def runSamplesPermu(self, df, gmt=None):
"""Single Sample GSEA workflow with permutation procedure"""
assert self.min_size <= self.max_size
mkdirs(self.outdir)
self.resultsOnSamples = OrderedDict()
outdir = self.outdir
# iter throught each sample
for name, ser in df.iteritems():
self.outdir = os.path.join(outdir, str(name))
self._logger.info("Run Sample: %s " % name)
mkdirs(self.outdir)
# sort ranking values from high to low or reverse
dat2 = ser.sort_values(ascending=self.ascending)
# reset integer index, or caused unwanted problems
# df.reset_index(drop=True, inplace=True)
# compute ES, NES, pval, FDR, RES
gsea_results, hit_ind, rank_ES, subsets = gsea_compute(
data=dat2,
n=self.permutation_num,
gmt=gmt,
weighted_score_type=self.weighted_score_type,
permutation_type='gene_set',
method=None,
pheno_pos='',
pheno_neg='',
classes=None,
ascending=self.ascending,
processes=self._processes,
seed=self.seed,
single=True,
scale=self.scale)
# write file
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=dat2,
permutation_type="gene_sets")
self.resultsOnSamples[name] = self.res2d.es
# plotting
if self._noplot: continue
self._logger.info("Plotting Sample: %s \n" % name)
self._plotting(rank_metric=dat2,
results=self.results,
res2d=self.res2d,
graph_num=self.graph_num,
outdir=self.outdir,
figsize=self.figsize,
format=self.format,
module=self.module)
# save es, nes to file
self._save(outdir)
return
def run(self):
"""GSEA prerank 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)
dat2 = self._rank_metric(self.rnk)
assert len(dat2) > 1
#cpu numbers
self._set_cores()
#Start Analysis
logger.info("Parsing data files for GSEA.............................")
#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['gene_name'].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(data=dat2, n=self.permutation_num, gmt=gmt,
weighted_score_type=self.weighted_score_type,
permutation_type='gene_set', method=None,
phenoPos=self.pheno_pos, phenoNeg=self.pheno_neg,
classes=None, ascending=self.ascending, seed=self.seed,
processes=self._processes, prerank=True)
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=dat2, permutation_type="gene_sets")
#Plotting
self._plotting(rank_metric=dat2, 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 run(self):
"""GSEA prerank workflow"""
assert self.min_size <= self.max_size
# parsing rankings
dat2 = self._load_ranking(self.rnk)
assert len(dat2) > 1
# cpu numbers
self._set_cores()
# Start Analysis
self._logger.info("Parsing data files for GSEA.............................")
# filtering out gene sets and build gene sets dictionary
gmt = self.load_gmt(gene_list=dat2.index.values, gmt=self.gene_sets)
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(data=dat2, n=self.permutation_num, gmt=gmt,
weighted_score_type=self.weighted_score_type,
permutation_type='gene_set', method=None,
pheno_pos=self.pheno_pos, pheno_neg=self.pheno_neg,
classes=None, ascending=self.ascending,
processes=self._processes, seed=self.seed)
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=dat2, permutation_type="gene_sets")
# Plotting
if not self._noplot:
self._plotting(rank_metric=dat2, results=self.results, res2d=self.res2d,
graph_num=self.graph_num, outdir=self.outdir,
figsize=self.figsize, format=self.format,
module=self.module, phenoPos=self.pheno_pos, phenoNeg=self.pheno_neg)
self._logger.info("Congratulations. GSEApy run successfully................\n")
if self._outdir is None:
self._tmpdir.cleanup()
return
def run(self):
"""GSEA main procedure"""
assert self.permutation_type in ["phenotype", "gene_set"]
assert self.min_size <= self.max_size
if isinstance(self.data, pd.DataFrame):
df = self.data.copy()
elif os.path.isfile(self.data):
df = pd.read_table(self.data, comment='#')
else:
raise Exception('Error parsing gene expression dataframe!')
sys.exit(1)
#data frame must have lenght > 1
assert len(df) > 1
# creat output dirs
mkdirs(self.outdir)
logger = self._log_init(
module=self.module,
log_level=logging.INFO if self.verbose else logging.WARNING)
#Start Analysis
logger.info("Parsing data files for GSEA.............................")
# phenotype labels parsing
phenoPos, phenoNeg, cls_vector = gsea_cls_parser(self.classes)
#select correct expression genes and values.
dat = self.__drop_dat(df, cls_vector)
#ranking metrics calculation.
dat2 = ranking_metric(df=dat,
method=self.method,
phenoPos=phenoPos,
phenoNeg=phenoNeg,
classes=cls_vector,
ascending=self.ascending)
#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['gene_name'].values)
logger.info(
"%04d gene_sets used for further statistical testing....." %
len(gmt))
logger.info("Start to run GSEA...Might take a while..................")
#cpu numbers
self._set_cores()
#compute ES, NES, pval, FDR, RES
gsea_results, hit_ind, rank_ES, subsets = gsea_compute(
data=dat,
n=self.permutation_num,
gmt=gmt,
weighted_score_type=self.weighted_score_type,
permutation_type=self.permutation_type,
method=self.method,
phenoPos=phenoPos,
phenoNeg=phenoNeg,
classes=cls_vector,
ascending=self.ascending,
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=dat2,
permutation_type="gene_sets")
#Plotting
heat_dat = dat.loc[dat2.gene_name]
self._plotting(rank_metric=dat2,
results=self.results,
res2d=self.res2d,
graph_num=self.graph_num,
outdir=self.outdir,
figsize=self.figsize,
format=self.format,
module=self.module,
data=heat_dat,
classes=cls_vector,
phenoPos=phenoPos,
phenoNeg=phenoNeg)
logger.info("Congratulations. GSEApy run successfully................")
return
