Sm.pl.stacked barplot
Short Description
sm.pl.stacked_barplot: The function allows users to generate a stacked bar plot of a categorical column.
The function can generate the plots using matplotlib and Plotly libraries. Plotly is browser based and so
it can be used for interactive data exploration.
Function
stacked_barplot(adata, x_axis='imageid', y_axis='phenotype', subset_xaxis=None, subset_yaxis=None, order_xaxis=None, order_yaxis=None, method='percent', plot_tool='matplotlib', matplotlib_cmap=None, matplotlib_bbox_to_anchor=(1, 1.02), matplotlib_legend_loc=2, return_data=False, **kwargs)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
adata |
AnnData Object |
required | |
x_axis |
string, required |
'imageid' |
|
y_axis |
string, required |
'phenotype' |
|
subset_xaxis |
list, optional |
None |
|
subset_yaxis |
list, optional |
None |
|
order_xaxis |
list, optional |
None |
|
order_yaxis |
list, optional |
None |
|
method |
string, optional |
'percent' |
|
plot_tool |
string, optional |
'matplotlib' |
|
matplotlib_cmap |
string, optional |
None |
|
matplotlib_bbox_to_anchor |
tuple, optional |
(1, 1.02) |
|
matplotlib_legend_loc |
int, optional |
2 |
|
return_data |
bool, optional |
False |
|
**kwargs |
Additional keyword arguments passed to: |
{} |
Stacked bar plot. If return_data is set to True also returns a dataframe of the data used for the plot.
Examples:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | |
Source code in scimap/plotting/_stacked_barplot.py
def stacked_barplot (adata, x_axis='imageid', y_axis='phenotype', subset_xaxis=None, subset_yaxis=None,
order_xaxis=None, order_yaxis=None,
method='percent', plot_tool='matplotlib', matplotlib_cmap=None,
matplotlib_bbox_to_anchor=(1,1.02), matplotlib_legend_loc=2,
return_data=False, **kwargs):
"""
Parameters:
adata : AnnData Object
x_axis : string, required
Column name of the data that need to be plotted in the x-axis.
y_axis : string, required
Column name of the data that need to be plotted in the y-axis.
subset_xaxis : list, optional
Subset x-axis before plotting. Pass in a list of categories. eg- subset_xaxis = ['ROI_1', 'ROI_5']
subset_yaxis : list, optional
Subset y-axis before plotting. Pass in a list of categories. eg- subset_yaxis = ['Celltype_A', 'Celltype_B']
order_xaxis : list, optional
Order the x-axis of the plot as needed. Pass in a list of categories. eg- order_xaxis = ['ROI_5', 'ROI_1']
The default is None and will be plotted based on alphabetic order. Please note that if you change the order, pass all categories, failure to do so
will generate NaN's.
order_yaxis : list, optional
Order the y-axis of the plot as needed. Pass in a list of categories. eg- order_yaxis = ['Celltype_B', 'Celltype_A']
The default is None and will be plotted based on alphabetic order. Please note that if you change the order, pass all categories, failure to do so
will generate NaN's.
method : string, optional
Available options: 'percent' and 'absolute'.
1) Use Percent to plot the percent proportion.
2) Use 'absolute' to plot the plot the absolute number.
plot_tool : string, optional
Available options: 'matplotlib' and 'plotly'.
1) matplotlib uses the standard python plotting method
2) plotly opens the plot in a local browser. Advantage is to be able
to hover over the plot and retreive data for plots with large number of categories.
matplotlib_cmap : string, optional
Colormap to select colors from. If string, load colormap with that name from matplotlib.
matplotlib_bbox_to_anchor : tuple, optional
Bounding box argument used along with matplotlib_legend_loc to control
the legend location when using the matplotlib method.
matplotlib_legend_loc : int, optional
Location of legend used along with matplotlib_bbox_to_anchor to control
the legend location when using the matplotlib method.
return_data : bool, optional
When True, return the data used for plotting.
**kwargs : Additional keyword arguments passed to:
1) Pandas DataFrame.plot() when using the `matplotlib` method (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot))
2) Plotly.bar() when using the `plotly` method (https://plotly.com/python-api-reference/generated/plotly.express.bar.html))
Returns:
Stacked bar plot. If return_data is set to `True` also returns a dataframe of the data used for the plot.
Example:
```python
# Plot the absolute number of phenotypes using the matplotlib
# tool across differnt ROI's
# ROI column is `epidermis_roi` and phenotypes are stored under `phenotype`
sm.pl.stacked_barplot (adata,x_axis='epidermis_roi',y_axis='phenotype',
method='absolute',plot_tool='matplotlib',
figsize=(10, 10))
# Plot the number of cells normalized to 100% using the plotly
# tool across differnt ROI's
sm.pl.stacked_barplot (adata,x_axis='epidermis_roi',y_axis='phenotype',
method='percent',plot_tool='plotly',
color_discrete_sequence=px.colors.qualitative.Alphabet)
# Same as above but visualize only a subset of ROI's and a subset of
# phenotypes
subset_xaxis = ['epidermis_1', 'epidermis_5', 'epidermis_6']
subset_yaxis = ['APCs', 'Keratinocytes', 'Macrophages']
sm.pl.stacked_barplot (adata,x_axis='epidermis_roi',y_axis='phenotype',
subset_xaxis=subset_xaxis,subset_yaxis=subset_yaxis,
method='percent',plot_tool='plotly')
# Visualize absolute number of phenotypes and return the data into a
# dataframe `absolute_number`
absolute_number = sm.pl.stacked_barplot (adata,x_axis='epidermis_roi',
y_axis='phenotype', method='absolute',
plot_tool='matplotlib', return_data=True)
```
"""
# create the dataframe with details
data = pd.DataFrame(adata.obs)[[x_axis,y_axis]].astype(str)
# subset the data if needed
#if subset_data is not None:data = data[data[list(subset_data.keys())[0]].isin(list(subset_data.values())[0])]
if subset_xaxis is not None:
if isinstance(subset_xaxis, str):
subset_xaxis = [subset_xaxis]
data = data[data[x_axis].isin(subset_xaxis)]
if subset_yaxis is not None:
if isinstance(subset_yaxis, str):
subset_yaxis = [subset_yaxis]
data = data[data[y_axis].isin(subset_yaxis)]
# Method: Absolute or Percentile
if method == 'percent':
total = data.groupby([x_axis,y_axis]).size().unstack().fillna(0).sum(axis=1)
rg = pd.DataFrame(data.groupby([x_axis,y_axis]).size().unstack().fillna(0).div(total, axis=0).stack())
elif method == 'absolute':
rg = pd.DataFrame(data.groupby([x_axis,y_axis]).size().unstack().fillna(0).stack())
else:
raise ValueError('method should be either percent or absolute')
# change column name
rg.columns = ['count']
# Add the index as columns in the data frame
rg.reset_index(inplace=True)
# re-order the x oy y axis if requested by user
if order_xaxis is not None:
rg[x_axis] = rg[x_axis].astype('category')
rg[x_axis] = rg[x_axis].cat.reorder_categories(order_xaxis)
rg = rg.sort_values(x_axis)
if order_yaxis is not None:
rg[y_axis] = rg[y_axis].astype('category')
rg[y_axis] = rg[y_axis].cat.reorder_categories(order_yaxis)
rg = rg.sort_values(y_axis)
if order_xaxis and order_yaxis is not None:
rg = rg.sort_values([x_axis, y_axis])
pivot_df = rg.pivot(index=x_axis, columns=y_axis, values='count')
# Plotting tool
if plot_tool == 'matplotlib':
if matplotlib_cmap is None:
if len(rg[y_axis].unique()) <= 9:
matplotlib_cmap = "Set1"
elif len(rg[y_axis].unique()) > 9 and len(rg[y_axis].unique()) <=20:
matplotlib_cmap = plt.cm.tab20 #tab20
else:
matplotlib_cmap = plt.cm.gist_ncar
# Plotting
# add width if not passed via parameters
try:
width
except NameError:
width=0.9
# actual plotting
p = pivot_df.plot.bar(stacked=True, cmap=matplotlib_cmap, width=width, **kwargs)
handles, labels = p.get_legend_handles_labels() # for reversing the order of the legend
p.legend(reversed(handles), reversed(labels), bbox_to_anchor=matplotlib_bbox_to_anchor, loc=matplotlib_legend_loc)
elif plot_tool == 'plotly':
fig = px.bar(rg, x=x_axis, y="count", color=y_axis, **kwargs)
fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)'},
xaxis = dict(tickmode='linear') #type = 'category'
)
fig.show()
else:
raise ValueError('plot_tool should be either matplotlib or plotly')
# Return data
if return_data is True:
return pivot_df