scanpy.pl.dotplot

scanpy.pl.dotplot(adata, var_names, groupby, use_raw=None, log=False, num_categories=7, expression_cutoff=0.0, mean_only_expressed=False, cmap='Reds', dot_max=None, dot_min=None, standard_scale=None, smallest_dot=0.0, title=None, colorbar_title='Mean expression\\nin group', size_title='Fraction of cells\\nin group (%)', figsize=None, dendrogram=False, gene_symbols=None, var_group_positions=None, var_group_labels=None, var_group_rotation=None, layer=None, swap_axes=False, dot_color_df=None, show=None, save=None, ax=None, return_fig=False, **kwds)
../_images/scanpy.pl.dotplot.png

Makes a dot plot of the expression values of var_names.

For each var_name and each groupby category a dot is plotted. Each dot represents two values: mean expression within each category (visualized by color) and fraction of cells expressing the var_name in the category (visualized by the size of the dot). If groupby is not given, the dotplot assumes that all data belongs to a single category.

Note

A gene is considered expressed if the expression value in the adata (or adata.raw) is above the specified threshold which is zero by default.

An example of dotplot usage is to visualize, for multiple marker genes, the mean value and the percentage of cells expressing the gene across multiple clusters.

This function provides a convenient interface to the DotPlot class. If you need more flexibility, you should use DotPlot 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: 'Mean expression\nin group')

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

cmap : strstr (default: 'Reds')

String denoting matplotlib color map.

standard_scale : {‘var’, ‘group’}, NoneOptional[Literal[‘var’, ‘group’]] (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 : bool, NoneOptional[bool] (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.

size_title : str, NoneOptional[str] (default: 'Fraction of cells\nin group (%)')

Title for the size legend. New line character (n) can be used.

expression_cutoff : floatfloat (default: 0.0)

Expression cutoff that is used for binarizing the gene expression and determining the fraction of cells expressing given genes. A gene is expressed only if the expression value is greater than this threshold.

mean_only_expressed : boolbool (default: False)

If True, gene expression is averaged only over the cells expressing the given genes.

dot_max : float, NoneOptional[float] (default: None)

If none, the maximum dot size is set to the maximum fraction value found (e.g. 0.6). If given, the value should be a number between 0 and 1. All fractions larger than dot_max are clipped to this value.

dot_min : float, NoneOptional[float] (default: None)

If none, the minimum dot size is set to 0. If given, the value should be a number between 0 and 1. All fractions smaller than dot_min are clipped to this value.

smallest_dot : float, NoneOptional[float] (default: 0.0)

If none, the smallest dot has size 0. All expression levels with dot_min are plotted with this size.

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

Show the plot, do not return axis.

save : str, bool, NoneUnion[str, bool, 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.

kwds

Are passed to matplotlib.pyplot.scatter().

Return type

DotPlot, dict, NoneUnion[DotPlot, dict, None]

Returns

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

See also

DotPlot

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

rank_genes_groups_dotplot()

to plot marker genes identified using the rank_genes_groups() function.

Examples

Create a dot plot using the given markers and the PBMC example dataset grouped by the category ‘bulk_labels’.

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

Using var_names as dict:

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

Get DotPlot object for fine tuning

>>> dp = sc.pl.dotplot(adata, markers, 'bulk_labels', return_fig=True)
>>> dp.add_totals().style(dot_edge_color='black', dot_edge_lw=0.5).show()

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

>>> axes_dict = dp.get_axes()
>>> print(axes_dict)