def pack_datasets(project): log = task.logger config = GlobalConfig(task.conf) project_id = project["id"] log.info("--- [{0}] --------------------------------------------".format(project_id)) if not config.results.create_zip: log.info("Creation of the results compressed file is deactivated. Skipped.") return project_path = project["path"] temp_path = project["temp_path"] dest_path = os.path.join(project_path, "results.zip") sigdb = SigDb(config.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) if not config.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(config.project.annotations, 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 oncodriveclust(project): log = task.logger conf = task.conf log.info("--- [{0}] --------------------------------------------".format(project["id"])) config = GlobalConfig(conf) paths = PathsConfig(config) # avoid that project conf override path configurations config = GlobalConfig(conf, project["conf"]) oclust = OncodriveClust(config.oncodriveclust, paths, log) 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 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.ONCODRIVECLUST) 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 oclust.filter.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] >= oclust.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=oclust.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 combination_recurrences(projects_set): log = task.logger config = GlobalConfig(task.conf) paths = PathsConfig(config) 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 = paths.combination_path("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 create_datasets(project): log = task.logger config = GlobalConfig(task.conf) paths = PathsConfig(config) project_id = project["id"] log.info("--- [{0}] --------------------------------------------".format(project_id)) project_path = project["path"] temp_path = project["temp_path"] datasets_path = paths.project_results_path(project_path) ensure_path_exists(datasets_path) sigdb = SigDb(config.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() if not config.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(config.project.annotations, 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 combination_oncodrivefm(projects_set): log = task.logger config = GlobalConfig(task.conf) paths = PathsConfig(config) 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 = paths.combination_path("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 quality_control(log, config, project, ofm): ofm.load_expression_filter() filter = ofm.filter if ofm.filter_enabled else None 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) ofm.load_samples_thresholds(len(selected_samples)) samples_threshold = ofm.genes_samples_threshold 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 filter is None or filter.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 filter is None or filter.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