def check_enrichment(study_fn,
pop_fn,
assoc_fn,
print_summary=False,
save_summary=True,
savepath=None,
obo_dag=None):
p = optparse.OptionParser(__doc__)
p.add_option('--alpha',
default=0.05,
type="float",
help="Test-wise alpha for multiple testing "
"[default: %default]")
p.add_option('--pval',
default=None,
type="float",
help="Family-wise alpha (whole experiment), only print out "
"Bonferroni p-value is less than this value. "
"[default: %default]")
p.add_option('--compare',
dest='compare',
default=False,
action='store_true',
help="the population file as a comparison group. if this "
"flag is specified, the population is used as the study "
"plus the `population/comparison`")
p.add_option('--ratio',
dest='ratio',
type='float',
default=None,
help="only show values where the difference between study "
"and population ratios is greater than this. useful for "
"excluding GO categories with small differences, but "
"containing large numbers of genes. should be a value "
"between 1 and 2. ")
p.add_option('--fdr',
dest='fdr',
default=False,
action='store_true',
help="Calculate the false discovery rate (alt. to the "
"Bonferroni but slower)")
p.add_option('--indent',
dest='indent',
default=False,
action='store_true',
help="indent GO terms")
(opts, args) = p.parse_args()
args = [study_fn, pop_fn, assoc_fn]
bad = check_bad_args(args)
if bad:
print(bad)
sys.exit(p.print_help())
min_ratio = opts.ratio
if min_ratio is not None:
assert 1 <= min_ratio <= 2
assert 0 < opts.alpha < 1, "Test-wise alpha must fall between (0, 1)"
study_fn, pop_fn, assoc_fn = args
study, pop = read_geneset(study_fn, pop_fn, compare=opts.compare)
assoc = read_associations(assoc_fn)
methods = ["bonferroni", "sidak", "holm"]
if opts.fdr:
methods.append("fdr")
if obo_dag is None:
obo_file = "go-basic.obo"
obo_dag = GODag(obo_file=obo_file)
g = GOEnrichmentStudy(pop,
assoc,
obo_dag,
alpha=opts.alpha,
methods=methods)
results = g.run_study(study)
if print_summary:
g.print_summary(results,
min_ratio=min_ratio,
indent=opts.indent,
pval=opts.pval)
if save_summary:
if savepath is None:
savepath = study_fn.replace(
study_fn.split("/")[-1],
"enrichment_" + study_fn.split("/")[-1])
g.wr_tsv(savepath, results)
def term_enrichment(pop_genes,
gene_sets,
obo_path,
assoc_path,
folder,
condition,
regenerate=False,
test_sig=True,
**kwargs):
kwargs.setdefault('alpha', 0.05)
kwargs.setdefault('methods', ["bonferroni", "sidak", "holm"])
# Setup goatools enrichment
if regenerate:
assoc = read_associations(assoc_path)
go_dag = GODag(obo_file=obo_path)
pop = set(pop_genes)
g = GOEnrichmentStudy(pop, assoc, go_dag, **kwargs)
# go_enrich = OrderedDict()
go_enrich = OrderedDict()
for gc, genes in gene_sets.items():
# Write the gene list to a file
out_path = '{}/go_enrich/{}_{}_list.txt'.format(folder, condition, gc)
write_gene_list(genes, out_path)
enrich_path = out_path.replace('list', 'enrich')
try:
if regenerate:
raise ValueError('Override to retrain')
enrich = pd.read_csv(enrich_path, sep='\t', index_col=0)
except (FileNotFoundError, ValueError) as e:
r = g.run_study(frozenset(genes))
g.wr_tsv(enrich_path, r)
enrich = pd.read_csv(enrich_path, sep='\t', index_col=0)
enrich = enrich[(enrich.p_bonferroni < kwargs['alpha'])]
go_enrich[gc] = enrich
# Compile the results
# enrich_df = pd.concat(enrich_df, keys=gene_sets.keys())
# Get the sets
# go = enrich_df.groupby(level=0).apply(lambda x: set(x.index.get_level_values(1)))
go_sizes, go_terms = all_subsets(go_enrich)
all_terms = pd.concat(go_terms.values())
all_depths = all_terms['depth']
all_median = np.median(all_depths)
if test_sig:
for gene_class, terms in go_terms.items():
d = terms['depth'].values
if len(d) < 3:
print(gene_class, ' Skipped')
continue
t_med = np.median(d)
if t_med > all_median:
alternative = 'less'
elif t_med < all_median:
alternative = 'greater'
else:
alternative = 'two.sided'
ks_p = d_ks_test(d, all_depths, alternative=alternative)
print(gene_class, t_med, all_median, ks_p, sep='\t')
return pd.concat(go_terms.values(), keys=go_terms.keys())
