Sm.pl.pie
Short Description
sm.pl.pie: The function allows users to plot a pie plot for any categorical column of interest.
Function
pie(adata, phenotype='phenotype', group_by='imageid', ncols=None, subset_phenotype=None, subset_groupby=None, label='auto', title='auto', colors=None, autopct='%1.1f%%', legend=False, legend_loc='upper right', wedgeprops={'linewidth': 0}, return_data=False, **kwargs)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
adata |
AnnData object |
required | |
phenotype |
string, optional |
'phenotype' |
|
group_by |
string, optional |
'imageid' |
|
ncols |
int, optional |
None |
|
subset_phenotype |
list, optional |
None |
|
subset_groupby |
list, optional |
None |
|
label |
list, optional |
'auto' |
|
title |
string, optional |
'auto' |
|
colors |
list, optional |
None |
|
legend |
bool, optional |
False |
|
legend_loc |
string, optional |
'upper right' |
|
autopct |
None or str or callable, optional |
'%1.1f%%' |
|
wedgeprops |
dict, optional |
{'linewidth': 0} |
|
return_data |
bool, optional |
False |
|
**kwargs |
Keyword arguments to pass on to |
{} |
Returns data used for plotting if return_data = True
Examples:
1 2 3 4 5 6 7 | |
Source code in scimap/plotting/_pie.py
def pie (adata, phenotype='phenotype', group_by='imageid', ncols=None,
subset_phenotype=None, subset_groupby=None,
label='auto', title='auto', colors=None, autopct='%1.1f%%',
legend=False,legend_loc='upper right',
wedgeprops = {'linewidth': 0}, return_data=False, **kwargs):
"""
Parameters:
adata : AnnData object
phenotype : string, optional
Column contaning the cell-type inforamtion or any categorical data to be displayed
in the form of a pie plot.
group_by : string, optional
Column that contains inforamtion on data groupings which leads to generation of
pie plot for each group (e.g. image-id). If `None` is passed,
the entire data is considered as a single group.
ncols : int, optional
In case group_by is used, a grid of plots are returned. This paramenter
controls the number of columns in that grid.
subset_phenotype : list, optional
User can subset a list of categories within `phenotype` before plotting.
subset_groupby : list, optional
User can subset a list of categories within `group_by` before plotting.
label : list, optional
A list of strings providing the labels for each wedge.
title : string, optional
If `None`, the title of the pieplot is not plotted.
colors : list, optional
A sequence of colors through which the pie chart will cycle. If None, will use the
colors in the currently active cycle.
legend : bool, optional
If True, color legends are plotted seperately.
legend_loc: string, optional
Place a legend on the Axes.
autopct : None or str or callable, optional
If not None, is a string or function used to label the wedges with their numeric value.
The label will be placed inside the wedge. If it is a format string,
the label will be fmt % pct. If it is a function, it will be called.
wedgeprops : dict, optional
Dict of arguments passed to the wedge objects making the pie. For example, you can pass in
wedgeprops = {'linewidth': 3} to set the width of the wedge border lines equal to 3.
For more details, look at the doc/arguments of the wedge object. By default clip_on=False.
return_data : bool, optional
Returns the data used for plotting.
**kwargs :
Keyword arguments to pass on to `matplotlib.pyplot.pie`.
Returns:
Returns data used for plotting if `return_data = True`
Example:
```python
# pie plot showing stromal tumor content among the different samples
sm.pl.pie (adata, phenotype='Tumor_Stroma', group_by='imageid',
autopct='%1.1f%%',
textprops={'fontsize': 8, 'color': '#1d3557', 'fontweight': 'bold'},
ncols=5, label=None, title=None,
colors=['#a8dadc','#e63946'],
wedgeprops = {'linewidth': 0.8})
```
"""
# convert subset into list
if subset_phenotype is not None:
if isinstance (subset_phenotype, str):
subset_phenotype = [subset_phenotype]
if subset_groupby is not None:
if isinstance (subset_groupby, str):
subset_groupby = [subset_groupby]
# create copy of the required data
if group_by is not None:
data = adata.obs[[phenotype,group_by]]
else:
data = adata.obs[[phenotype]]
# subset data if needed
if subset_groupby is not None:
data = data[data[group_by].isin(subset_groupby)]
data[group_by] = data[group_by].astype('str').astype('category')
data[group_by] = data[group_by].cat.reorder_categories(subset_groupby)
data = data.sort_values(group_by)
if subset_phenotype is not None:
data = data[data[phenotype].isin(subset_phenotype)]
data[phenotype] = data[phenotype].astype('str').astype('category')
data[phenotype] = data[phenotype].cat.reorder_categories(subset_phenotype)
data = data.sort_values(phenotype)
if group_by and phenotype is not None:
data = data.sort_values([phenotype, group_by])
# calculate the proportion
if group_by is None:
prop = data[phenotype].value_counts().reset_index(inplace=False)
prop.columns = [phenotype, 'value']
prop['group_by'] = phenotype
labels = np.unique(prop[phenotype])
else:
# if group_by is provided
prop = pd.DataFrame(data.groupby([group_by,phenotype]).size()).reset_index(inplace=False)
prop.columns = ['group_by',phenotype,'value']
labels = np.unique(prop[phenotype])
#
if ncols is not None:
g = prop.groupby('group_by')
rows = int(np.ceil(len(g)/ncols))
else:
g = prop.groupby('group_by')
rows = 1
ncols = len(g)
# remove label if requested
if label == 'auto':
label = labels
elif label is None:
label = None
else:
label = label
# plot
if group_by is None:
fig, ax = plt.subplots()
ax.pie(prop.value, labels=label,colors=colors, wedgeprops = wedgeprops)
#ax.pie(prop.value, labels=label,colors=colors, wedgeprops = wedgeprops, **kwargs)
if title is None:
pass
else:
ax.set_title(phenotype)
else:
# plot the figure
# Ground work for removing unwanted axes
total_axes = list(range(ncols * rows))
required_axes = list(range(len(np.unique(prop['group_by']))))
final_axes = list(set(total_axes) ^ set(required_axes))
# Plot
fig, axes = plt.subplots(ncols=ncols, nrows=rows)
for (c, grp), ax in zip(g, axes.flat):
ax.pie(grp.value, labels=label, colors=colors, wedgeprops =wedgeprops)
#ax.pie(grp.value, labels=label, colors=colors, wedgeprops = wedgeprops, **kwargs)
if title is None:
pass
else:
ax.set_title(c)
# removing unwanted axis
for i in final_axes:
fig.delaxes(axes.flatten()[i])
if legend is True:
plt.legend(labels, loc=legend_loc, framealpha=1)
plt.show()
# return data
if return_data is True:
return prop