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 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 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 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)
