def run(self):
"""Single Sample GSEA workflow"""
assert self.min_size <= self.max_size
mkdirs(self.outdir)
logger = self._log_init(module=self.module,
log_level=logging.INFO if self.verbose else logging.WARNING)
dat = self._rank_metric(self.data)
assert len(dat) > 1
#cpu numbers
self._set_cores()
#Start Analysis
logger.info("Parsing data files for GSEA.............................")
#select correct expression genes and values.
dat2 = dat.set_index('gene_name')
del dat2['rank2']
#filtering out gene sets and build gene sets dictionary
gmt = gsea_gmt_parser(self.gene_sets, min_size=self.min_size, max_size=self.max_size, gene_list=dat2.index.values)
logger.info("%04d gene_sets used for further statistical testing....."% len(gmt))
logger.info("Start to run GSEA...Might take a while..................")
#compute ES, NES, pval, FDR, RES
gsea_results, hit_ind, rank_ES, subsets = gsea_compute_ss(data=dat2, n=self.permutation_num, gmt=gmt,
weighted_score_type=self.weighted_score_type,
seed=self.seed, processes=self._processes)
logger.info("Start to generate gseapy reports, and produce figures...")
res_zip = zip(subsets, list(gsea_results), hit_ind, rank_ES)
self._save_results(zipdata=res_zip, outdir=self.outdir, module=self.module,
gmt=gmt, rank_metric=dat, permutation_type="gene_sets")
#Plotting
self._plotting(rank_metric=dat, results=self.results, res2d=self.res2d,
graph_num=self.graph_num, outdir=self.outdir,
figsize=self.figsize, format=self.format, module=self.module)
logger.info("Congratulations. GSEApy run successfully................")
return
def runSample(self, df, gmt=None):
"""Single Sample GSEA workflow"""
assert self.min_size <= self.max_size
mkdirs(self.outdir)
#dat = self._rank_metric(df)
#assert len(dat) > 1
#Start Analysis
self._logger.info(
"Parsing data files for GSEA.............................")
#select correct expression genes and values.
if isinstance(df, pd.DataFrame):
if df.shape[1] == 1:
df = df.reset_index()
elif isinstance(df, pd.Series):
df = df.reset_index()
#sort ranking values from high to low or reverse
df.sort_values(by=df.columns[1],
ascending=self.ascending,
inplace=True)
df.columns = ['gene_name', 'rank']
df['rank2'] = df['rank']
else:
raise Exception('Error parsing gene ranking values!')
# revmove rank2
dat2 = df.set_index('gene_name')
del dat2['rank2']
#cpu numbers
self._set_cores()
#filtering out gene sets and build gene sets dictionary
if gmt is None:
gmt = gsea_gmt_parser(self.gene_sets,
min_size=self.min_size,
max_size=self.max_size,
gene_list=dat2.index.values)
self._logger.info(
"%04d gene_sets used for further statistical testing....." %
len(gmt))
self._logger.info(
"Start to run GSEA...Might take a while..................")
#compute ES, NES, pval, FDR, RES
gsea_results, hit_ind, rank_ES, subsets = gsea_compute_ss(
data=dat2,
n=self.permutation_num,
gmt=gmt,
weighted_score_type=self.weighted_score_type,
seed=self.seed,
processes=self._processes)
self._logger.info(
"Start to generate gseapy reports, and produce figures...")
res_zip = zip(subsets, list(gsea_results), hit_ind, rank_ES)
self._save_results(zipdata=res_zip,
outdir=self.outdir,
module=self.module,
gmt=gmt,
rank_metric=df,
permutation_type="gene_sets")
#Plotting
self._plotting(rank_metric=df,
results=self.results,
res2d=self.res2d,
graph_num=self.graph_num,
outdir=self.outdir,
figsize=self.figsize,
format=self.format,
module=self.module)
self._logger.info(
"Congratulations. GSEApy run successfully................")
return