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stacked_barplot

Short Description

sm.pl.stacked_barplot: This function creates stacked bar plots to visualize the distribution and proportions of categories within a specified categorical column across different groups or samples in an AnnData object. It supports both matplotlib for generating static plots and Plotly for interactive, browser-based visualizations. The flexibility to choose between plotting libraries caters to diverse analysis needs, from detailed publication-ready figures to dynamic exploration of complex datasets, enhancing the interpretability of spatial and phenotypic compositions.

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

The annotated data matrix.

 required
x_axis str

Column in adata.obs to be used as x-axis categories.

 'imageid'
y_axis str

Column in adata.obs representing categories to stack.

 'phenotype'
subset_xaxis list

Subsets categories in x_axis before plotting.

 None
subset_yaxis list

Subsets categories in y_axis before plotting.

 None
order_xaxis list

Specifies custom ordering for x-axis categories.

 None
order_yaxis list

Specifies custom ordering for y-axis categories.

 None
method str

Plotting method; 'percent' for percentage proportions, 'absolute' for actual counts.

 'percent'
plot_tool str

Choice of plotting library; 'matplotlib' for static plots, 'plotly' for interactive plots.

 'matplotlib'
matplotlib_cmap str

Matplotlib colormap for coloring the bars.

 None
matplotlib_bbox_to_anchor tuple

Adjusts the legend's bounding box location in matplotlib plots.

 (1, 1.02)
matplotlib_legend_loc int

Sets the legend location in matplotlib plots.

 2
return_data bool

If True, returns a DataFrame used for plotting instead of displaying the plot.

 False
**kwargs

Additional arguments passed to the plotting function (matplotlib or plotly).

 {}

Returns:

Name Type Description
Plot matplotlib

If return_data is True, returns a DataFrame containing the data used for plotting. Otherwise, displays the stacked bar plot.
Example 
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# Default stacked bar plot showing percentage composition
sm.pl.stacked_barplot(adata, x_axis='sample_id', y_axis='cell_type', method='percent')

# Stacked bar plot using absolute counts with matplotlib customization
sm.pl.stacked_barplot(adata, x_axis='region', y_axis='phenotype', method='absolute', plot_tool='matplotlib', 
                matplotlib_cmap='tab20', figsize=(12, 6), edgecolor='white')

# Interactive stacked bar plot using Plotly with subset and custom order
sm.pl.stacked_barplot(adata, x_axis='condition', y_axis='cell_state', subset_xaxis=['Control', 'Treated'], 
                order_yaxis=['State1', 'State2', 'State3'], method='percent', plot_tool='plotly', 
                color_discrete_map={'State1': '#1f77b4', 'State2': '#ff7f0e', 'State3': '#2ca02c'})

# Retrieve data used for plotting
data_df = sm.pl.stacked_barplot(adata, x_axis='batch', y_axis='cell_type', return_data=True)
Source code in scimap/plotting/stacked_barplot.py 
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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.AnnData):  
            The annotated data matrix.

        x_axis (str):  
            Column in `adata.obs` to be used as x-axis categories.

        y_axis (str):  
            Column in `adata.obs` representing categories to stack.

        subset_xaxis (list, optional):  
            Subsets categories in x_axis before plotting.

        subset_yaxis (list, optional):  
            Subsets categories in y_axis before plotting.

        order_xaxis (list, optional):  
            Specifies custom ordering for x-axis categories.

        order_yaxis (list, optional):  
            Specifies custom ordering for y-axis categories.

        method (str, optional):  
            Plotting method; 'percent' for percentage proportions, 'absolute' for actual counts.

        plot_tool (str, optional):  
            Choice of plotting library; 'matplotlib' for static plots, 'plotly' for interactive plots.

        matplotlib_cmap (str, optional):  
            Matplotlib colormap for coloring the bars.

        matplotlib_bbox_to_anchor (tuple, optional):  
            Adjusts the legend's bounding box location in matplotlib plots.

        matplotlib_legend_loc (int, optional):  
            Sets the legend location in matplotlib plots.

        return_data (bool, optional):  
            If True, returns a DataFrame used for plotting instead of displaying the plot.

        **kwargs:  
            Additional arguments passed to the plotting function (matplotlib or plotly).

Returns:
    Plot (matplotlib):
        If `return_data` is True, returns a DataFrame containing the data used for plotting. 
        Otherwise, displays the stacked bar plot.

Example:
    ```python

    # Default stacked bar plot showing percentage composition
    sm.pl.stacked_barplot(adata, x_axis='sample_id', y_axis='cell_type', method='percent')

    # Stacked bar plot using absolute counts with matplotlib customization
    sm.pl.stacked_barplot(adata, x_axis='region', y_axis='phenotype', method='absolute', plot_tool='matplotlib', 
                    matplotlib_cmap='tab20', figsize=(12, 6), edgecolor='white')

    # Interactive stacked bar plot using Plotly with subset and custom order
    sm.pl.stacked_barplot(adata, x_axis='condition', y_axis='cell_state', subset_xaxis=['Control', 'Treated'], 
                    order_yaxis=['State1', 'State2', 'State3'], method='percent', plot_tool='plotly', 
                    color_discrete_map={'State1': '#1f77b4', 'State2': '#ff7f0e', 'State3': '#2ca02c'})

    # Retrieve data used for plotting
    data_df = sm.pl.stacked_barplot(adata, x_axis='batch', y_axis='cell_type', 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