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foldchange

Short Description

sm.pl.foldchange: This function facilitates the visualization of fold changes in cell type abundance across samples or Regions of Interest (ROIs), offering insights into differential expression or abundance patterns. It is designed to work with data processed by sm.tl.foldchange, which should be executed beforehand to calculate the fold changes. Through heatmap or parallel coordinates presentations, users can effectively interpret variations, highlighting significant shifts and guiding further analyses.

Function

foldchange(adata, label='foldchange', p_val=0.05, nonsig_color='grey', subset_xaxis=None, subset_yaxis=None, cmap='vlag', log=True, center=0, method='heatmap', invert_axis=None, parallel_coordinates_color=None, matplotlib_bbox_to_anchor=(1.04, 1), matplotlib_legend_loc='upper left', xticks_rotation=90, return_data=False, fileName='foldchange.pdf', saveDir=None, **kwargs)

Parameters:

Name Type Description Default
adata AnnData

The annotated data matrix with fold change calculations.

 required
label str

Label key from adata.uns indicating the fold change data to visualize.

 'foldchange'
p_val float

P-value threshold for significance; values above this threshold are considered non-significant.

 0.05
nonsig_color str

Color for non-significant changes in the visualization.

 'grey'
subset_xaxis list

Categories to include from the x-axis for plotting.

 None
subset_yaxis list

Categories to include from the y-axis for plotting.

 None
cmap str

Colormap for the heatmap visualization.

 'vlag'
log bool

If True, fold changes are converted to log2 scale for visualization.

 True
center float

The value at which the colormap is centered.

 0
method str

Plotting method, either 'heatmap' for a heatmap or 'parallel_coordinates' for a parallel coordinates plot.

 'heatmap'
invert_axis bool

If True, inverts the x and y axes in the plot. Default is False.

 None
parallel_coordinates_color list

Specifies custom colors for parallel coordinates plot.

 None
matplotlib_bbox_to_anchor tuple

Adjusts the legend position in parallel coordinates plot.

 (1.04, 1)
matplotlib_legend_loc str

Specifies the location of the legend.

 'upper left'
xticks_rotation int

Rotation angle for x-axis tick labels.

 90
return_data bool

If True, returns the data frame used for plotting instead of the plot.

 False

Returns:

Type Description

Dataframe; plot (pandas, matplotlib):
If return_data is True, returns a pandas DataFrame used for plotting. Otherwise, displays the plot.
Example 
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# Generate a heatmap of fold changes with custom settings
sm.pl.foldchange(adata, label='foldchange', method='heatmap', cmap='coolwarm', log=True,
                 p_val=0.05, nonsig_color='lightgrey', xticks_rotation=45)

# Create a parallel coordinates plot to visualize fold changes across groups
sm.pl.foldchange(adata, label='foldchange', method='parallel_coordinates', log=True,
                 parallel_coordinates_color=['red', 'blue', 'green'], invert_axis=True)

# Return the data frame used for fold change visualization
df_foldchange = sm.pl.foldchange(adata, label='foldchange', return_data=True)
Source code in scimap/plotting/foldchange.py 
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def foldchange (adata, 
                label='foldchange', 
                p_val=0.05, 
                nonsig_color='grey',
                subset_xaxis=None,
                subset_yaxis=None,
                cmap = 'vlag', 
                log=True,
                center=0, 
                method='heatmap', 
                invert_axis=None,
                parallel_coordinates_color=None,
                matplotlib_bbox_to_anchor=(1.04,1),
                matplotlib_legend_loc='upper left',
                xticks_rotation=90,
                return_data = False,
                fileName='foldchange.pdf',
                saveDir=None,
                **kwargs):
    """
Parameters:
        adata (anndata.AnnData):  
            The annotated data matrix with fold change calculations.

        label (str):  
            Label key from `adata.uns` indicating the fold change data to visualize.

        p_val (float):  
            P-value threshold for significance; values above this threshold are considered non-significant.

        nonsig_color (str):  
            Color for non-significant changes in the visualization.

        subset_xaxis (list, optional):  
            Categories to include from the x-axis for plotting.

        subset_yaxis (list, optional):  
            Categories to include from the y-axis for plotting.

        cmap (str):  
            Colormap for the heatmap visualization.

        log (bool):  
            If True, fold changes are converted to log2 scale for visualization.

        center (float):  
            The value at which the colormap is centered.

        method (str):  
            Plotting method, either 'heatmap' for a heatmap or 'parallel_coordinates' for a parallel coordinates plot.

        invert_axis (bool, optional):  
            If True, inverts the x and y axes in the plot. Default is False.

        parallel_coordinates_color (list, optional):  
            Specifies custom colors for parallel coordinates plot.

        matplotlib_bbox_to_anchor (tuple):  
            Adjusts the legend position in parallel coordinates plot.

        matplotlib_legend_loc (str):  
            Specifies the location of the legend.

        xticks_rotation (int):  
            Rotation angle for x-axis tick labels.

        return_data (bool):  
            If True, returns the data frame used for plotting instead of the plot.

Returns:
        Dataframe; plot (pandas, matplotlib):  
            If `return_data` is True, returns a pandas DataFrame used for plotting. Otherwise, displays the plot.

Example:
        ```python

        # Generate a heatmap of fold changes with custom settings
        sm.pl.foldchange(adata, label='foldchange', method='heatmap', cmap='coolwarm', log=True,
                         p_val=0.05, nonsig_color='lightgrey', xticks_rotation=45)

        # Create a parallel coordinates plot to visualize fold changes across groups
        sm.pl.foldchange(adata, label='foldchange', method='parallel_coordinates', log=True,
                         parallel_coordinates_color=['red', 'blue', 'green'], invert_axis=True)

        # Return the data frame used for fold change visualization
        df_foldchange = sm.pl.foldchange(adata, label='foldchange', return_data=True)

        ```
    """

    # set color for heatmap
    #cmap_updated = copy.copy(matplotlib.cm.get_cmap(cmap))
    cmap_updated = matplotlib.cm.get_cmap(cmap)
    cmap_updated.set_bad(color=nonsig_color)


    # get the data
    fc = adata.uns[str(label)+'_fc']
    p = adata.uns[str(label)+'_pval']

    #fold
    fold = fc.copy()
    p_mask = p.copy()

    # reference image
    ref = fold.index.name

    # log
    if log is True:
        fold = np.log2(fold)

    # create a mask for non-sig values
    p_mask[p_mask > p_val] = np.nan

    # subset x axis data
    if subset_xaxis is not None:
        if isinstance (subset_xaxis, str):
            subset_xaxis = [subset_xaxis]
        fold = fold [subset_xaxis]
        p_mask = p_mask [subset_xaxis]
        #reorder

    # subset y axis data
    if subset_yaxis is not None:
        if isinstance (subset_yaxis, str):
            subset_yaxis = [subset_yaxis]
        fold = fold.loc [subset_yaxis]
        p_mask = p_mask.loc [subset_yaxis]
        #reorder

    # invert axis if user requests
    if invert_axis is True:
        fold = fold.T
        p_mask = p_mask.T

    #mask
    mask = p_mask.isnull() # identify the NAN's for masking 

    if method == 'heatmap':
        # heatmap of the foldchange
        #g= sns.clustermap(fold, cmap=cmap, mask=mask, center=center, col_cluster=False, row_cluster=False)
        fig= sns.clustermap(fold, cmap=cmap, mask=mask, center=center, **kwargs)
        plt.suptitle('reference: '+ str(ref))
        plt.setp(fig.ax_heatmap.get_xticklabels(), rotation=xticks_rotation)
        plt.tight_layout()


    if method == 'parallel_coordinates':
        fold['sample'] = fold.index
        # plotting
        fig, axes = plt.subplots()
        if parallel_coordinates_color is not None:
            parallel_coordinates(fold, 'sample', color=parallel_coordinates_color, **kwargs)
        else:
            #parallel_coordinates(fold, 'sample', colormap=cmap_updated)
            parallel_coordinates(fold, 'sample', colormap=cmap_updated, **kwargs)
        axes.grid(False)
        plt.legend(bbox_to_anchor=matplotlib_bbox_to_anchor, loc=matplotlib_legend_loc)
        plt.axhline(y=0, color='black', linestyle='-')
        plt.xticks(rotation = xticks_rotation)
        plt.suptitle('reference: '+ str(ref))
        fig.tight_layout()

    # save
    if saveDir:
        if not os.path.exists(saveDir):
            os.makedirs(saveDir)
        full_path = os.path.join(saveDir, fileName)
        plt.savefig(full_path, dpi=300)
        plt.close()
        print(f"Saved plot to {full_path}")
    else:
        plt.show()

    # return data
    if return_data is True:
        return fold