scanpy.pl.dotplot

scanpy.pl.dotplot(adata, var_names, groupby, *, use_raw=None, log=False, num_categories=7, categories_order=None, expression_cutoff=0.0, mean_only_expressed=False, standard_scale=None, 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, vmin=None, vmax=None, vcenter=None, norm=None, cmap='Reds', dot_max=None, dot_min=None, smallest_dot=0.0, **kwds)

Make 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 AnnData

Annotated data matrix.

var_names str | Sequence[str] | Mapping[str, 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]

The key of the observation grouping to consider.

use_raw bool | None (default: None)

Use raw attribute of adata if present.

log bool (default: False)

Plot on logarithmic axis.

num_categories int (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 Sequence[str] | None (default: None)

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

figsize tuple[float, float] | None (default: None)

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

dendrogram 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 | None (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]] | None (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] | None (default: None)

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

var_group_rotation float | None (default: None)

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

layer str | None (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 | None (default: None)

Title for the figure

colorbar_title str | None (default: 'Mean expression\\nin group')

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

cmap Colormap | str | None (default: 'Reds')

String denoting matplotlib color map.

standard_scale Optional[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 | None (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 | None (default: False)

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

size_title str | None (default: 'Fraction of cells\\nin group (%)')

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

expression_cutoff float (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 bool (default: False)

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

dot_max float | None (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 | None (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 (default: 0.0)

All expression levels with dot_min are plotted with this size.

show bool | None (default: None)

Show the plot, do not return axis.

save 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 | None (default: None)

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

vmin float | None (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 | None (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 | None (default: None)

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

norm Normalize | None (default: None)

Custom color normalization object from matplotlib. See Colormap normalization for details.

kwds

Are passed to matplotlib.pyplot.scatter().

Return type:

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)