def volcano_plot(self, save_name=None, out_dir=None, sig_column=False,
p_value=0.1, fold_change_cutoff=1.5, x_range=None,
y_range=None):
""" Create a volcano plot of data
Parameters
----------
save_name: str
name to save figure
out_dir: str, directory
Location to save figure
sig_column: bool, optional
If to use significant flags of data
p_value: float, optional
Criteria for significant
fold_change_cutoff: float, optional
Criteria for significant
y_range: array_like
upper and lower bounds of plot in y direction
x_range: array_like
upper and lower bounds of plot in x direction
Returns
-------
matplotlib.Figure
"""
fig = v_plot.volcano_plot(self, save_name=save_name, out_dir=out_dir,
sig_column=sig_column, p_value=p_value,
fold_change_cutoff=fold_change_cutoff,
x_range=x_range, y_range=y_range)
return fig
def volcano_plot(self,
exp_data_type,
save_name,
out_dir=None,
sig_column=False,
p_value=0.1,
fold_change_cutoff=1.5,
x_range=None,
y_range=None):
""" Create a volcano plot of data
Creates a volcano plot of data type provided
Parameters
----------
exp_data_type: str
Type of experimental method for plot
save_name: str
name to save figure
out_dir: str, directory
Location to save figure
sig_column: bool, optional
If to use significant flags of data
p_value: float, optional
Criteria for significant
fold_change_cutoff: float, optional
Criteria for significant
y_range: array_like
upper and lower bounds of plot in y direction
x_range: array_like
upper and lower bounds of plot in x direction
Returns
-------
"""
if not self._check_experiment_type_existence(exp_type=exp_data_type):
return
data = self.data[self.data[exp_method] == exp_data_type].copy()
fig = v_plot.volcano_plot(data,
save_name=save_name,
out_dir=out_dir,
sig_column=sig_column,
p_value=p_value,
fold_change_cutoff=fold_change_cutoff,
x_range=x_range,
y_range=y_range)
return fig
def volcano_plot(self,
save_name=None,
out_dir=None,
sig_column=False,
p_value=0.1,
fold_change_cutoff=1.5,
x_range=None,
y_range=None):
""" Create a volcano plot of data
Parameters
----------
save_name: str
name to save figure
out_dir: str, directory
Location to save figure
sig_column: bool, optional
If to use significant flags of data
p_value: float, optional
Criteria for significant
fold_change_cutoff: float, optional
Criteria for significant
y_range: array_like
upper and lower bounds of plot in y direction
x_range: array_like
upper and lower bounds of plot in x direction
Returns
-------
matplotlib.Figure
"""
fig = v_plot.volcano_plot(self,
save_name=save_name,
out_dir=out_dir,
sig_column=sig_column,
p_value=p_value,
fold_change_cutoff=fold_change_cutoff,
x_range=x_range,
y_range=y_range)
return fig
# import matplotlib.pyplot as plt
# plt.plot(data['padj'], data['p_value_group_1_and_group_2'], '.')
# plt.show()
data['p_value_group_1_and_group_2'] = data['padj']
# data['p_value_group_1_and_group_2'] = data['PValue']
data[fold_change] = np.where(data[fold_change] > 2, np.exp2(data[fold_change]),
-1 * np.exp2(-1 * data[fold_change]))
data[flag] = False
location = (np.abs(data[fold_change]) > 1.5) & (data['padj'] < 0.1)
data.loc[location, flag] = True
data['species_type'] = 'protein'
data['gene'] = data['gene_id']
data['protein'] = data['gene']
data['time'] = 0
data.to_csv('HMCES_two_controls_data.csv', index=False)
# print(data.dtypes)
# exp_data = ExperimentalData('magine_RPE_data.csv', data_directory='.')
vp.volcano_plot(data, 'HMCES_controls',
image_type='pdf') #, x_range=[-3,3], y_range=[-1,12])
quit()
magine = Analyzer(exp_data,
network='1',
metric='enrichment',
output_directory='RPE_HTML')
magine.run_go(data_type='rnaseq', run_type='changed', metric='enrichment')
quit