProjectDb(project["db"])
data = set()
for csq in projdb.consequences(join_samples=True):
# filters={ProjectDb.CSQ_CTYPES : so.PROTEIN_AFFECTING | so.SYNONYMOUS}):
is_selected = so.match(csq.ctypes, so.PROTEIN_AFFECTING)
is_synonymous = so.match(csq.ctypes, so.SYNONYMOUS)
if csq.gene not in source_genes:
source_genes[csq.gene] = gene_index = len(source_genes)
if is_selected:
selected_genes.add(gene_index)
if is_synonymous:
syn_genes.add(gene_index)
for sample in csq.var.samples:
if sample.name not in source_samples:
source_samples[sample.name] = sample_index = len(source_samples)
if is_selected:
selected_samples.add(sample_index)
data.add((csq.gene, sample_index))
projdb.close()
log.info("Counting selected, filtered and threshold ...")
# calculate selected and filter counts
data2 = set()
for gene, sample_index in data:
gene_index = source_genes[gene]
if gene_index not in selected_gene_sample_count:
selected_gene_sample_count[gene_index] = 1
else:
selected_gene_sample_count[gene_index] += 1
if filt.valid(gene):
data2.add((gene_index, sample_index))
filter_genes.add(gene_index)
filter_samples.add(sample_index)
if gene_index not in filter_gene_sample_count:
filter_gene_sample_count[gene_index] = 1
else:
filter_gene_sample_count[gene_index] += 1
# calculate threshold counts
for gene_index, sample_index in data2:
if selected_gene_sample_count[gene_index] >= samples_threshold:
threshold_genes.add(gene_index)
threshold_samples.add(sample_index)
log.info("Counting significant genes ...")
# significance of q-values
projdb = ProjectDb(project["db"])
sig_thresholds = [0.0, 0.001, 0.005] + [i / 100.0 for i in range(1, 11)] + [1.0]
sig_count = [0] * len(sig_thresholds)
for gene in projdb.genes():
if gene.id in source_genes and source_genes[gene.id] in threshold_genes:
i = 0
while i < len(sig_thresholds) and gene.fm_qvalue > sig_thresholds[i]:
i += 1
for j in range(i, len(sig_count)):
sig_count[j] += 1
projdb.close()
source_genes_count = len(source_genes)
syn_genes_count = len(syn_genes)
selected_genes_count = len(selected_genes)
filter_genes_count = len(filter_genes)
threshold_genes_count = len(threshold_genes)
source_samples_count = len(source_samples)
selected_samples_count = len(selected_samples)
filter_samples_count = len(filter_samples)
threshold_samples_count = len(threshold_samples)
sorted_filter_genes = sorted(filter_genes, reverse=True, key=lambda gi: filter_gene_sample_count[gi])
qc_data = dict(
source=dict(
genes=sorted(source_genes.keys(), key=lambda k: source_genes[k]),
genes_count=source_genes_count,
genes_lost_count=max(0, source_genes_count - syn_genes_count - threshold_genes_count),
samples=sorted(source_samples.keys(), key=lambda k: source_samples[k]),
samples_count=source_samples_count,
),
samples_lost_count=max(0, source_samples_count - threshold_samples_count),
synonymous=dict(
genes=sorted(syn_genes),
genes_count=syn_genes_count,
ratio=(float(syn_genes_count) / selected_genes_count) if selected_genes_count > 0 else 0,
),
selected=dict(
genes=sorted(selected_genes),
genes_count=selected_genes_count,
genes_lost=sorted(set(source_genes.values()) - syn_genes - selected_genes),
genes_lost_count=max(0, source_genes_count - syn_genes_count - selected_genes_count),
samples=sorted(selected_samples),
samples_count=selected_samples_count,
samples_lost=sorted(set(source_samples.values()) - selected_samples),
samples_lost_count=max(0, source_samples_count - selected_samples_count),
),
filter=dict(
genes=sorted_filter_genes,
genes_count=filter_genes_count,
genes_lost=sorted(selected_genes - filter_genes),
genes_lost_count=max(0, selected_genes_count - filter_genes_count),
genes_sample_count=[filter_gene_sample_count[gene_index] for gene_index in sorted_filter_genes],
samples=sorted(filter_samples),
samples_count=filter_samples_count,
samples_lost=sorted(selected_samples - filter_samples),
samples_lost_count=max(0, selected_samples_count - filter_samples_count),
),
threshold=dict(
genes=sorted(threshold_genes),
genes_count=threshold_genes_count,
genes_lost=sorted(filter_genes - threshold_genes),
genes_lost_count=max(0, filter_genes_count - threshold_genes_count),
samples=sorted(threshold_samples),
samples_count=threshold_samples_count,
samples_threshold=samples_threshold,
samples_lost=sorted(filter_samples - threshold_samples),
samples_lost_count=max(0, filter_samples_count - threshold_samples_count),
),
results=dict(sig_thresholds=sig_thresholds[1:], sig_count=sig_count[1:]),
)
project_results = ProjectResults(project)
project_results.save_quality_control("oncodriveclust", qc_data)
def quality_control(log, conf, project, filt):
data = {}
projdb = ProjectDb(project["db"])
for csq in projdb.consequences(join_samples=True, join_ctypes=True):#,
#filters={ProjectDb.CSQ_CTYPES : so.ONCODRIVEFM}):
is_selected = so.match(csq.ctypes, so.ONCODRIVEFM)
var = csq.var
for sample in var.samples:
key = (sample.id, csq.gene)
if key not in data:
data[key] = is_selected
else:
data[key] = data[key] or is_selected
projdb.close()
source_genes = {}
selected_genes = set()
filter_genes = set()
threshold_genes = set()
selected_gene_sample_count = {} # number of samples for each selected gene
filter_gene_sample_count = {} # number of samples per gene
source_samples = {}
selected_samples = set()
filter_samples = set()
threshold_samples = set()
for (sample, gene), is_selected in data.items():
if sample in source_samples:
sample_index = source_samples[sample]
else:
source_samples[sample] = sample_index = len(source_samples)
if is_selected:
selected_samples.add(sample_index)
increment(selected_gene_sample_count, gene)
samples_threshold = get_threshold(log, conf, project,
"oncodrivefm.genes.threshold", ONCODRIVEFM_GENES_THRESHOLD, len(selected_samples))
for (sample, gene), is_selected in data.items():
if gene not in source_genes:
source_genes[gene] = len(source_genes)
gi = source_genes[gene]
sample_index = source_samples[sample]
if is_selected:
if filt is None or filt.valid(gene):
filter_samples.add(sample_index)
increment(filter_gene_sample_count, gi)
if selected_gene_sample_count[gene] >= samples_threshold:
threshold_samples.add(sample_index)
for gene, sample_count in selected_gene_sample_count.items():
gi = source_genes[gene]
selected_genes.add(gi)
if filt is None or filt.valid(gene):
filter_genes.add(gi)
if sample_count >= samples_threshold:
threshold_genes.add(gi)
# significance of q-values
projdb = ProjectDb(project["db"])
sig_thresholds = [0.0, 0.001, 0.005] + [i / 100.0 for i in range(1, 11)] + [1.0]
sig_count = [0] * len(sig_thresholds)
for gene in projdb.genes():
if gene.id in source_genes and source_genes[gene.id] in threshold_genes:
i = 0
while i < len(sig_thresholds) and gene.fm_qvalue > sig_thresholds[i]:
i += 1
for j in range(i, len(sig_count)):
sig_count[j] += 1
projdb.close()
source_samples_count = len(source_samples)
selected_samples_count = len(selected_samples)
filter_samples_count = len(filter_samples)
threshold_samples_count = len(threshold_samples)
source_genes_count = len(source_genes)
selected_genes_count = len(selected_genes)
filter_genes_count = len(filter_genes)
threshold_genes_count = len(threshold_genes)
sorted_filter_genes = sorted(filter_genes, reverse=True, key=lambda gi: filter_gene_sample_count[gi])
qc_data = dict(
source=dict(
genes=sorted(source_genes.keys(), key=lambda k: source_genes[k]),
genes_count=source_genes_count,
genes_lost_count=max(0, source_genes_count - threshold_genes_count),
samples=sorted(source_samples.keys(), key=lambda k: source_samples[k]),
samples_count=source_samples_count),
samples_lost_count=max(0, source_samples_count - threshold_samples_count),
selected=dict(
genes=sorted(selected_genes),
genes_count=selected_genes_count,
genes_lost=sorted(set(source_genes.values()) - selected_genes),
genes_lost_count=max(0, source_genes_count - selected_genes_count),
samples=sorted(selected_samples),
samples_count=selected_samples_count,
samples_lost=sorted(set(source_samples.values()) - selected_samples),
samples_lost_count=max(0, source_samples_count - selected_samples_count)),
filter=dict(
genes=sorted_filter_genes,
genes_count=filter_genes_count,
genes_lost=sorted(selected_genes - filter_genes),
genes_lost_count=max(0, selected_genes_count - filter_genes_count),
genes_sample_count=[filter_gene_sample_count[gi] for gi in sorted_filter_genes],
samples=sorted(filter_samples),
samples_count=filter_samples_count,
samples_lost=sorted(selected_samples - filter_samples),
samples_lost_count=max(0, selected_samples_count - filter_samples_count)),
threshold=dict(
genes=sorted(threshold_genes),
genes_count=threshold_genes_count,
genes_lost=sorted(filter_genes - threshold_genes),
genes_lost_count=max(0, filter_genes_count - threshold_genes_count),
samples=sorted(threshold_samples),
samples_count=threshold_samples_count,
samples_threshold=samples_threshold,
samples_lost=sorted(filter_samples - threshold_samples),
samples_lost_count=max(0, filter_samples_count - threshold_samples_count)),
results=dict(
sig_thresholds=sig_thresholds[1:],
sig_count=sig_count[1:])
)
return qc_data
def pack_results(project):
log = task.logger
conf = task.conf
project_id = project["id"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
project_path = project["path"]
temp_path = project["temp_path"]
dest_path = os.path.join(project_path, "results.zip")
sigdb = SigDb(conf["sigdb_path"])
sigdb.open()
projdb = ProjectDb(project["db"])
projres = ProjectResults(project)
gene_sym = projdb.get_gene_symbols()
total_samples = projdb.get_total_affected_samples()
log.info("Compressing files ...")
arc = None
try:
arc = Archive(dest_path, mode="w", fmt="zip")
log.info(" Variant genes ...")
with ArcFile(task, arc, project_id, "variant_genes", "w") as vf:
write_line(vf, "PROJECT_ID", "CHR", "STRAND", "START", "ALLELE",
"GENE_ID", "SYMBOL", "VAR_IMPACT", "VAR_IMPACT_DESC",
"SAMPLE_FREQ", "SAMPLE_TOTAL", "SAMPLE_PROP",
"CODING_REGION", "PROTEIN_CHANGES", "INTOGEN_DRIVER", "XREFS")
for afg in projdb.affected_genes(join_variant=True, join_xrefs=True, join_rec=True):
var = afg.var
rec = afg.rec
start, end, ref, alt = var_to_tab(var)
xrefs = [xref for xref in var.xrefs]
if sigdb.exists_variant(var.chr, start):
xrefs += ["I:1"]
xrefs = ",".join(xrefs)
intogen_driver = 1 if sigdb.exists_gene(afg.gene_id) else 0
write_line(vf, project_id, var.chr, var.strand, start, "{0}/{1}".format(ref, alt),
afg.gene_id, gene_sym.get(afg.gene_id),
afg.impact, TransFIC.class_name(afg.impact),
rec.sample_freq or 0, total_samples, rec.sample_prop or 0,
afg.coding_region, afg.prot_changes, intogen_driver, xrefs)
log.info(" Variant samples ...")
with ArcFile(task, arc, project_id, "variant_samples", "w") as vf:
write_line(vf, "PROJECT_ID", "CHR", "STRAND", "START", "ALLELE", "SAMPLES")
for var in projdb.variants(join_samples=True):
start, end, ref, alt = var_to_tab(var)
write_line(vf, project_id, var.chr, var.strand, start, "{0}/{1}".format(ref, alt),
",".join([s.name for s in var.samples]))
log.info(" Consequences ...")
with ArcFile(task, arc, project_id, "consequences", "w") as cf:
write_line(cf, "PROJECT_ID", "CHR", "STRAND", "START", "ALLELE", "TRANSCRIPT_ID", "CT",
"GENE_ID", "SYMBOL", "UNIPROT_ID", "PROTEIN_ID", "PROTEIN_POS", "AA_CHANGE",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS",
"IMPACT", "IMPACT_CLASS")
for csq in projdb.consequences(join_variant=True):
var = csq.var
start, end, ref, alt = var_to_tab(var)
allele = "{0}/{1}".format(ref, alt)
uniprot = protein = protein_pos = aa_change = None
sift_score = sift_tfic = sift_tfic_class = None
pph2_score = pph2_tfic = pph2_tfic_class = None
ma_score = ma_tfic = ma_tfic_class = None
if so.match(csq.ctypes, so.ONCODRIVEFM):
uniprot, protein = csq.uniprot, csq.protein
if so.match(csq.ctypes, so.NON_SYNONYMOUS):
protein_pos, aa_change = csq.protein_pos, csq.aa_change
sift_score, sift_tfic, sift_tfic_class = csq.sift_score, csq.sift_tfic, TransFIC.class_name(csq.sift_tfic_class)
pph2_score, pph2_tfic, pph2_tfic_class = csq.pph2_score, csq.pph2_tfic, TransFIC.class_name(csq.pph2_tfic_class)
ma_score, ma_tfic, ma_tfic_class = csq.ma_score, csq.ma_tfic, TransFIC.class_name(csq.ma_tfic_class)
write_line(cf, project_id, var.chr, var.strand, start, allele, csq.transcript,
",".join(csq.ctypes), csq.gene, gene_sym.get(csq.gene),
uniprot, protein, protein_pos, aa_change,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class,
csq.impact, TransFIC.class_name(csq.impact))
log.info(" Genes ...")
with ArcFile(task, arc, project_id, "genes", "w") as gf:
write_line(gf, "PROJECT_ID", "GENE_ID", "SYMBOL", "FM_PVALUE", "FM_QVALUE", "FM_EXC_CAUSE",
"SAMPLE_FREQ", "SAMPLE_TOTAL", "SAMPLE_PROP",
"CLUST_ZSCORE", "CLUST_PVALUE", "CLUST_QVALUE", "CLUST_EXC_CAUSE", "CLUST_COORDS",
"INTOGEN_DRIVER", "XREFS")
for gene in projdb.genes(join_xrefs=True, join_rec=True):
if gene.rec.sample_freq is not None and gene.rec.sample_freq > 0:
intogen_driver = 1 if sigdb.exists_gene(gene.id) else 0
write_line(gf, project_id, gene.id, gene.symbol, gene.fm_pvalue, gene.fm_qvalue, gene.fm_exc_cause,
gene.rec.sample_freq, total_samples, gene.rec.sample_prop or 0,
gene.clust_zscore, gene.clust_pvalue, gene.clust_qvalue, gene.clust_exc_cause,
gene.clust_coords, intogen_driver, ",".join(gene.xrefs))
log.info(" Pathways ...")
with ArcFile(task, arc, project_id, "pathways", "w") as pf:
write_line(pf, "PROJECT_ID", "PATHWAY_ID", "GENE_COUNT", "FM_ZSCORE", "FM_PVALUE", "FM_QVALUE",
"SAMPLE_FREQ", "SAMPLE_TOTAL", "SAMPLE_PROP", "GENE_FREQ", "GENE_TOTAL", "GENE_PROP")
for pathway in projdb.pathways(join_rec=True):
if pathway.rec.sample_freq is not None and pathway.rec.sample_freq > 0:
write_line(pf, project_id, pathway.id, pathway.gene_count, pathway.fm_zscore, pathway.fm_pvalue, pathway.fm_qvalue,
pathway.rec.sample_freq or 0, total_samples, pathway.rec.sample_prop or 0,
pathway.rec.gene_freq or 0, pathway.gene_count, pathway.rec.gene_prop or 0)
skip_oncodrivefm = conf.get("skip_oncodrivefm", False, dtype=bool)
if not skip_oncodrivefm:
log.info(" Genes per sample functional impact ...")
with ArcFile(task, arc, project_id, "fimpact.gitools.tdm", "w") as f:
write_line(f, "SAMPLE", "GENE_ID",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS")
for fields in projdb.sample_gene_fimpacts():
(gene, sample,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class) = fields
write_line(f, sample, gene,
sift_score, sift_tfic, TransFIC.class_name(sift_tfic_class),
pph2_score, pph2_tfic, TransFIC.class_name(pph2_tfic_class),
ma_score, ma_tfic, TransFIC.class_name(ma_tfic_class))
log.info("Saving project configuration ...")
with ArcFile(task, arc, project_id, "project", "w") as f:
names = ["PROJECT_ID", "ASSEMBLY", "SAMPLES_TOTAL"]
values = [project_id, project["assembly"], total_samples]
names, values = projres.get_annotations_to_save(conf, project["annotations"], names=names, values=values)
tsv.write_line(f, *names)
tsv.write_line(f, *values, null_value="-")
finally:
if arc is not None:
arc.close()
projdb.close()
sigdb.close()
def prepare_files(project):
log = task.logger
conf = task.conf
projects_out_port = task.ports("projects_out")
project_id = project["id"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
project_results = ProjectResults(project)
mutations_threshold, genes_filter_enabled, genes_filter, filt = get_oncodriveclust_configuration(log, conf, project)
log.info("Loading transcripts CDS length ...")
cds_len = load_cds_len(conf)
log.info("Retrieving gene alterations ...")
projdb = ProjectDb(project["db"])
data = retrieve_data(projdb, cds_len)
projdb.close()
data_paths = [
os.path.join(project["temp_path"], "oncodriveclust-non-syn-data.tsv"),
os.path.join(project["temp_path"], "oncodriveclust-syn-data.tsv")]
log.info("Saving data ...")
log.debug("> {0}".format(data_paths[NON_SYN]))
log.debug("> {0}".format(data_paths[SYN]))
df = [tsv.open(path, "w") for path in data_paths]
gene_sample_count = {}
for key, value in data.items():
findex, gene, sample = key
transcript, transcript_len, protein_pos = value
if findex == NON_SYN:
if gene not in gene_sample_count:
gene_sample_count[gene] = 1
else:
gene_sample_count[gene] += 1
if genes_filter_enabled and not filt.valid(gene):
continue
tsv.write_line(df[findex], gene, sample, protein_pos)
for f in df:
f.close()
exc_path = os.path.join(project["temp_path"], "oncodriveclust-excluded-cause.tsv")
log.info("Saving excluded gene causes ...")
log.debug("> {0}".format(exc_path))
with tsv.open(exc_path, "w") as exf:
tsv.write_line(exf, "GENE", "EXCLUDED_CAUSE")
for gene, sample_count in gene_sample_count.items():
causes = []
if genes_filter_enabled and not filt.valid(gene):
causes += [ProjectDb.GENE_EXC_FILTER]
if sample_count < mutations_threshold:
causes += [ProjectDb.GENE_EXC_THRESHOLD]
if len(causes) > 0:
tsv.write_line(exf, gene, "".join(causes))
log.info("Sending project ...")
projects_out_port.send(dict(project,
oncodriveclust=dict(
data_paths=data_paths,
mutations_threshold=mutations_threshold,
genes_filter_enabled=genes_filter_enabled, # not used
genes_filter=genes_filter))) # not used
def projects(project):
log = task.logger
conf = task.conf
projects_out_port = task.ports("projects_out")
log.info("--- [{0}] --------------------------------------------".format(project["id"]))
projdb = ProjectDb(project["db"])
total_samples = projdb.get_total_affected_samples()
if total_samples == 0:
log.warn("There are no samples, recurrences cannot be calculated.")
projdb.close()
return
log.info("Calculating project recurrences for variant genes ...")
projdb.compute_affected_genes_recurrences(total_samples)
if not conf.get("variants_only", False):
log.info("Calculating project recurrences for genes ...")
projdb.compute_gene_recurrences(total_samples)
log.info("Calculating project recurrences for pathways ...")
projdb.compute_pathway_recurrences(total_samples)
projdb.commit()
projdb.close()
projects_out_port.send(project)
def combination_oncodrivefm(projects_set):
log = task.logger
conf = task.conf
classifier, projects = projects_set
classifier_id = classifier["id"]
group_values = classifier["group_values"]
short_values = classifier["group_short_values"]
long_values = classifier["group_long_values"]
group_name = classifier["group_name"]
group_short_name = classifier["group_short_name"]
group_long_name = classifier["group_long_name"]
if len(group_values) == 0:
group_file_prefix = classifier_id
else:
group_file_prefix = "{0}-{1}".format(classifier_id, group_short_name)
group_file_prefix = normalize_id(group_file_prefix)
log.info("--- [{0} ({1}) ({2}) ({3})] {4}".format(classifier["name"], group_long_name, group_short_name, group_name, "-" * 30))
log.info("Exporting project data ...")
base_path = make_temp_dir(task, suffix=".{0}".format(group_file_prefix))
log.debug("> {0}".format(base_path))
project_ids = []
gene_files = []
pathway_files = []
for project in projects:
project_id = project["id"]
project_ids += [project_id]
log.info(" Project {0}:".format(project["id"]))
projdb = ProjectDb(project["db"])
log.info(" Genes ...")
count = 0
file_path = os.path.join(base_path, "{0}-genes.tsv".format(project_id))
gene_files += [file_path]
with open(file_path, "w") as f:
tsv.write_param(f, "classifier", classifier_id)
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "slice", project_id)
tsv.write_line(f, "GENE_ID", "PVALUE")
for gene in projdb.genes():
if gene.fm_pvalue is not None:
tsv.write_line(f, gene.id, gene.fm_pvalue, null_value="-")
count += 1
log.info(" {0} genes".format(count))
log.info(" Pathways ...")
count = 0
file_path = os.path.join(base_path, "{0}-pathways.tsv".format(project_id))
pathway_files += [file_path]
with open(file_path, "w") as f:
tsv.write_param(f, "classifier", classifier_id)
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "slice", project_id)
tsv.write_line(f, "PATHWAY_ID", "ZSCORE")
for pathway in projdb.pathways():
if pathway.fm_zscore is not None:
tsv.write_line(f, pathway.id, pathway.fm_zscore, null_value="-")
count += 1
log.info(" {0} pathways".format(count))
projdb.close()
log.info("Combining ...")
combination_path = get_combination_path(conf, "oncodrivefm")
log.info(" Genes ...")
cmd = " ".join([
"oncodrivefm-combine",
"-m median-empirical",
"-o '{0}'".format(combination_path),
"-n 'gene-{0}'".format(group_file_prefix),
"-D 'classifier={0}'".format(classifier_id),
"-D 'group_id={0}'".format(group_name),
"-D 'group_short_name={0}'".format(group_short_name),
"-D 'group_long_name={0}'".format(group_long_name),
"--output-format tsv.gz"
] + ["'{0}'".format(name) for name in gene_files])
log.debug(cmd)
ret_code = subprocess.call(cmd, shell=True)
if ret_code != 0:
#log.error("OncodriveFM error while combining gene pvalues:\n{0}".format(cmd))
#return -1
raise Exception("OncodriveFM error while combining gene pvalues:\n{0}".format(cmd))
log.info(" Pathways ...")
cmd = " ".join([
"oncodrivefm-combine",
"-m median-zscore",
"-o '{0}'".format(combination_path),
"-n 'pathway-{0}'".format(group_file_prefix),
"-D 'classifier={0}'".format(classifier_id),
"-D 'group_id={0}'".format(group_name),
"-D 'group_short_name={0}'".format(group_short_name),
"-D 'group_long_name={0}'".format(group_long_name),
"--output-format tsv.gz"
] + ["'{0}'".format(name) for name in pathway_files])
log.debug(cmd)
ret_code = subprocess.call(cmd, shell=True)
if ret_code != 0:
#log.error("OncodriveFM error while combining pathway zscores:\n{0}".format(cmd))
#return -1
raise Exception("OncodriveFM error while combining pathway zscores:\n{0}".format(cmd))
remove_temp(task, base_path)
def end():
log = task.logger
projects_out_port = task.ports("projects_out")
log.info("Updating the projects database ...")
for project_id, projects in task.context.items():
log.info("[{0}]".format(project_id))
for index, project in enumerate(projects):
projdb = ProjectDb(project["db"])
if index == 0:
log.info(" Functional impact ...")
projdb.delete_sample_gene_fimpact()
with tsv.open(project["sample_gene_fi_data"], "r") as f:
types = (int, str, float, float, int, float, float, int, float, float, int)
for fields in tsv.lines(f, types, header=True, null_value="-"):
projdb.add_sample_gene_fimpact(*fields)
ofm = project["oncodrivefm"]
del project["oncodrivefm"]
exc_path = os.path.join(project["temp_path"], "oncodrivefm-excluded-cause.tsv")
log.info(" Excluded gene causes ...")
log.debug(" > {0}".format(exc_path))
count = 0
with tsv.open(exc_path, "r") as exf:
for gene, cause in tsv.lines(exf, (str, str), header=True):
projdb.update_gene(Gene(id=gene, fm_exc_cause=cause))
count += 1
log.debug(" {0} genes excluded".format(count))
for feature, results_path in ofm:
log.info(" {0} ...".format(feature))
log.debug(" > {0}".format(results_path))
if feature == "genes":
with tsv.open(results_path, "r") as f:
count = 0
for gene, pvalue, qvalue in tsv.lines(f, (str, float, float), header=True):
projdb.update_gene(
Gene(id=gene, fm_pvalue=pvalue, fm_qvalue=qvalue, fm_exc_cause=ProjectDb.NO_GENE_EXC)
)
count += 1
log.info(" {0} genes".format(count))
elif feature == "pathways":
with tsv.open(results_path, "r") as f:
count = 0
for pathway, zscore, pvalue, qvalue in tsv.lines(f, (str, float, float, float), header=True):
projdb.update_pathway(
Pathway(id=pathway, fm_zscore=zscore, fm_pvalue=pvalue, fm_qvalue=qvalue)
)
count += 1
log.info(" {0} pathways".format(count))
projdb.commit()
projdb.close()
projects_out_port.send(projects[0])
def scan_files(project):
log = task.logger
conf = task.conf
projects_port, liftover_projects_port = task.ports("projects_out", "liftover_projects")
project_id = project["id"]
temp_path = project["temp_path"]
project_path = project["path"]
projdb_path = project["db"]
assembly = project["assembly"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
if assembly == "hg18":
out_port = liftover_projects_port
elif assembly == "hg19":
out_port = projects_port
else:
raise Exception("Unexpected assembly: {0}".format(assembly))
#if os.path.exists(projdb_path):
# log.warn("Variations database already created, skipping this step.")
# out_port.send(project)
# return
if os.path.exists(projdb_path):
os.remove(projdb_path)
log.info("Creating variants database ...")
projdb_tmp_path = make_temp_file(task, suffix=".db")
log.debug(projdb_tmp_path)
projdb = ProjectDb(projdb_tmp_path)
projdb.create()
data_path = conf["data_path"]
log.info("Loading genes ...")
projdb.load_genes(get_data_ensembl_genes_path(conf))
log.info("Loading pathways ...")
projdb.load_pathways(
get_data_kegg_def_path(conf),
get_data_kegg_ensg_map_path(conf))
log.info("Parsing variants ...")
for file in project["files"]:
if not os.path.isabs(file):
raise InternalError("Non absolute path found: {0}".format(file))
if not os.path.exists(file):
raise Exception("Input file not found: {0}".format(file))
if not os.path.isfile(file):
raise Exception("Not a file: {0}".format(file))
for container_name, path, name, ext, f in archived_files(file):
fname = os.path.join(path, name + ext)
if container_name is not None:
source_name = "{0}:{1}".format(os.path.basename(container_name), fname)
else:
source_name = name + ext
log.info("=> {0} ...".format(source_name))
sample_id = os.path.basename(name)
if ext.lower() in _SUPPORTED_EXTENSIONS:
parser_type = ext[1:]
else:
parser_type = "tab"
parser = create_variants_parser(parser_type, f, source_name, sample_id)
source_id = projdb.add_source(source_name)
var_ids = set()
for var in parser:
for line_num, text in parser.read_lines():
projdb.add_source_line(source_id, line_num, text)
var_id = projdb.add_variant(var, source_id=source_id, line_num=parser.get_line_num())
var_ids.add(var_id)
for line_num, text in parser.read_lines():
projdb.add_source_line(source_id, line_num, text)
num_variants = len(var_ids)
log.info(" {0} variants".format(num_variants))
if num_variants == 0:
raise Exception("No variants found in source '{}'. "
"Please check the documentation for the expected input for '{}' format.".format(
source_name, parser.name))
projdb.commit()
projdb.close()
log.info("Copying variants database ...")
log.debug("{0} -> {1}".format(projdb_tmp_path, projdb_path))
shutil.copy(projdb_tmp_path, projdb_path)
remove_temp(task, projdb_tmp_path)
out_port.send(project)
def combination_recurrences(projects_set):
log = task.logger
conf = task.conf
classifier, projects = projects_set
classifier_id = classifier["id"]
group_values = classifier["group_values"]
short_values = classifier["group_short_values"]
long_values = classifier["group_long_values"]
group_name = classifier["group_name"]
group_short_name = classifier["group_short_name"]
group_long_name = classifier["group_long_name"]
if len(group_values) == 0:
group_file_prefix = classifier_id
else:
group_file_prefix = "{0}-{1}".format(classifier_id, group_short_name)
group_file_prefix = normalize_id(group_file_prefix)
log.info("--- [{0} ({1}) ({2}) ({3})] {4}".format(classifier["name"], group_long_name, group_short_name, group_name, "-" * 30))
log.info("Creating database ...")
db_path = make_temp_file(task, suffix="-{0}.db".format(group_file_prefix))
log.debug(" > {0}".format(db_path))
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
create_db(conn)
log.info("Combining recurrences ...")
c = conn.cursor()
sample_total = 0
project_ids = []
for project in projects:
project_ids += [project["id"]]
log.info(" Project {0}:".format(project["id"]))
projdb = ProjectDb(project["db"])
project_sample_total = projdb.get_total_affected_samples()
sample_total += project_sample_total
log.info(" Total samples = {0}".format(project_sample_total))
log.info(" Variant genes ...")
count = 0
for afg in projdb.affected_genes(join_variant=True, join_xrefs=True, join_rec=True):
var = afg.var
rec = afg.rec
if rec.sample_freq is None:
log.warn("Discarding variant gene without sample frequency: {0}".format(repr(afg)))
continue
start, end, ref, alt = var_to_tab(var)
try:
c.execute("INSERT INTO variants (chr, strand, start, ref, alt, xrefs) VALUES (?,?,?,?,?,?)",
(var.chr, var.strand, start, ref, alt, ",".join(var.xrefs)))
var_id = c.lastrowid
except sqlite3.IntegrityError:
c.execute("SELECT var_id FROM variants WHERE chr=? AND strand=? AND start=? AND ref=? AND alt=?",
(var.chr, var.strand, start, ref, alt))
r = c.fetchone()
var_id = r[0]
try:
c.execute("INSERT INTO variant_genes (var_id, gene_id, impact, coding_region, prot_changes, sample_freq) VALUES (?,?,?,?,?,?)",
(var_id, afg.gene_id, afg.impact, afg.coding_region, afg.prot_changes, rec.sample_freq))
except sqlite3.IntegrityError:
c.execute("""
UPDATE variant_genes
SET sample_freq=sample_freq + ?
WHERE var_id=? AND gene_id=?""",
(rec.sample_freq, var_id, afg.gene_id))
count += 1
log.info(" {0} variant genes".format(count))
log.info(" Genes ...")
count = 0
for gene in projdb.genes(join_xrefs=True, join_rec=True):
rec = gene.rec
if rec.sample_freq is None:
continue
c.execute("SELECT COUNT(*) FROM genes WHERE gene_id=?", (gene.id,))
r = c.fetchone()
if r[0] == 0:
c.execute("INSERT INTO genes (gene_id, sample_freq) VALUES (?,?)",
(gene.id, rec.sample_freq))
else:
c.execute("UPDATE genes SET sample_freq=sample_freq + ? WHERE gene_id=?",
(rec.sample_freq, gene.id))
count += 1
log.info(" {0} genes".format(count))
log.info(" Pathways ...")
count = 0
for pathway in projdb.pathways(join_rec=True):
rec = pathway.rec
if rec.sample_freq is None:
continue
c.execute("SELECT COUNT(*) FROM pathways WHERE pathway_id=?", (pathway.id,))
r = c.fetchone()
if r[0] == 0:
c.execute("INSERT INTO pathways (pathway_id, sample_freq) VALUES (?,?)",
(pathway.id, rec.sample_freq))
else:
c.execute("UPDATE pathways SET sample_freq=sample_freq + ? WHERE pathway_id=?",
(rec.sample_freq, pathway.id))
count += 1
log.info(" {0} pathways".format(count))
projdb.close()
log.info("Calculating proportions with {0} samples in total among projects ...".format(sample_total))
if sample_total > 0:
c.execute("UPDATE variant_genes SET sample_prop=CAST(sample_freq AS REAL)/{0}.0".format(sample_total))
c.execute("UPDATE genes SET sample_prop=CAST(sample_freq AS REAL)/{0}.0".format(sample_total))
c.execute("UPDATE pathways SET sample_prop=CAST(sample_freq AS REAL)/{0}.0".format(sample_total))
c.close()
conn.commit()
log.info("Saving results ...")
c = conn.cursor()
base_path = get_combination_path(conf, "recurrences")
log.info(" Variant genes ...")
with tsv.open(os.path.join(base_path, "variant_gene-{0}.tsv.gz".format(group_file_prefix)), "w") as f:
tsv.write_param(f, "classifier", classifier["id"])
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "group_short_name", group_short_name)
tsv.write_param(f, "group_long_name", group_long_name)
tsv.write_param(f, "projects", ",".join(project_ids))
tsv.write_param(f, "SAMPLE_TOTAL", sample_total)
tsv.write_line(f, "CHR", "STRAND", "START", "ALLELE", "GENE_ID", "IMPACT", "IMPACT_CLASS", "SAMPLE_FREQ", "SAMPLE_PROP", "PROT_CHANGES", "XREFS")
for r in c.execute("SELECT * FROM variant_genes JOIN variants USING (var_id) ORDER BY chr*1, chr, strand, start, gene_id"):
strand, ref, alt = r["strand"], r["ref"], r["alt"]
allele = "{0}/{1}".format(ref, alt)
tsv.write_line(f, r["chr"], strand, r["start"], allele,
r["gene_id"], r["impact"], TransFIC.class_name(r["impact"]),
r["sample_freq"], r["sample_prop"], r["prot_changes"], r["xrefs"], null_value="-")
log.info(" Genes ...")
with tsv.open(os.path.join(base_path, "gene-{0}.tsv.gz".format(group_file_prefix)), "w") as f:
tsv.write_param(f, "classifier", classifier["id"])
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "group_short_name", group_short_name)
tsv.write_param(f, "group_long_name", group_long_name)
tsv.write_param(f, "projects", ",".join(project_ids))
tsv.write_param(f, "SAMPLE_TOTAL", sample_total)
tsv.write_line(f, "GENE_ID", "SAMPLE_FREQ", "SAMPLE_PROP")
for r in c.execute("SELECT * FROM genes ORDER BY gene_id"):
tsv.write_line(f, r["gene_id"], r["sample_freq"], r["sample_prop"], null_value="-")
log.info(" Pathways ...")
with tsv.open(os.path.join(base_path, "pathway-{0}.tsv.gz".format(group_file_prefix)), "w") as f:
tsv.write_param(f, "classifier", classifier["id"])
tsv.write_param(f, "group_id", group_name)
tsv.write_param(f, "group_short_name", group_short_name)
tsv.write_param(f, "group_long_name", group_long_name)
tsv.write_param(f, "projects", ",".join(project_ids))
tsv.write_param(f, "SAMPLE_TOTAL", sample_total)
tsv.write_line(f, "PATHWAY_ID", "SAMPLE_FREQ", "SAMPLE_PROP")
for r in c.execute("SELECT * FROM pathways ORDER BY pathway_id"):
tsv.write_line(f, r["pathway_id"], r["sample_freq"], r["sample_prop"], null_value="-")
conn.close()
remove_temp(task, db_path)
def prepare_files(project):
log = task.logger
conf = task.conf
projects_out_port = task.ports("projects_out")
project_id = project["id"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
project_results = ProjectResults(project)
projdb = ProjectDb(project["db"])
log.info("Retrieving functional impact scores for genes ...")
data = retrieve_data(projdb)
projdb.close()
# save data matrix
dst_path = os.path.join(project["temp_path"], "oncodrivefm-data.tdm")
sgfi_path = os.path.join(project["temp_path"], "sample_gene-fimpact.tsv.gz")
project["sample_gene_fi_data"] = sgfi_path
log.info("Saving functional impact scores ...")
log.debug("> {0}".format(dst_path))
with open(dst_path, "w") as f:
sgff = tsv.open(sgfi_path, "w")
tsv.write_line(f, "SAMPLE", "GENE", "SIFT", "PPH2", "MA")
tsv.write_line(sgff, "SAMPLE", "GENE",
"SIFT_SCORE", "SIFT_TFIC", "SIFT_TFIC_CLASS",
"PPH2_SCORE", "PPH2_TFIC", "PPH2_TFIC_CLASS",
"MA_SCORE", "MA_TFIC", "MA_TFIC_CLASS")
for key, values in data.iteritems():
sample, gene = key
(sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class) = values
tsv.write_line(f, sample, gene, sift_score, pph2_score, ma_score)
tsv.write_line(sgff, sample, gene,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class, null_value="-")
sgff.close()
# count samples
samples = set()
gene_sample_count = {}
for sample, gene in data.keys():
samples.add(sample)
if gene not in gene_sample_count:
gene_sample_count[gene] = 1
else:
gene_sample_count[gene] += 1
num_samples = len(samples)
if num_samples == 0:
log.warn("There are no samples data, skipping OncodriveFM for this project")
return
(num_cores, estimator,
genes_num_samplings, genes_threshold, genes_filter_enabled, genes_filter, filt,
pathways_num_samplings, pathways_threshold) = get_oncodrivefm_configuration(log, conf, project, num_samples)
# Create a dataset with information on why some genes are not considered for calculation in OncodriveFM
# There are basically two possible reasons:
# - It does not pass the filter
# - There are less samples mutated than the threshold
exc_path = os.path.join(project["temp_path"], "oncodrivefm-excluded-cause.tsv")
log.info("Saving excluded gene causes ...")
log.debug("> {0}".format(exc_path))
with tsv.open(exc_path, "w") as exf:
tsv.write_line(exf, "GENE", "EXCLUDED_CAUSE")
for gene, sample_count in gene_sample_count.items():
causes = []
if genes_filter_enabled and not filt.valid(gene):
causes += [ProjectDb.GENE_EXC_FILTER]
if sample_count < genes_threshold:
causes += [ProjectDb.GENE_EXC_THRESHOLD]
if len(causes) > 0:
tsv.write_line(exf, gene, "".join(causes))
ofm = dict(
data=dst_path,
num_cores=num_cores,
estimator=estimator)
for slice_name in ["SIFT", "PPH2", "MA"]:
projects_out_port.send(dict(project,
oncodrivefm=dict(ofm,
feature="genes",
slice=slice_name,
num_samplings=genes_num_samplings,
threshold=genes_threshold,
filter_enabled=genes_filter_enabled,
filter=genes_filter)))
for slice_name in ["SIFT", "PPH2", "MA"]:
projects_out_port.send(dict(project,
oncodrivefm=dict(ofm,
feature="pathways",
slice=slice_name,
num_samplings=pathways_num_samplings,
threshold=pathways_threshold,
filter_enabled=genes_filter_enabled,
filter=genes_filter)))
def datasets(project):
log = task.logger
conf = task.conf
project_id = project["id"]
log.info("--- [{0}] --------------------------------------------".format(project_id))
project_path = project["path"]
temp_path = project["temp_path"]
datasets_path = get_website_results_path(project_path)
if not os.path.exists(datasets_path):
os.makedirs(datasets_path)
sigdb = SigDb(conf["sigdb_path"])
sigdb.open()
projdb = ProjectDb(project["db"])
gene_sym = projdb.get_gene_symbols()
total_samples = projdb.get_total_affected_samples()
log.info("Exporting variant genes ...")
vf = open_dataset(project_id, project_path, datasets_path, "variant_gene", "w", log)
tsv.write_param(vf, "SAMPLE_TOTAL", total_samples)
tsv.write_line(vf, "VAR_ID", "CHR", "STRAND", "START", "ALLELE",
"GENE_ID", "IMPACT", "IMPACT_CLASS",
"SAMPLE_FREQ", "SAMPLE_PROP",
"CODING_REGION", "PROTEIN_CHANGES", "INTOGEN_DRIVER", "XREFS")
sf = open_dataset(project_id, project_path, datasets_path, "variant-samples", "w", log)
tsv.write_line(sf, "VAR_ID", "CHR", "STRAND", "START", "ALLELE", "SAMPLE")
count = 0
for afg in projdb.affected_genes(join_variant=True, join_samples=True, join_xrefs=True, join_rec=True):
var = afg.var
rec = afg.rec
start, end, ref, alt = var_to_tab(var)
allele = "{0}/{1}".format(ref, alt)
xrefs = [xref for xref in var.xrefs]
if sigdb.exists_variant(var.chr, start):
xrefs += ["I:1"]
xrefs = ",".join(xrefs)
intogen_driver = 1 if sigdb.exists_gene(afg.gene_id) else 0
tsv.write_line(vf, var.id, var.chr, var.strand, start, allele,
afg.gene_id, afg.impact, TransFIC.class_name(afg.impact),
rec.sample_freq, rec.sample_prop,
afg.coding_region, afg.prot_changes, intogen_driver, xrefs, null_value="\N")
for sample in var.samples:
tsv.write_line(sf, var.id, var.chr, var.strand, start, allele, sample.name, null_value="\N")
count += 1
vf.close()
sf.close()
log.info(" {0} variant genes".format(count))
log.info("Exporting consequences ...")
cf = open_dataset(project_id, project_path, datasets_path, "consequence", "w", log)
tsv.write_line(cf, "VAR_ID", "CHR", "STRAND", "START", "ALLELE", "TRANSCRIPT_ID",
"CT", "GENE_ID", "SYMBOL", "UNIPROT_ID", "PROTEIN_ID", "PROTEIN_POS", "AA_CHANGE",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS",
"IMPACT", "IMPACT_CLASS")
count = 0
for csq in projdb.consequences(join_variant=True):
var = csq.var
start, end, ref, alt = var_to_tab(var)
allele = "{0}/{1}".format(ref, alt)
uniprot = protein = protein_pos = aa_change = None
sift_score = sift_tfic = sift_tfic_class = None
pph2_score = pph2_tfic = pph2_tfic_class = None
ma_score = ma_tfic = ma_tfic_class = None
if so.match(csq.ctypes, so.ONCODRIVEFM):
uniprot, protein = csq.uniprot, csq.protein
if so.match(csq.ctypes, so.NON_SYNONYMOUS):
protein_pos, aa_change = csq.protein_pos, csq.aa_change
sift_score, sift_tfic, sift_tfic_class = csq.sift_score, csq.sift_tfic, TransFIC.class_name(csq.sift_tfic_class)
pph2_score, pph2_tfic, pph2_tfic_class = csq.pph2_score, csq.pph2_tfic, TransFIC.class_name(csq.pph2_tfic_class)
ma_score, ma_tfic, ma_tfic_class = csq.ma_score, csq.ma_tfic, TransFIC.class_name(csq.ma_tfic_class)
tsv.write_line(cf, var.id, var.chr, var.strand, start, allele, csq.transcript,
",".join(csq.ctypes), csq.gene, gene_sym.get(csq.gene),
uniprot, protein, protein_pos, aa_change,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class,
csq.impact, TransFIC.class_name(csq.impact), null_value="\N")
count += 1
cf.close()
log.info(" {0} consequences".format(count))
log.info("Exporting genes ...")
gf = open_dataset(project_id, project_path, datasets_path, "gene", "w", log)
tsv.write_param(gf, "SAMPLE_TOTAL", total_samples)
tsv.write_line(gf, "GENE_ID", "FM_PVALUE", "FM_QVALUE", "FM_EXC_CAUSE",
"CLUST_ZSCORE", "CLUST_PVALUE", "CLUST_QVALUE", "CLUST_EXC_CAUSE", "CLUST_COORDS",
"SAMPLE_FREQ", "SAMPLE_PROP", "INTOGEN_DRIVER")
for gene in projdb.genes(join_rec=True):
rec = gene.rec
if rec.sample_freq is None or rec.sample_freq == 0:
continue
intogen_driver = 1 if sigdb.exists_gene(gene.id) else 0
tsv.write_line(gf, gene.id, gene.fm_pvalue, gene.fm_qvalue, gene.fm_exc_cause,
gene.clust_zscore, gene.clust_pvalue, gene.clust_qvalue, gene.clust_exc_cause, gene.clust_coords,
rec.sample_freq or 0, rec.sample_prop or 0,
intogen_driver, null_value="\N")
gf.close()
log.info("Exporting pathways ...")
pf = open_dataset(project_id, project_path, datasets_path, "pathway", "w", log)
tsv.write_param(pf, "SAMPLE_TOTAL", total_samples)
tsv.write_line(pf, "PATHWAY_ID", "GENE_COUNT", "FM_ZSCORE", "FM_PVALUE", "FM_QVALUE",
"SAMPLE_FREQ", "SAMPLE_PROP", "GENE_FREQ", "GENE_TOTAL", "GENE_PROP")
for pathway in projdb.pathways(join_rec=True):
rec = pathway.rec
if rec.sample_freq is None or rec.sample_freq == 0:
continue
tsv.write_line(pf, pathway.id, pathway.gene_count, pathway.fm_zscore, pathway.fm_pvalue, pathway.fm_qvalue,
rec.sample_freq or 0, rec.sample_prop or 0, rec.gene_freq or 0, pathway.gene_count, rec.gene_prop or 0, null_value="\N")
pf.close()
skip_oncodrivefm = conf.get("skip_oncodrivefm", False, dtype=bool)
if not skip_oncodrivefm:
log.info("Exporting genes per sample functional impact ...")
with open_dataset(project_id, project_path, datasets_path, "gene_sample-fimpact", "w", log) as f:
tsv.write_line(f, "GENE_ID", "SAMPLE",
"SIFT_SCORE", "SIFT_TRANSFIC", "SIFT_TRANSFIC_CLASS",
"PPH2_SCORE", "PPH2_TRANSFIC", "PPH2_TRANSFIC_CLASS",
"MA_SCORE", "MA_TRANSFIC", "MA_TRANSFIC_CLASS")
for fields in projdb.sample_gene_fimpacts():
(gene, sample,
sift_score, sift_tfic, sift_tfic_class,
pph2_score, pph2_tfic, pph2_tfic_class,
ma_score, ma_tfic, ma_tfic_class) = fields
tsv.write_line(f, gene, sample,
sift_score, sift_tfic, TransFIC.class_name(sift_tfic_class),
pph2_score, pph2_tfic, TransFIC.class_name(pph2_tfic_class),
ma_score, ma_tfic, TransFIC.class_name(ma_tfic_class), null_value="\N")
projdb.close()
sigdb.close()
log.info("Saving project configuration ...")
projres = ProjectResults(project)
with open_dataset(project_id, project_path, datasets_path, "project.tsv", "w", log) as f:
names = ["ASSEMBLY", "SAMPLES_TOTAL"]
values = [project["assembly"], total_samples]
names, values = projres.get_annotations_to_save(conf, project["annotations"], names=names, values=values)
tsv.write_line(f, *names)
tsv.write_line(f, *values, null_value="\N")
projects_port = task.ports("projects_out")
projects_port.send(project)
def liftover(project):
log = task.logger
conf = task.conf
lifted_project_port = task.ports("lifted_projects")
log.info("--- [{0}] --------------------------------------------".format(project["id"]))
log.info("Preparing liftOver files ...")
in_path = make_temp_file(task, suffix=".bed")
in_file = open(in_path, "w")
out_path = make_temp_file(task, suffix=".bed")
unmapped_path = os.path.join(project["temp_path"], "liftover_unmapped.bed")
projdb = ProjectDb(project["db"])
for var in projdb.variants(order_by="position"):
in_file.write(tsv.line_text("chr" + var.chr, var.start, var.start + len(var.ref), var.id))
in_file.close()
log.info("Running liftOver ...")
project["from_assembly"] = project["assembly"]
project["assembly"] = "hg19"
cmd = " ".join([
conf["liftover_bin"],
in_path,
os.path.join(conf["liftover_chain_path"], "hg18ToHg19.over.chain"),
out_path,
unmapped_path
])
log.debug(cmd)
subprocess.call(cmd, shell=True)
log.info("Annotating unmapped variants ...")
count = 0
with open(unmapped_path, "r") as f:
for line in f:
if line.lstrip().startswith("#"):
continue
fields = line.rstrip().split("\t")
var_id = int(fields[3])
projdb.update_variant_start(var_id, start=None)
count += 1
log.info(" {0} unmapped variants annotated".format(count))
log.info("Updating variants ...")
count = 0
with open(out_path, "r") as f:
for line in f:
fields = line.rstrip().split("\t")
chr, start, end, var_id = fields
projdb.update_variant_start(var_id, start=start)
count += 1
log.info(" {0} variants".format(count))
remove_temp(task, in_path, out_path)
projdb.commit()
projdb.close()
lifted_project_port.send(project)
