def create_histogram_measurements(self,
exp_data_type,
save_name=None,
y_range=None,
out_dir=None):
"""
Plots a histogram of data
Parameters
----------
exp_data_type: str
Which data to plot
save_name: str
Name of figure
out_dir: str, path
Path to location to save figure
y_range: array_like
range of data
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()
data = data.dropna(subset=[p_val])
data = data[np.isfinite(data[fold_change])]
data = data.dropna(subset=[fold_change])
tmp = np.array(log2_normalize_df(data, fold_change)[fold_change])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(tmp, 50, color='gray')
if y_range is not None:
plt.xlim(y_range[0], y_range[1])
ax.set_yscale('log', basey=10)
ax.set_xlabel('log$_2$ Fold Change', fontsize=16)
ax.set_ylabel('Count', fontsize=16)
if save_name is not None:
v_plot.save_plot(fig, save_name, out_dir)
return fig
def create_histogram_measurements(self, save_name=None,
y_range=None, out_dir=None):
"""
Plots a histogram of data
Parameters
----------
save_name: str
Name of figure
out_dir: str, path
Path to location to save figure
y_range: array_like
range of data
Returns
-------
"""
data = self.copy()
data = data.dropna(subset=[p_val])
data = data[np.isfinite(data[fold_change])]
data = data.dropna(subset=[fold_change])
tmp = np.array(log2_normalize_df(data, fold_change)[fold_change])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(tmp, 50, color='gray')
if y_range is not None:
plt.xlim(y_range[0], y_range[1])
ax.set_yscale('log', basey=10)
ax.set_xlabel('log$_2$ Fold Change', fontsize=16)
ax.set_ylabel('Count', fontsize=16)
fig.tight_layout()
if save_name is not None:
v_plot.save_plot(fig, save_name, out_dir)
return fig
def plot_histogram(self, save_name=None, y_range=None, out_dir=None):
"""
Plots a histogram of data
Parameters
----------
save_name: str
Name of figure
out_dir: str, path
Path to location to save figure
y_range: array_like
range of data
Returns
-------
"""
data = self.copy()
data = data.dropna(subset=[p_val])
data = data[np.isfinite(data[fold_change])]
data = data.dropna(subset=[fold_change])
tmp = np.array(log2_normalize_df(data, fold_change)[fold_change])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(tmp, 50, color='gray')
if y_range is not None:
plt.xlim(y_range[0], y_range[1])
ax.set_yscale('log', basey=10)
ax.set_xlabel('log$_2$ Fold Change', fontsize=16)
ax.set_ylabel('Count', fontsize=16)
fig.tight_layout()
if save_name is not None:
v_plot.save_plot(fig, save_name, out_dir)
return fig
def volcano_by_sample(self,
save_name=None,
p_value=0.1,
out_dir=None,
fold_change_cutoff=1.5,
y_range=None,
x_range=None,
sig_column=False):
"""
Creates a figure of subplots of provided experimental method
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
-------
"""
data = self.copy()
n_sample = np.sort(data[sample_id].unique())
if len(n_sample) > 8:
n_cols = 3
else:
n_cols = 2
n_rows = np.rint(np.rint(len(n_sample) / float(n_cols)))
if n_cols * n_rows < len(n_sample):
if n_cols >= n_rows:
n_rows += 1
else:
n_cols += 1
fig = plt.figure(figsize=(4 * n_rows, 3 * n_cols))
for n, i in enumerate(n_sample):
sample = data[data[sample_id] == i].copy()
sample = sample.dropna(subset=[p_val])
sample = sample[np.isfinite(sample[fold_change])]
sample = sample.dropna(subset=[fold_change])
sec_0, sec_1, sec_2 = v_plot.create_mask(sample, sig_column,
p_value,
fold_change_cutoff)
ax = fig.add_subplot(n_rows, n_cols, n + 1)
ax.set_title(i)
v_plot.add_volcano_plot(ax, sec_0, sec_1, sec_2)
if not sig_column:
fc = np.log2(fold_change_cutoff)
log_p_val = -1 * np.log10(p_value)
ax.axvline(x=fc, linestyle='--')
ax.axvline(x=-1 * fc, linestyle='--')
ax.axhline(y=log_p_val, linestyle='--')
if y_range is not None:
ax.set_ylim(y_range[0], y_range[1])
if x_range is not None:
ax.set_xlim(x_range[0], x_range[1])
fig.tight_layout()
if save_name is not None:
v_plot.save_plot(fig, save_name=save_name, out_dir=out_dir)
return fig
def time_series_volcano(self, save_name=None, p_value=0.1,
out_dir=None, fold_change_cutoff=1.5,
y_range=None, x_range=None, sig_column=False):
"""
Creates a figure of subplots of provided experimental method
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
-------
"""
n_sample = np.sort(self[sample_id].unique())
if len(n_sample) > 8:
n_cols = 3
else:
n_cols = 2
n_rows = np.rint(np.rint(len(n_sample) / float(n_cols)))
if n_cols * n_rows < len(n_sample):
if n_cols >= n_rows:
n_rows += 1
else:
n_cols += 1
fig = plt.figure(figsize=(4 * n_rows, 3 * n_cols))
for n, i in enumerate(n_sample):
sample = self.loc[self[sample_id] == i].copy()
sample = sample.dropna(subset=[p_val])
sample = sample[np.isfinite(sample[fold_change])]
sample = sample.dropna(subset=[fold_change])
sec_0, sec_1, sec_2 = v_plot.create_mask(sample, sig_column,
p_value,
fold_change_cutoff)
ax = fig.add_subplot(n_rows, n_cols, n + 1)
ax.set_title(i)
v_plot.add_volcano_plot(ax, sec_0, sec_1, sec_2)
if not sig_column:
fc = np.log2(fold_change_cutoff)
log_p_val = -1 * np.log10(p_value)
ax.axvline(x=fc, linestyle='--')
ax.axvline(x=-1 * fc, linestyle='--')
ax.axhline(y=log_p_val, linestyle='--')
if y_range is not None:
ax.set_ylim(y_range[0], y_range[1])
if x_range is not None:
ax.set_xlim(x_range[0], x_range[1])
fig.tight_layout()
if save_name is not None:
v_plot.save_plot(fig, save_name=save_name, out_dir=out_dir)
return fig