def run(self):
"""main replot function"""
assert self.min_size <= self.max_size
import glob
from bs4 import BeautifulSoup
#parsing files.......
try:
results_path = glob.glob(self.indir + '*/edb/results.edb')[0]
rank_path = glob.glob(self.indir + '*/edb/*.rnk')[0]
gene_set_path = glob.glob(self.indir + '*/edb/gene_sets.gmt')[0]
except IndexError as e:
logger.debug(e)
logger.error("Could not locate GSEA files in the given directory!")
sys.exit(1)
#extract sample names from .cls file
cls_path = glob.glob(self.indir + '*/edb/*.cls')
if cls_path:
phenoPos, phenoNeg, classes = gsea_cls_parser(cls_path[0])
else:
# logic for prerank results
phenoPos, phenoNeg = '', ''
#start reploting
self.gene_sets = gene_set_path
mkdirs(self.outdir)
logger = self._log_init(
module=self.module,
log_level=logging.INFO if self.verbose else logging.WARNING)
#obtain gene sets
gene_set_dict = gsea_gmt_parser(gene_set_path,
min_size=self.min_size,
max_size=self.max_size)
#obtain rank_metrics
rank_metric = self._rank_metric(rank_path)
correl_vector = rank_metric['rank'].values
gene_list = rank_metric['gene_name']
#extract each enriment term in the results.edb files and plot.
database = BeautifulSoup(open(results_path), features='xml')
length = len(database.findAll('DTG'))
for idx in range(length):
#extract statistical resutls from results.edb file
enrich_term, hit_ind, nes, pval, fdr = gsea_edb_parser(
results_path, index=idx)
gene_set = gene_set_dict.get(enrich_term)
#calculate enrichment score
RES = enrichment_score(
gene_list=gene_list,
gene_set=gene_set,
weighted_score_type=self.weighted_score_type,
correl_vector=correl_vector)[2]
#plotting
gsea_plot(rank_metric, enrich_term, hit_ind, nes, pval, fdr, RES,
phenoPos, phenoNeg, self.figsize, self.format,
self.outdir, self.module)
logger.info(
"Congratulations! Your plots have been reproduced successfully!")
def run(self):
"""main replot function"""
assert self.min_size <= self.max_size
assert self.fignum > 0
import glob
from bs4 import BeautifulSoup
# parsing files.......
try:
results_path = glob.glob(self.indir + '*/edb/results.edb')[0]
rank_path = glob.glob(self.indir + '*/edb/*.rnk')[0]
gene_set_path = glob.glob(self.indir + '*/edb/gene_sets.gmt')[0]
except IndexError as e:
sys.stderr.write(
"Could not locate GSEA files in the given directory!")
sys.exit(1)
# extract sample names from .cls file
cls_path = glob.glob(self.indir + '*/edb/*.cls')
if cls_path:
phenoPos, phenoNeg, classes = gsea_cls_parser(cls_path[0])
else:
# logic for prerank results
phenoPos, phenoNeg = '', ''
# start reploting
self.gene_sets = gene_set_path
# obtain gene sets
gene_set_dict = self.parse_gmt(gmt=gene_set_path)
# obtain rank_metrics
rank_metric = self._load_ranking(rank_path)
correl_vector = rank_metric.values
gene_list = rank_metric.index.values
# extract each enriment term in the results.edb files and plot.
database = BeautifulSoup(open(results_path), features='xml')
length = len(database.findAll('DTG'))
fig_num = self.fignum if self.fignum <= length else length
for idx in range(fig_num):
# extract statistical resutls from results.edb file
enrich_term, hit_ind, nes, pval, fdr = gsea_edb_parser(
results_path, index=idx)
gene_set = gene_set_dict.get(enrich_term)
# calculate enrichment score
RES = enrichment_score(
gene_list=gene_list,
correl_vector=correl_vector,
gene_set=gene_set,
weighted_score_type=self.weighted_score_type,
nperm=0)[-1]
# plotting
gsea_plot(rank_metric, enrich_term, hit_ind, nes, pval, fdr, RES,
phenoPos, phenoNeg, self.figsize, self.format,
self.outdir, self.module)
self._logger.info(
"Congratulations! Your plots have been reproduced successfully!\n")
def run(self):
"""main replot function"""
assert self.min_size <= self.max_size
# parsing files.......
try:
results_path = glob.glob(self.indir+'*/edb/results.edb')[0]
rank_path = glob.glob(self.indir+'*/edb/*.rnk')[0]
gene_set_path = glob.glob(self.indir+'*/edb/gene_sets.gmt')[0]
except IndexError as e:
raise Exception("Could not locate GSEA files in the given directory!")
# extract sample names from .cls file
cls_path = glob.glob(self.indir+'*/edb/*.cls')
if cls_path:
pos, neg, classes = gsea_cls_parser(cls_path[0])
else:
# logic for prerank results
pos, neg = '',''
# start reploting
self.gene_sets = gene_set_path
# obtain gene sets
gene_set_dict = self.parse_gmt(gmt=gene_set_path)
# obtain rank_metrics
rank_metric = self._load_ranking(rank_path)
correl_vector = rank_metric.values
gene_list = rank_metric.index.values
# extract each enriment term in the results.edb files and plot.
database = gsea_edb_parser(results_path)
for enrich_term, data in database.items():
# extract statistical resutls from results.edb file
hit_ind, nes, pval, fdr = data
gene_set = gene_set_dict.get(enrich_term)
if float(pval) > 0.1:
continue
# calculate enrichment score
RES = enrichment_score(gene_list=gene_list,
correl_vector=correl_vector,
gene_set=gene_set,
weighted_score_type=self.weighted_score_type,
nperm=0)[-1]
# plotting
term = enrich_term.replace('/','_').replace(":","_")
outfile = '{0}/{1}.{2}.{3}'.format(self.outdir, term, self.module, self.format)
gseaplot(rank_metric=rank_metric, term=enrich_term,
hit_indices=hit_ind, nes=nes, pval=pval, fdr=fdr,
RES=RES, pheno_pos=pos, pheno_neg=neg,
figsize=self.figsize, ofname=outfile)
self._logger.info("Congratulations! Your plots have been reproduced successfully!\n")
def gsea_signed_single(sig, values, midpoint=0, weighted_score_type=0):
"""
Compute signed GSEA on a single signature
"""
assert values.index.is_unique, "Index for values must be unique"
v2 = values.copy()
for gene in sig.values.index:
if sig.values[gene] < 0 and gene in v2.index:
v2[gene] = (v2[gene] - midpoint) * -1 + midpoint
# convert signature from signed into gseapy format
v2 = v2.sort_values(ascending=False)
gene_list = v2.index.tolist()
correl_vector = v2.values
sig_genes = list(sig.genes)
rs = np.random.RandomState(1028)
# gsea
es, esnull, ind, RES = enrichment_score(
gene_list,
gene_set=sig_genes,
correl_vector=correl_vector,
weighted_score_type=weighted_score_type,
nperm=1000,
rs=rs)
# Add in the leading-edge genes
LE_genes = []
if es > 0:
ii = np.argmax(RES)
for hi in ind:
if hi > ii:
break
gene = gene_list[hi]
if sig.values[gene] < 0:
gene = "(" + gene + ")"
LE_genes.append(gene)
else:
ii = np.argmin(RES)
for hi in ind[::-1]:
if hi < ii:
break
gene = gene_list[hi]
if sig.values[gene] < 0:
gene = "(" + gene + ")"
LE_genes.append(gene)
return es, ind, RES, esnull, LE_genes
