episcanpy.api.pl.stacked_violin

episcanpy.api.pl.stacked_violin(adata, var_names, groupby, log=False, use_raw=None, num_categories=7, title=None, colorbar_title='Median expression\\nin group', figsize=None, dendrogram=False, gene_symbols=None, var_group_positions=None, var_group_labels=None, standard_scale=None, var_group_rotation=None, layer=None, stripplot=False, jitter=False, size=1, scale='width', yticklabels=False, order=None, swap_axes=False, show=None, save=None, return_fig=False, row_palette=None, cmap='Blues', ax=None, vmin=None, vmax=None, vcenter=None, norm=None, **kwds)

Stacked violin plots.

Makes a compact image composed of individual violin plots (from violinplot()) stacked on top of each other. Useful to visualize gene expression per cluster.

Wraps seaborn.violinplot() for AnnData.

This function provides a convenient interface to the StackedViolin class. If you need more flexibility, you should use StackedViolin directly.

Parameters

adata : AnnDataAnnData

Annotated data matrix.

var_names : str | Sequence[str] | MappingUnion[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]

var_names should be a valid subset of adata.var_names. If var_names is a mapping, then the key is used as label to group the values (see var_group_labels). The mapping values should be sequences of valid adata.var_names. In this case either coloring or ‘brackets’ are used for the grouping of var names depending on the plot. When var_names is a mapping, then the var_group_labels and var_group_positions are set.

groupby : str | Sequence[str]Union[str, Sequence[str]]

The key of the observation grouping to consider.

use_raw : bool | NoneOptional[bool] (default: None)

Use raw attribute of adata if present.

log : boolbool (default: False)

Plot on logarithmic axis.

num_categories : intint (default: 7)

Only used if groupby observation is not categorical. This value determines the number of groups into which the groupby observation should be subdivided.

categories_order

Order in which to show the categories. Note: add_dendrogram or add_totals can change the categories order.

figsize : Tuple[float, float] | NoneOptional[Tuple[float, float]] (default: None)

Figure size when multi_panel=True. Otherwise the rcParam[‘figure.figsize] value is used. Format is (width, height)

dendrogram : bool | strUnion[bool, str] (default: False)

If True or a valid dendrogram key, a dendrogram based on the hierarchical clustering between the groupby categories is added. The dendrogram information is computed using scanpy.tl.dendrogram(). If tl.dendrogram has not been called previously the function is called with default parameters.

gene_symbols : str | NoneOptional[str] (default: None)

Column name in .var DataFrame that stores gene symbols. By default var_names refer to the index column of the .var DataFrame. Setting this option allows alternative names to be used.

var_group_positions : Sequence[Tuple[int, int]] | NoneOptional[Sequence[Tuple[int, int]]] (default: None)

Use this parameter to highlight groups of var_names. This will draw a ‘bracket’ or a color block between the given start and end positions. If the parameter var_group_labels is set, the corresponding labels are added on top/left. E.g. var_group_positions=[(4,10)] will add a bracket between the fourth var_name and the tenth var_name. By giving more positions, more brackets/color blocks are drawn.

var_group_labels : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

Labels for each of the var_group_positions that want to be highlighted.

var_group_rotation : float | NoneOptional[float] (default: None)

Label rotation degrees. By default, labels larger than 4 characters are rotated 90 degrees.

layer : str | NoneOptional[str] (default: None)

Name of the AnnData object layer that wants to be plotted. By default adata.raw.X is plotted. If use_raw=False is set, then adata.X is plotted. If layer is set to a valid layer name, then the layer is plotted. layer takes precedence over use_raw.

title : str | NoneOptional[str] (default: None)

Title for the figure

colorbar_title : str | NoneOptional[str] (default: 'Median expression\nin group')

Title for the color bar. New line character (n) can be used.

cmap : str | NoneOptional[str] (default: 'Blues')

String denoting matplotlib color map.

standard_scale : {‘var’, ‘obs’} | NoneOptional[Literal[‘var’, ‘obs’]] (default: None)

Whether or not to standardize the given dimension between 0 and 1, meaning for each variable or group, subtract the minimum and divide each by its maximum.

swap_axes : boolbool (default: False)

By default, the x axis contains var_names (e.g. genes) and the y axis the groupby categories. By setting swap_axes then x are the groupby categories and y the var_names.

return_fig : bool | NoneOptional[bool] (default: False)

Returns DotPlot object. Useful for fine-tuning the plot. Takes precedence over show=False.

stripplot : boolbool (default: False)

Add a stripplot on top of the violin plot. See stripplot().

jitter : float | boolUnion[float, bool] (default: False)

Add jitter to the stripplot (only when stripplot is True) See stripplot().

size : intint (default: 1)

Size of the jitter points.

order : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

Order in which to show the categories. Note: if dendrogram=True the categories order will be given by the dendrogram and order will be ignored.

scale : {‘area’, ‘count’, ‘width’}Literal[‘area’, ‘count’, ‘width’] (default: 'width')

The method used to scale the width of each violin. If ‘width’ (the default), each violin will have the same width. If ‘area’, each violin will have the same area. If ‘count’, a violin’s width corresponds to the number of observations.

yticklabels : bool | NoneOptional[bool] (default: False)

Set to true to view the y tick labels.

row_palette : str | NoneOptional[str] (default: None)

Be default, median values are mapped to the violin color using a color map (see cmap argument). Alternatively, a ‘row_palette` can be given to color each violin plot row using a different colors. The value should be a valid seaborn or matplotlib palette name (see color_palette()). Alternatively, a single color name or hex value can be passed, e.g. ‘red’ or ‘#cc33ff’.

show : bool | NoneOptional[bool] (default: None)

Show the plot, do not return axis.

save : bool | str | NoneUnion[bool, str, None] (default: None)

If True or a str, save the figure. A string is appended to the default filename. Infer the filetype if ending on {‘.pdf’, ‘.png’, ‘.svg’}.

ax : _AxesSubplot | NoneOptional[_AxesSubplot] (default: None)

A matplotlib axes object. Only works if plotting a single component.

vmin : float | NoneOptional[float] (default: None)

The value representing the lower limit of the color scale. Values smaller than vmin are plotted with the same color as vmin.

vmax : float | NoneOptional[float] (default: None)

The value representing the upper limit of the color scale. Values larger than vmax are plotted with the same color as vmax.

vcenter : float | NoneOptional[float] (default: None)

The value representing the center of the color scale. Useful for diverging colormaps.

norm : Normalize | NoneOptional[Normalize] (default: None)

Custom color normalization object from matplotlib. See https://matplotlib.org/stable/tutorials/colors/colormapnorms.html for details.

kwds

Are passed to violinplot().

Return type

StackedViolin | dict | NoneUnion[StackedViolin, dict, None]

Returns

If return_fig is True, returns a StackedViolin object, else if show is false, return axes dict

See also

StackedViolin

The StackedViolin class can be used to to control several visual parameters not available in this function.

rank_genes_groups_stacked_violin()

using the rank_genes_groups() function.

Examples

Visualization of violin plots of a few genes grouped by the category ‘bulk_labels’:

>>> import scanpy as sc
>>> adata = sc.datasets.pbmc68k_reduced()
>>> markers = ['C1QA', 'PSAP', 'CD79A', 'CD79B', 'CST3', 'LYZ']
>>> sc.pl.stacked_violin(adata, markers, groupby='bulk_labels', dendrogram=True)

Same visualization but passing var_names as dict, which adds a grouping of the genes on top of the image:

>>> markers = {'T-cell': 'CD3D', 'B-cell': 'CD79A', 'myeloid': 'CST3'}
>>> sc.pl.stacked_violin(adata, markers, groupby='bulk_labels', dendrogram=True)

Get StackedViolin object for fine tuning

>>> vp = sc.pl.stacked_violin(adata, markers, 'bulk_labels', return_fig=True)
>>> vp.add_totals().style(ylim=(0,5)).show()

The axes used can be obtained using the get_axes() method:

>>> axes_dict = vp.get_axes()