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.
"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(",")
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 goe(
genelist,
go_file,
goa_file,
bg=None,
nmin=5,
conversion=None,
evidence_set={
'EXP', 'IDA', 'IPI', 'IMP', 'IGI', 'HTP', 'HDA', 'HMP', 'HGI', 'IBA',
'IBD', 'IKR', 'IRD', 'ISS', 'ISO', 'ISA', 'ISM'
}):
"""Finds GO enrichment with goatools (0.7.11 tested).
**WARNING**\ : This method is inexact for multi-maps in gene name conversion. However, it has a negligible effect in top GO component removal in single-cell co-expression.
Parameters
------------
genelist: list of str
Genes to search for enrichment.
go_file: str
File path for GO DAG (downloadable at http://geneontology.org/docs/download-ontology/)).
goa_file: str
File path for GO associations. See parameter **conversion**.
bg: list of str
Background genes.
nmin: int
Minimum number of principal genes required in GO.
conversion: tuple
Conversion of `gene ID system <https://docs.mygene.info/en/latest/doc/data.html>`_ from gene list to the GO annotation.
* name_from: Gene naming system of genelist. For gene names, use 'symbol,alias'.
* name_to: Gene naming system of goa_file. Examples:
* Human: use 'uniprot.Swiss-Prot' (for GO annotations downloded from http://geneontology.org/gene-associations/goa_human.gaf.gz).
* Mouse: use 'MGI' (for GO annotations downloded from http://current.geneontology.org/annotations/mgi.gaf.gz).
* species: Species for gene name conversion. Examples: 'human', 'mouse'.
evidence_set: set of str
`GO evidences <http://geneontology.org/docs/guide-go-evidence-codes/>`_ to include. Defaults to non-expression based results to avoid circular reasoning bias.
Returns
----------
goe: pandas.DataFrame
GO enrichment.
gotop: str
Top enriched GO ID
genes: list of str or None
Intersection list of genes in gotop and also bg. None if bg is None.
"""
from tempfile import NamedTemporaryFile
from os import linesep
from goatools.go_enrichment import GOEnrichmentStudy
from goatools.obo_parser import GODag
from goatools.associations import read_gaf
from collections import defaultdict
import itertools
from biothings_client import get_client
import pandas as pd
import logging
assert type(genelist) is list and len(genelist) > 0
if nmin < 1:
nmin = 1
bg0 = bg
# Convert gene names
if conversion is not None:
assert len(conversion) == 3
name_from, name_to, species = conversion
mg = get_client('gene')
ans = set(genelist)
if bg is not None:
t1 = set(bg)
assert len(ans - t1) == 0
ans |= t1
ans = list(ans)
ans = mg.querymany(ans,
scopes=name_from,
fields=name_to,
species=species)
t1 = set(['query', '_score', name_to.split('.')[0]])
ans = list(filter(lambda x: len(t1 - set(x)) == 0, ans))
ans = sorted(ans, key=lambda x: x['_score'])
convert = {x['query']: x for x in ans}
for xi in name_to.split('.'):
convert = filter(lambda x: xi in x[1], convert.items())
convert = {x[0]: x[1][xi] for x in convert}
convert = {
x[0]: x[1] if type(x[1]) is str else x[1][0]
for x in convert.items()
}
genelist2 = list(
set([convert[x]
for x in filter(lambda x: x in convert, genelist)]))
if bg is not None:
bg = list(
set([convert[x] for x in filter(lambda x: x in convert, bg)]))
t1 = set(genelist)
converti = list(filter(lambda x: x[0] in t1, convert.items()))
t1 = defaultdict(list)
for xi in converti:
t1[xi[1]].append(xi[0])
converti = dict(t1)
t1 = defaultdict(list)
for xi in convert.items():
t1[xi[1]].append(xi[0])
convertia = dict(t1)
else:
genelist2 = genelist
# Load GO DAG and association files
logging.debug('Reading GO DAG file ' + go_file)
godag = GODag(go_file)
logging.debug('Reading GO association file ' + goa_file)
goa = read_gaf(goa_file, evidence_set=evidence_set)
if bg is None:
bg = list(goa.keys())
# Compute enrichment
goe = GOEnrichmentStudy(bg, goa, godag)
ans = goe.run_study(genelist2)
# Format output
with NamedTemporaryFile() as f:
goe.wr_tsv(f.name, ans)
ans = f.read()
ans = ans.decode()
ans = [x.split('\t') for x in ans.split(linesep)]
if len(ans[-1]) < 2:
ans = ans[:-1]
if len(ans) == 0 or len(ans[0]) == 0:
raise ValueError('No enrichment found. Check your input ID type.')
ans[0][0] = ans[0][0].strip('# ')
ans = pd.DataFrame(ans[1:], columns=ans[0])
ans.drop(['NS', 'enrichment', 'study_count', 'p_sidak', 'p_holm'],
axis=1,
inplace=True)
for xj in ['p_uncorrected', 'p_bonferroni']:
ans[xj] = pd.to_numeric(ans[xj], errors='raise')
ans['depth'] = pd.to_numeric(ans['depth'],
errors='raise',
downcast='unsigned')
# Odds ratio column and sort column
ans['odds_ratio'] = toratio(ans['ratio_in_study']) / toratio(
ans['ratio_in_pop'])
ans = ans[[
'name', 'depth', 'p_uncorrected', 'p_bonferroni', 'odds_ratio',
'ratio_in_study', 'ratio_in_pop', 'GO', 'study_items'
]]
ans['study_items'] = ans['study_items'].apply(lambda x: x.replace(' ', ''))
# Convert back study_items
if conversion is not None:
ans['study_items'] = ans['study_items'].apply(lambda x: ','.join(
list(
itertools.chain.from_iterable(
[converti[y] for y in x.split(',')])))
if len(x) > 0 else x)
ans.sort_values('p_uncorrected', inplace=True)
# Get top enriched GO by P-value
gotop = ans[
(ans['odds_ratio'] > 1)
& ans['ratio_in_study'].apply(lambda x: int(x.split('/')[0]) >= nmin)]
if len(gotop) == 0:
raise ValueError('No GO enrichment found for given criteria.')
gotop = str(gotop.iloc[0]['GO'])
if bg0 is not None:
# Children GOs
gos = set([gotop] + list(godag.query_term(gotop).get_all_children()))
# Look for genes
genes = list(
filter(lambda x: len(list(filter(lambda y: y in gos, goa[x]))) > 0,
goa))
if conversion is not None:
genes = [
convertia[x] for x in filter(lambda x: x in convertia, genes)
]
genes = list(set(list(itertools.chain.from_iterable(genes))))
genes = set(genes)
genes = list(filter(lambda x: x in genes, bg0))
else:
genes = None
return (ans, gotop, genes)
def enrich(gene2go: str,
study: str,
obo: str,
population: str = None,
geneid2symbol: str = None,
correct='fdr_bh',
alpha=0.05,
top=20,
goea_out=None,
dag_out=None,
dpi=300,
show_gene_limit=6,
only_plot_sig=False):
"""
Go enrichment based on goatools
:param gene2go: a file with two columns: gene_id \t go_term_id
:param study: a file with at least one column, first column contains gene id, second columns is regulation direction
:param obo: go-basic file download from GeneOntology
:param population: a file with each row contains one gene; default to use all genes in gene2go file as population
:param geneid2symbol: file with two columns: gene_id \t gene_symbol, used for DAG plot
:param correct: pvalue adjustment method:
Method used for testing and adjustment of pvalues. Can be either the
full name or initial letters. Available methods are:
- `bonferroni` : one-step correction
- `sidak` : one-step correction
- `holm-sidak` : step down method using Sidak adjustments
- `holm` : step-down method using Bonferroni adjustments
- `simes-hochberg` : step-up method (independent)
- `hommel` : closed method based on Simes tests (non-negative)
- `fdr_bh` : Benjamini/Hochberg (non-negative)
- `fdr_by` : Benjamini/Yekutieli (negative)
- `fdr_tsbh` : two stage fdr correction (non-negative)
- `fdr_tsbky` : two stage fdr correction (non-negative)
:param alpha: fdr cutoff, default 0.05
:param top: n top go terms to plot, sorted by corrected pvalue
:param goea_out: output enrichment result file
:param dag_out: dag figure file
:param dpi: resolution of image, no effect for svg
:param show_gene_limit: the max number of gene in a node to show
:param only_plot_sig: only plot dag for significantly enriched terms
:return: None
"""
if str(correct) == '3':
correct = 'fdr_bh'
if geneid2symbol:
geneid2symbol = dict(x.strip().split()[:2] for x in open(geneid2symbol)
if x.strip())
else:
geneid2symbol = dict()
obo = GODag(obo, optional_attrs=['relationship', 'is_a'])
gene2go = read_associations(gene2go)
study_genes = [x.strip().split()[0] for x in open(study)]
try:
reg_dict = dict(x.strip().split()[:2] for x in open(study))
except:
reg_dict = {x.strip(): '' for x in open(study)}
if not population:
population = gene2go.keys()
else:
population = [
x.strip().split()[0] for x in open(population) if x.strip()
]
goea_obj = GOEnrichmentStudy(population,
gene2go,
obo,
propagate_counts=False,
alpha=alpha,
methods=('fdr_bh', ))
keep_if = lambda r: r.ratio_in_study[0] != 0
goea_results_all = goea_obj.run_study(study_genes, keep_if=keep_if)
goea_out = goea_out or study + '.goea.xls'
goea_obj.wr_tsv(goea_out, goea_results_all)
def func(y):
results = []
genes = [x.strip() for x in y.split(',')]
for gene in genes:
tmp = [gene]
if gene in reg_dict:
tmp.append(reg_dict[gene])
if gene in geneid2symbol:
tmp.append(geneid2symbol[gene])
results.append('|'.join(tmp))
return ';'.join(results)
# func = lambda y: ';'.join(x.strip()+'|'+reg_dict[x.strip()] if x.strip() in reg_dict else x.strip() for x in y.split(','))
table = pd.read_table(goea_out, header=0, index_col=0)
# 重新校正pvalue, 修改内容
fdr = multipletests(table['p_uncorrected'], method=correct)[1]
table['p_fdr_bh'] = fdr
# 修改goea_result_all方便后续的画图
for r, fdr in zip(goea_results_all, fdr):
r.p_fdr_bh = fdr
table.columns = [
x if x != 'p_fdr_bh' else 'p_corrected' for x in table.columns
]
table['enrichment'] = [
'e' if x <= alpha else 'p' for x in table['p_corrected']
]
table['study_items'] = table.loc[:, 'study_items'].map(func)
# table = table.sort_values(by=['p_corrected', 'p_uncorrected'])
table.to_csv(goea_out, header=True, index=True, sep='\t')
# -------------------plot dag------------------------
for each in ['BP', 'MF', 'CC']:
if only_plot_sig:
goea_results_sig = table[table['enrichment'] == 'e']
else:
goea_results_sig = table.copy()
goea_results_sig = goea_results_sig[goea_results_sig['NS'] == each]
if not goea_results_sig.shape[0]:
print(f"No significant term to plot for {each} ")
return
if goea_results_sig.shape[0] >= top:
goea_results_sig = goea_results_sig.iloc[:top]
goid_subset = list(goea_results_sig.index)
# t = obo[goid_subset[5]]
# for k, v in t.relationship.items():
# print(t, k, type(v), list(v)[0].id)
# print(dag_out[:-4]+'.'+each+dag_out[-4:])
dag_out = dag_out or study + '.goea.dag.svg'
plot_gos(
dag_out[:-4] + '.' + each + dag_out[-4:],
goid_subset, # Source GO ids, 如果分析结果里面没有包含这个节点，则他的颜色会是苍白绿色，但这里这个情况不会出现
obo,
goea_results=
goea_results_all, # use pvals for coloring:"p_{M}".format(M=goea[0].method_flds[0].fieldname)
# We can further configure the plot...
id2symbol=geneid2symbol, # Print study gene Symbols, not GeneIDs
study_items=show_gene_limit, # Only max 6 gene Symbols on GO terms
items_p_line=3, # Print 3 genes per line)
dpi=0 if dag_out.endswith('svg') else dpi,
# title="Directed Graph of enriched {} terms".format(each)
)
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.
