def __init__(self, run, cancer, data_type, patients=None, drop_pc1=False,
create_real_features=True, create_meta_features=True,
filter_down=True, draw_figures=False):
'''
'''
Dataset.__init__(self, cancer.path, data_type, compressed=True)
self.df = IM.read_data(run.data_path, cancer.name, data_type,
tissue_code='All')
if patients is not None:
self.df = self.df.ix[:, patients].dropna(axis=1, how='all')
self.patients = patients
else:
self.patients = self.df.xs('01',1,1).columns
self.global_vars = pd.DataFrame(index=self.patients)
self.features = {}
self.global_loadings = pd.DataFrame(index=self.df.index)
self._calc_global_pcs(drop_pc1)
if create_real_features is True:
self._get_real_features()
if create_meta_features is True:
self._get_meta_features(run.gene_sets, filter_down)
self.features = pd.concat(self.features)
if draw_figures is True:
self._creat_pathway_figures()
def __init__(self, run, cancer, data_type, patients=None, drop_pc1=False,
create_real_features=True, create_meta_features=True,
filter_down=True, draw_figures=False):
'''
'''
Dataset.__init__(self, cancer.path, data_type, compressed=True)
self.df = IM.read_data(run.data_path, cancer.name, data_type,
tissue_code='All')
if patients is not None:
self.df = self.df.ix[:, patients].dropna(axis=1, how='all')
self.patients = patients
else:
self.patients = self.df.xs('01', 1, 1).columns
self.global_vars = pd.DataFrame(index=self.patients)
self.features = {}
self.global_loadings = pd.DataFrame(index=self.df.index)
self._calc_global_pcs(drop_pc1)
if create_real_features is True:
self._get_real_features()
if create_meta_features is True:
self._get_meta_features(run.gene_sets, filter_down)
self.features = pd.concat(self.features)
if draw_figures is True:
self._creat_pathway_figures()
def __init__(self, run, cancer, cn_type, patients=None):
Dataset.__init__(self, cancer.path, cn_type, compressed=False)
min_pat = run.parameters['min_patients']
if cn_type == 'CN_broad':
self.df = get_gistic(run.data_path, cancer.name,
min_patients=min_pat)
if patients is not None:
self.df = self.df.ix[:, patients].dropna(1, how='all')
self.features = self.df
def __init__(self, run, cancer, cn_type, patients=None):
'''
'''
Dataset.__init__(self, cancer.path, cn_type, compressed=False)
min_pat = run.parameters['min_patients']
if cn_type == 'CN_broad':
self.df = FH.get_gistic(run.data_path, cancer.name,
min_patients=min_pat)
if patients is not None:
self.df = self.df.ix[:, patients].dropna(1, how='all')
self.features = self.df
def __init__(self, run, cancer, patients=None,
create_features=True, draw_figures=False):
"""
"""
Dataset.__init__(self, cancer.path, 'Mutation', compressed=False)
self.df = FH.get_mutation_matrix(run.data_path, cancer.name)
if patients is not None:
self.df = self.df.ix[:, patients].dropna(1, how='all')
if create_features is True:
min_pat = run.parameters['min_patients']
self._create_feature_matrix(run.gene_sets, min_pat)
if draw_figures is True:
self._create_pathway_figures(run.gene_sets)
def __init__(self,
run,
cancer,
patients=None,
create_features=True,
draw_figures=False):
"""
"""
Dataset.__init__(self, cancer.path, 'Mutation', compressed=False)
self.df = FH.get_mutation_matrix(run.data_path, cancer.name)
if patients is not None:
self.df = self.df.ix[:, patients].dropna(1, how='all')
if create_features is True:
min_pat = run.parameters['min_patients']
self._create_feature_matrix(run.gene_sets, min_pat)
if draw_figures is True:
self._create_pathway_figures(run.gene_sets)
