"background population. Please check.\n".format(overlap))
assoc = read_associations(assoc_fn)
methods = args.method.split(",")
obo_dag = GODag(obo_file=args.obo)
propagate_counts = not args.no_propagate_counts
g = GOEnrichmentStudy(pop, assoc, obo_dag,
propagate_counts=propagate_counts,
alpha=args.alpha,
pvalcalc=args.pvalcalc,
methods=methods)
results = g.run_study(study)
if args.outfile is None:
g.print_summary(results, min_ratio=min_ratio, indent=args.indent, pval=args.pval)
else:
# Users can print to both tab-separated file and xlsx file in one run.
outfiles = args.outfile.split(",")
prt_if = None # Print all values
if args.pval is not None:
# Only print out when uncorrected p-value < this value.
prt_if = lambda nt: nt.p_uncorrected < args.pval
for outfile in outfiles:
if outfile.endswith(".xlsx"):
g.wr_xlsx(outfile, results, prt_if=prt_if)
else:
g.wr_tsv(outfile, results, prt_if=prt_if)
# Copyright (C) 2010-2016, H Tang et al., All rights reserved.
assoc = read_associations(assoc_fn)
methods = args.method.split(",")
if args.fdr:
methods.append("fdr")
obo_dag = GODag(obo_file=args.obo)
propagate_counts = not args.no_propagate_counts
g = GOEnrichmentStudy(pop, assoc, obo_dag,
propagate_counts=propagate_counts,
alpha=args.alpha,
methods=methods)
results = g.run_study(study)
if args.outfile is None:
g.print_summary(results, min_ratio=min_ratio, indent=args.indent, pval=args.pval)
else:
# Users can print to both tab-separated file and xlsx file in one run.
outfiles = args.outfile.split(",")
prt_if = None # Print all values
if args.pval is not None:
# Only print out when uncorrected p-value < this value.
prt_if = lambda nt: nt.p_uncorrected < args.pval
for outfile in outfiles:
if outfile.endswith(".xlsx"):
g.wr_xlsx(outfile, results, prt_if=prt_if)
else:
g.wr_tsv(outfile, results, prt_if=prt_if)
# Copyright (C) 2010-2016, H Tang et al., All rights reserved.
assoc = read_associations(assoc_fn)
methods = args.method.split(",")
obo_dag = GODag(obo_file=args.obo)
propagate_counts = not args.no_propagate_counts
g = GOEnrichmentStudy(pop, assoc, obo_dag,
propagate_counts=propagate_counts,
alpha=args.alpha,
pvalcalc=args.pvalcalc,
methods=methods)
results = g.run_study(study)
if args.outfile is None:
g.print_summary(results, min_ratio=min_ratio, indent=args.indent, pval=args.pval)
else:
# Users can print to both tab-separated file and xlsx file in one run.
outfiles = args.outfile.split(",")
if args.pval is not None:
# Only print results when uncorrected p-value < this value.A
num_orig = len(results)
results = [r for r in results if r.p_uncorrected <= args.pval]
sys.stdout.write("{N:7,} of {M:,} results have uncorrected P-values <= {PVAL}=pval\n".format(
N=len(results), M=num_orig, PVAL=args.pval))
for outfile in outfiles:
if outfile.endswith(".xlsx"):
g.wr_xlsx(outfile, results, indent=args.indent)
else:
g.wr_tsv(outfile, results, indent=args.indent)
# Copyright (C) 2010-2018, H Tang et al. All rights reserved.
def goea(gene_ids, gene_symbols, trajectory, cluster, out_dir
): ## list of genes represented by their ensembl id and gene symbol
## load ontologies
if not os.path.exists(out_dir):
os.mkdir(out_dir)
from goatools.obo_parser import GODag
obodag = GODag("goea/go-basic.obo")
## load associations
from goatools.associations import read_ncbi_gene2go
geneid2gos_human = read_ncbi_gene2go("goea/gene2go", taxids=[9606])
## background gene set
from goea.genes_NCBI_9606_ProteinCoding import GENEID2NT as GeneID2nt_human
## GOEA object
from goatools.go_enrichment import GOEnrichmentStudy
goeaobj = GOEnrichmentStudy(
GeneID2nt_human.keys(), # List of mouse protein-coding genes
geneid2gos_human, # geneid/GO associations
obodag, # Ontologies
propagate_counts=False,
alpha=0.05, # default significance cut-off
methods=['fdr_bh']) # defult multipletest correction method
geneid2symbol = {}
for gene_symbol in gene_symbols:
for id in GeneID2nt_human.keys():
if GeneID2nt_human[id][5] == gene_symbol:
geneid2symbol[int(id)] = gene_symbol
#from PyEntrezId import Conversion
#for (gene_id, gene_symbol) in zip(gene_ids, gene_symbols):
# id = Conversion('*****@*****.**')
# gene_id = id.convert_ensembl_to_entrez(gene_id) ## get entrez
# geneid2symbol[int(gene_id)] = gene_symbol
## identify which id correspond to the genes in the cluster
## Run GOEA
# 'p_' means "pvalue". 'fdr_bh' is the multipletest method we are currently using.
"""
import rpy2
from rpy2.robjects import r, pandas2ri
from rpy2.robjects import pandas2ri
import rpy2.robjects as robjects
robjects.r('''
f <- function(geneNames) {
library(clusterProfiler)
kk <- enrichKEGG(geneNames)
as.data.frame(kk)
}
''')
r_enrich = robjects.globalenv['f']
"""
# print(r_enrich.r_repr())
gene_names = np.array(list(geneid2symbol.keys()))
print(gene_names)
"""
pandas2ri.activate()
res = r_enrich(gene_names)
res = r_enrich(gene_names, organism="hsa", pvalueCutoff=0.5, pAdjustMethod="BH", qvalueCutoff=0.1)
print(res)
print(pandas2ri.ri2py(res))
return
"""
geneids_study = geneid2symbol.keys()
with open(
out_dir + '/' + trajectory[-8:] + 'cluster ' + str(cluster) +
'genes.txt', 'w') as f:
for gene in geneids_study:
f.write("%s\n" % gene)
goea_results_all = goeaobj.run_study(geneids_study)
goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]
## Write the result to file
goeaobj.wr_xlsx(out_dir + '/' + trajectory[-8:] + 'cluster ' +
str(cluster) + 'goea_symbols.xlsx',
goea_results_sig,
itemid2name=geneid2symbol)
goeaobj.wr_xlsx(
out_dir + '/' + trajectory[-8:] + 'cluster ' + str(cluster) +
'goea_geneids.xlsx', goea_results_sig)
obo_dag = GODag(obo_file=args.obo)
propagate_counts = not args.no_propagate_counts
g = GOEnrichmentStudy(pop,
assoc,
obo_dag,
propagate_counts=propagate_counts,
alpha=args.alpha,
pvalcalc=args.pvalcalc,
methods=methods)
results = g.run_study(study)
if args.outfile is None:
g.print_summary(results,
min_ratio=min_ratio,
indent=args.indent,
pval=args.pval)
else:
# Users can print to both tab-separated file and xlsx file in one run.
outfiles = args.outfile.split(",")
prt_if = None # Print all values
if args.pval is not None:
# Only print out when uncorrected p-value < this value.
prt_if = lambda nt: nt.p_uncorrected < args.pval
for outfile in outfiles:
if outfile.endswith(".xlsx"):
g.wr_xlsx(outfile, results, prt_if=prt_if, indent=args.indent)
else:
g.wr_tsv(outfile, results, prt_if=prt_if, indent=args.indent)
# Copyright (C) 2010-2017, H Tang et al. All rights reserved.
"background population. Please check.\n".format(overlap))
assoc = read_associations(assoc_fn)
methods = args.method.split(",")
obo_dag = GODag(obo_file=args.obo)
propagate_counts = not args.no_propagate_counts
g = GOEnrichmentStudy(pop, assoc, obo_dag,
propagate_counts=propagate_counts,
alpha=args.alpha,
pvalcalc=args.pvalcalc,
methods=methods)
results = g.run_study(study)
if args.outfile is None:
g.print_summary(results, min_ratio=min_ratio, indent=args.indent, pval=args.pval)
else:
# Users can print to both tab-separated file and xlsx file in one run.
outfiles = args.outfile.split(",")
prt_if = None # Print all values
if args.pval is not None:
# Only print out when uncorrected p-value < this value.
prt_if = lambda nt: nt.p_uncorrected < args.pval
for outfile in outfiles:
if outfile.endswith(".xlsx"):
g.wr_xlsx(outfile, results, prt_if=prt_if, indent=args.indent)
else:
g.wr_tsv(outfile, results, prt_if=prt_if, indent=args.indent)
# Copyright (C) 2010-2016, H Tang et al. All rights reserved.
