#! python
# -*- coding: utf-8 -*-
import glob
import logging
import os
import xml.etree.ElementTree as ET
from collections import Counter
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from gseapy.base import GSEAbase
from gseapy.gse import CorrelType, Metric, gsea_rs, prerank2d_rs, prerank_rs, ssgsea_rs
from gseapy.parser import gsea_cls_parser
from gseapy.plot import gseaplot
from gseapy.utils import mkdirs
# from memory_profiler import profile
[docs]
class GSEA(GSEAbase):
"""GSEA main tool"""
def __init__(
self,
data: Union[pd.DataFrame, str],
gene_sets: Union[List[str], str, Dict[str, str]],
classes: Union[List[str], str, Dict[str, str]],
outdir: Optional[str] = None,
min_size: int = 15,
max_size: int = 500,
permutation_num: int = 1000,
weight: float = 1.0,
permutation_type: str = "phenotype",
method: str = "signal_to_noise",
ascending: bool = False,
threads: int = 1,
figsize: Tuple[float, float] = (6.5, 6),
format: str = "pdf",
graph_num: int = 20,
no_plot: bool = False,
seed: int = 123,
verbose: bool = False,
):
super(GSEA, self).__init__(
outdir=outdir,
gene_sets=gene_sets,
module="gsea",
threads=threads,
verbose=verbose,
)
self.data = data
self.classes = classes
self.permutation_type = permutation_type
self.method = method
self.min_size = min_size
self.max_size = max_size
self.permutation_num = int(permutation_num) if int(permutation_num) > 0 else 0
self.weight = weight
self.ascending = ascending
self.figsize = figsize
self.format = format
self.graph_num = int(graph_num)
self.seed = seed
self.ranking = None
self._noplot = no_plot
self.pheno_pos = "pos"
self.pheno_neg = "neg"
[docs]
def load_data(self, cls_vec: List[str]) -> Tuple[pd.DataFrame, Dict]:
"""pre-processed the data frame.new filtering methods will be implement here."""
# read data in
if isinstance(self.data, pd.DataFrame):
exprs = self.data.copy()
# handle index is gene_names
if exprs.index.dtype == "O":
exprs = exprs.reset_index()
elif os.path.isfile(self.data):
# GCT input format?
if self.data.endswith("gct"):
exprs = pd.read_csv(self.data, skiprows=2, sep="\t")
else:
exprs = pd.read_csv(self.data, comment="#", sep="\t")
else:
raise Exception("Error parsing gene expression DataFrame!")
# drop duplicated gene names
if exprs.iloc[:, 0].duplicated().sum() > 0:
self._logger.warning(
"Dropping duplicated gene names, only keep the first values"
)
# drop duplicate gene_names.
exprs.drop_duplicates(subset=exprs.columns[0], inplace=True)
if exprs.isnull().any().sum() > 0:
self._logger.warning("Input data contains NA, filled NA with 0")
exprs.dropna(how="all", inplace=True) # drop rows with all NAs
exprs = exprs.fillna(0)
# set gene name as index
exprs.set_index(keys=exprs.columns[0], inplace=True)
# select numberic columns
df = exprs.select_dtypes(include=[np.number])
# in case the description column is numeric
if len(cls_vec) == (df.shape[1] - 1):
df = df.iloc[:, 1:]
# drop gene which std == 0 in all samples
cls_dict = {k: v for k, v in zip(df.columns, cls_vec)}
# compatible to py3.7
major, minor, _ = [int(i) for i in pd.__version__.split(".")]
if (major == 1 and minor < 5) or (major < 1):
# fix numeric_only error
df_std = df.groupby(by=cls_dict, axis=1).std()
else:
df_std = df.groupby(by=cls_dict, axis=1).std(numeric_only=True)
df = df[df_std.sum(axis=1) > 0]
df = df + 1e-08 # we don't like zeros!!!
return df, cls_dict
[docs]
def calculate_metric(
self,
df: pd.DataFrame,
method: str,
pos: str,
neg: str,
classes: Dict[str, List[str]],
ascending: bool,
) -> Tuple[List[int], pd.Series]:
"""The main function to rank an expression table. works for 2d array.
:param df: gene_expression DataFrame.
:param method: The method used to calculate a correlation or ranking. Default: 'log2_ratio_of_classes'.
Others methods are:
1. 'signal_to_noise' (s2n) or 'abs_signal_to_noise' (abs_s2n)
You must have at least three samples for each phenotype.
The more distinct the gene expression is in each phenotype,
the more the gene acts as a “class marker”.
2. 't_test'
Uses the difference of means scaled by the standard deviation and number of samples.
Note: You must have at least three samples for each phenotype to use this metric.
The larger the t-test ratio, the more distinct the gene expression is in each phenotype
and the more the gene acts as a “class marker.”
3. 'ratio_of_classes' (also referred to as fold change).
Uses the ratio of class means to calculate fold change for natural scale data.
4. 'diff_of_classes'
Uses the difference of class means to calculate fold change for natural scale data
5. 'log2_ratio_of_classes'
Uses the log2 ratio of class means to calculate fold change for natural scale data.
This is the recommended statistic for calculating fold change for log scale data.
:param str pos: one of labels of phenotype's names.
:param str neg: one of labels of phenotype's names.
:param dict classes: column id to group mapping.
:param bool ascending: bool or list of bool. Sort ascending vs. descending.
:return: returns argsort values of a tuple where
0: argsort positions (indices)
1: pd.Series of correlation value. Gene_name is index, and value is rankings.
visit here for more docs: http://software.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html
"""
# exclude any zero stds.
# compatible to py3.7
major, minor, _ = [int(i) for i in pd.__version__.split(".")]
if (major == 1 and minor < 5) or (major < 1):
# fix numeric_only error
df_mean = df.groupby(by=classes, axis=1).mean()
df_std = df.groupby(by=classes, axis=1).std()
else:
df_mean = df.groupby(by=classes, axis=1).mean(numeric_only=True)
df_std = df.groupby(by=classes, axis=1).std(numeric_only=True)
class_values = Counter(classes.values())
n_pos = class_values[pos]
n_neg = class_values[neg]
if method in ["signal_to_noise", "s2n"]:
ser = (df_mean[pos] - df_mean[neg]) / (df_std[pos] + df_std[neg])
elif method in ["abs_signal_to_noise", "abs_s2n"]:
ser = ((df_mean[pos] - df_mean[neg]) / (df_std[pos] + df_std[neg])).abs()
elif method == "t_test":
ser = (df_mean[pos] - df_mean[neg]) / np.sqrt(
df_std[pos] ** 2 / n_pos + df_std[neg] ** 2 / n_neg
)
elif method == "ratio_of_classes":
ser = df_mean[pos] / df_mean[neg]
elif method == "diff_of_classes":
ser = df_mean[pos] - df_mean[neg]
elif method == "log2_ratio_of_classes":
ser = np.log2(df_mean[pos] / df_mean[neg])
else:
logging.error("Please provide correct method name!!!")
raise LookupError("Input method: %s is not supported" % method)
ser_ind = ser.values.argsort().tolist()
ser = ser.iloc[ser_ind]
if ascending:
return ser_ind, ser
# descending order
return ser_ind[::-1], ser[::-1]
[docs]
def load_classes(
self,
):
"""Parse group (classes)"""
if isinstance(self.classes, dict):
# check number of samples
class_values = Counter(self.classes.values())
s = []
for c, v in sorted(class_values.items(), key=lambda item: item[1]):
if v < 3:
raise Exception(f"Number of {c}: {v}, it must be >= 3!")
s.append(c)
self.pheno_pos = s[0]
self.pheno_neg = s[1]
# n_pos = class_values[pos]
# n_neg = class_values[neg]
return
else:
pos, neg, cls_vector = gsea_cls_parser(self.classes)
self.pheno_pos = pos
self.pheno_neg = neg
return cls_vector
# @profile
[docs]
def run(self):
"""GSEA main procedure"""
m = self.method.lower()
if m in ["signal_to_noise", "s2n"]:
method = Metric.Signal2Noise
elif m in ["s2n", "abs_signal_to_noise", "abs_s2n"]:
method = Metric.AbsSignal2Noise
elif m == "t_test":
method = Metric.Ttest
elif m == "ratio_of_classes":
method = Metric.RatioOfClasses
elif m == "diff_of_classes":
method = Metric.DiffOfClasses
elif m == "log2_ratio_of_classes":
method = Metric.Log2RatioOfClasses
else:
raise Exception("Sorry, input method %s is not supported" % m)
assert self.permutation_type in ["phenotype", "gene_set"]
assert self.min_size <= self.max_size
# Start Analysis
self._logger.info("Parsing data files for GSEA.............................")
# phenotype labels parsing
cls_vector = self.load_classes()
# select correct expression genes and values.
dat, cls_dict = self.load_data(cls_vector)
self.cls_dict = cls_dict
# data frame must have length > 1
assert len(dat) > 1
# filtering out gene sets and build gene sets dictionary
gmt = self.load_gmt(gene_list=dat.index.values, gmt=self.gene_sets)
self.gmt = gmt
self._logger.info(
"%04d gene_sets used for further statistical testing....." % len(gmt)
)
self._logger.info("Start to run GSEA...Might take a while..................")
# cpu numbers
# compute ES, NES, pval, FDR, RES
if self.permutation_type == "gene_set":
# ranking metrics calculation.
idx, dat2 = self.calculate_metric(
df=dat,
method=self.method,
pos=self.pheno_pos,
neg=self.pheno_neg,
classes=cls_dict,
ascending=self.ascending,
)
gsum = prerank_rs(
dat2.index.values.tolist(), # gene list
dat2.squeeze().values.tolist(), # ranking values
gmt, # must be a dict object
self.weight,
self.min_size,
self.max_size,
self.permutation_num,
self._threads,
self.seed,
)
## need to update indices, prerank_rs only stores input's order
# so compatible with code code below
indices = gsum.indices
indices[0] = idx
gsum.indices = indices # only accept [[]]
else: # phenotype permutation
group = list(
map(lambda x: True if x == self.pheno_pos else False, cls_vector)
)
gsum = gsea_rs(
dat.index.values.tolist(),
dat.values.tolist(), # each row is gene values across samples
gmt,
group,
method,
self.weight,
self.min_size,
self.max_size,
self.permutation_num,
self._threads,
self.seed,
)
if self._outdir is not None:
self._logger.info(
"Start to generate GSEApy reports and figures............"
)
self.ranking = pd.Series(gsum.rankings[0], index=dat.index[gsum.indices[0]])
# reorder datarame for heatmap
# self._heatmat(df=dat.loc[dat2.index], classes=cls_vector)
self._heatmat(df=dat.iloc[gsum.indices[0]], classes=cls_vector)
# write output and plotting
self.to_df(gsum.summaries, gmt, self.ranking)
self._logger.info("Congratulations. GSEApy ran successfully.................\n")
return
[docs]
class Prerank(GSEAbase):
"""GSEA prerank tool"""
def __init__(
self,
rnk: Union[pd.DataFrame, pd.Series, str],
gene_sets: Union[List[str], str, Dict[str, str]],
outdir: Optional[str] = None,
pheno_pos="Pos",
pheno_neg="Neg",
min_size: int = 15,
max_size: int = 500,
permutation_num: int = 1000,
weight: float = 1.0,
ascending: bool = False,
threads: int = 1,
figsize: Tuple[float, float] = (6.5, 6),
format: str = "pdf",
graph_num: int = 20,
no_plot: bool = False,
seed: int = 123,
verbose: bool = False,
):
super(Prerank, self).__init__(
outdir=outdir,
gene_sets=gene_sets,
module="prerank",
threads=threads,
verbose=verbose,
)
self.rnk = rnk
self.pheno_pos = pheno_pos
self.pheno_neg = pheno_neg
self.min_size = min_size
self.max_size = max_size
self.permutation_num = int(permutation_num) if int(permutation_num) > 0 else 0
self.weight = weight
self.ascending = ascending
self.figsize = figsize
self.format = format
self.graph_num = int(graph_num)
self.seed = seed
self.ranking = None
self._noplot = no_plot
self.permutation_type = "gene_set"
def load_ranking(self):
ranking = self._load_ranking(self.rnk) # index is gene_names
if isinstance(ranking, pd.DataFrame):
# handle index is not gene_names
if ranking.index.dtype != "O":
ranking.set_index(keys=ranking.columns[0], inplace=True)
# handle columns names are intergers
if ranking.columns.dtype != "O":
ranking.columns = ranking.columns.astype(str)
# drop na values
ranking = ranking.loc[ranking.index.dropna()]
if ranking.isnull().any().sum() > 0:
self._logger.warning("Input rankings contains NA values!")
# fill na
ranking.dropna(how="all", inplace=True)
ranking.fillna(0, inplace=True)
# drop duplicate IDs, keep the first
if ranking.index.duplicated().sum() > 0:
self._logger.warning("Duplicated ID detected!")
ranking = ranking.loc[~ranking.index.duplicated(keep="first"), :]
# check whether contains infinity values
if np.isinf(ranking).values.sum() > 0:
self._logger.warning("Input gene rankings contains inf values!")
col_min_max = {
np.inf: ranking[np.isfinite(ranking)].max(), # column-wise max
-np.inf: ranking[np.isfinite(ranking)].min(),
} # column-wise min
ranking = ranking.replace({col: col_min_max for col in ranking.columns})
# check ties in prerank stats
dups = ranking.apply(lambda df: df.duplicated().sum() / df.size)
if (dups > 0).sum() > 0:
msg = (
"Duplicated values found in preranked stats:\nsample\tratio\n%s\n"
% (dups.to_string(float_format="{:,.2%}".format))
)
msg += "The order of those genes will be arbitrary, which may produce unexpected results."
self._logger.warning(msg)
return ranking
# @profile
[docs]
def run(self):
"""GSEA prerank workflow"""
assert self.min_size <= self.max_size
# parsing rankings
dat2 = self.load_ranking()
assert len(dat2) > 1
self.ranking = dat2
# 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.gmt = gmt
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
if isinstance(dat2, pd.DataFrame):
_prerank = prerank2d_rs
else:
_prerank = prerank_rs
# run
gsum = _prerank(
dat2.index.values.tolist(), # gene list
dat2.values.tolist(), # ranking values
gmt, # must be a dict object
self.weight,
self.min_size,
self.max_size,
self.permutation_num,
self._threads,
self.seed,
)
self.to_df(
gsea_summary=gsum.summaries,
gmt=gmt,
rank_metric=dat2,
indices=gsum.indices if isinstance(dat2, pd.DataFrame) else None,
)
if self._outdir is not None:
self._logger.info(
"Start to generate gseapy reports, and produce figures..."
)
self._logger.info("Congratulations. GSEApy runs successfully................\n")
return
[docs]
class SingleSampleGSEA(GSEAbase):
"""GSEA extension: single sample GSEA"""
def __init__(
self,
data: Union[pd.DataFrame, pd.Series, str],
gene_sets: Union[List[str], str, Dict[str, str]],
outdir: Optional[str] = None,
sample_norm_method: str = "rank",
correl_norm_type: str = "zscore",
min_size: int = 15,
max_size: int = 500,
permutation_num: Optional[int] = None,
weight: float = 0.25,
ascending: bool = False,
threads: int = 1,
figsize: Tuple[float, float] = (6.5, 6),
format: str = "pdf",
graph_num: int = 20,
no_plot: bool = True,
seed: int = 123,
verbose: bool = False,
**kwargs,
):
super(SingleSampleGSEA, self).__init__(
outdir=outdir,
gene_sets=gene_sets,
module="ssgsea",
threads=threads,
verbose=verbose,
)
self.data = data
self.sample_norm_method = sample_norm_method
self.correl_type = self.norm_correl(correl_norm_type)
self.weight = weight
self.min_size = min_size
self.max_size = max_size
self.permutation_num = int(permutation_num) if permutation_num else None
self.ascending = ascending
self.figsize = figsize
self.format = format
self.graph_num = int(graph_num)
self.seed = seed
self.ranking = None
self._noplot = no_plot
self.permutation_type = "gene_set"
[docs]
def corplot(self):
"""NES Correlation plot
TODO
"""
[docs]
def setplot(self):
"""ranked genes' location plot
TODO
"""
def load_data(self) -> pd.DataFrame:
# load data
exprs = self.data
if isinstance(exprs, pd.DataFrame):
rank_metric = exprs.copy()
# handle dataframe with gene_name as index.
self._logger.debug("Input data is a DataFrame with gene names")
# handle index is not gene_names
if rank_metric.index.dtype != "O":
rank_metric.set_index(keys=rank_metric.columns[0], inplace=True)
if rank_metric.columns.dtype != "O":
rank_metric.columns = rank_metric.columns.astype(str)
rank_metric = rank_metric.select_dtypes(include=[np.number])
elif isinstance(exprs, pd.Series):
# change to DataFrame
self._logger.debug("Input data is a Series with gene names")
if exprs.name is None:
# rename col if name attr is none
exprs.name = "sample1"
elif exprs.name.dtype != "O":
exprs.name = exprs.name.astype(str)
rank_metric = exprs.to_frame()
elif os.path.isfile(exprs):
# GCT input format?
if exprs.endswith("gct"):
rank_metric = pd.read_csv(
exprs, skiprows=1, comment="#", index_col=0, sep="\t"
)
else:
# just txt file like input
rank_metric = pd.read_csv(exprs, comment="#", index_col=0, sep="\t")
if rank_metric.shape[1] == 1:
# rnk file like input
rank_metric.columns = rank_metric.columns.astype(str)
# select numbers
rank_metric = rank_metric.select_dtypes(include=[np.number])
else:
raise Exception("Error parsing gene ranking values!")
if rank_metric.index.duplicated().sum() > 0:
self._logger.warning(
"Dropping duplicated gene names, values averaged by gene names!"
)
rank_metric = rank_metric.loc[rank_metric.index.dropna()]
rank_metric = rank_metric.groupby(level=0).mean()
if rank_metric.isnull().any().sum() > 0:
self._logger.warning("Input data contains NA, filled NA with 0")
rank_metric = rank_metric.fillna(0)
return rank_metric
[docs]
def norm_samples(self, dat: pd.DataFrame) -> pd.DataFrame:
"""normalization samples
see here: https://github.com/broadinstitute/ssGSEA2.0/blob/f682082f62ae34185421545f25041bae2c78c89b/src/ssGSEA2.0.R#L237
"""
if self.sample_norm_method == "rank":
data = dat.rank(axis=0, method="average", na_option="bottom")
data = 10000 * data / data.shape[0]
elif self.sample_norm_method == "log_rank":
data = dat.rank(axis=0, method="average", na_option="bottom")
data = np.log(10000 * data / data.shape[0] + np.exp(1))
elif self.sample_norm_method == "log":
dat[dat < 1] = 1
data = np.log(dat + np.exp(1))
elif self.sample_norm_method == "custom":
self._logger.info("Use custom rank metric for ssGSEA")
data = dat
else:
raise Exception("No supported method: %s" % self.sample_norm_method)
return data
[docs]
def norm_correl(self, cortype):
"""
After norm_samples, input value will be further nomizalized before using them to calculate scores.
see source code:
https://github.com/broadinstitute/ssGSEA2.0/blob/f682082f62ae34185421545f25041bae2c78c89b/src/ssGSEA2.0.R#L396
"""
if cortype == "zscore":
return CorrelType.ZScore
elif cortype == "rank":
return CorrelType.Rank
elif cortype == "symrank":
return CorrelType.SymRank
else:
raise Exception("unsupported correl type input")
[docs]
def run(self):
"""run entry"""
if self.permutation_num is None:
self.permutation_num = 0
self._logger.info("Parsing data files for ssGSEA...........................")
# load data
data = self.load_data()
# normalized samples, and rank
normdat = self.norm_samples(data)
self.ranking = normdat
# filtering out gene sets and build gene sets dictionary
gmt = self.load_gmt(gene_list=normdat.index.values, gmt=self.gene_sets)
self.gmt = gmt
self._logger.info(
"%04d gene_sets used for further statistical testing....." % len(gmt)
)
# start analysis
self._logger.info("Start to run ssGSEA...Might take a while................")
if self.permutation_num > 0:
# run permutation procedure and calculate pvals, fdrs
self._logger.warning(
"Run ssGSEA with permutation procedure, don't use the pval, fdr results for publication."
)
self.runSamplesPermu(df=normdat, gmt=gmt)
[docs]
def runSamplesPermu(
self, df: pd.DataFrame, gmt: Optional[Dict[str, List[str]]] = None
):
"""Single Sample GSEA workflow with permutation procedure"""
assert self.min_size <= self.max_size
if self._outdir:
mkdirs(self.outdir)
gsum = ssgsea_rs(
df.index.values.tolist(),
df.values.tolist(),
gmt,
self.weight,
self.min_size,
self.max_size,
self.permutation_num, # permutate just like runing prerank analysis
self.correl_type,
self._threads,
self.seed,
)
self.to_df(gsum.summaries, gmt, df, gsum.indices)
return
[docs]
class Replot(GSEAbase):
"""To reproduce GSEA desktop output results."""
def __init__(
self,
indir: str,
outdir: str = "GSEApy_Replot",
weight: float = 1.0,
min_size: int = 3,
max_size: int = 1000,
figsize: Tuple[float, float] = (6.5, 6),
format: str = "pdf",
verbose: bool = False,
):
self.indir = indir
self.outdir = outdir
self.weight = weight
self.min_size = min_size
self.max_size = max_size
self.figsize = figsize
self.format = format
self.verbose = bool(verbose)
self.module = "replot"
self.gene_sets = None
self.ascending = False
# init logger
self.prepare_outdir()
[docs]
def gsea_edb_parser(self, results_path):
"""Parse results.edb file stored under **edb** file folder.
:param results_path: the path of results.edb file.
:return:
a dict contains { enrichment_term: [es, nes, pval, fdr, fwer, hit_ind]}
"""
xtree = ET.parse(results_path)
xroot = xtree.getroot()
res = {}
# dict_keys(['RANKED_LIST', 'GENESET', 'FWER', 'ES_PROFILE',
# 'HIT_INDICES', 'ES', 'NES', 'TEMPLATE', 'RND_ES', 'RANK_SCORE_AT_ES',
# 'NP', 'RANK_AT_ES', 'FDR'])
for node in xroot.findall("DTG"):
enrich_term = node.attrib.get("GENESET").split("#")[1]
es_profile = node.attrib.get("ES_PROFILE").split(" ")
# esnull = term.get('RND_ES').split(" ")
hit_ind = node.attrib.get("HIT_INDICES").split(" ")
es_profile = [float(i) for i in es_profile]
hit_ind = [int(i) for i in hit_ind]
# esnull = [float(i) for i in esnull ]
es = float(node.attrib.get("ES"))
nes = float(node.attrib.get("NES"))
pval = float(node.attrib.get("NP"))
fdr = float(node.attrib.get("FDR"))
fwer = float(node.attrib.get("FWER"))
logging.debug("Enriched Gene set is: " + enrich_term)
res[enrich_term] = [es, nes, pval, fdr, fwer, hit_ind]
return res
[docs]
def run(self):
"""main replot function"""
assert self.min_size <= self.max_size
# parsing files.......
try:
results_path = glob.glob(os.path.join(self.indir, "edb/results.edb"))[0]
rank_path = glob.glob(os.path.join(self.indir, "edb/*.rnk"))[0]
gene_set_path = glob.glob(os.path.join(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(os.path.join(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 = self.gsea_edb_parser(results_path)
for enrich_term, data in database.items():
# extract statistical resutls from results.edb file
es, nes, pval, fdr, fwer, hit_ind = data
gene_set = gene_set_dict.get(enrich_term)
# calculate enrichment score
RES = self.enrichment_score(
gene_list=gene_list,
correl_vector=correl_vector,
gene_set=gene_set,
weight=self.weight,
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,
hits=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"
)