scanpy.pl.matrixplot#
- scanpy.pl.matrixplot(adata, var_names, groupby, *, use_raw=None, log=False, num_categories=7, categories_order=None, figsize=None, dendrogram=False, title=None, cmap='viridis', colorbar_title='Mean expression\\nin group', gene_symbols=None, var_group_positions=None, var_group_labels=None, var_group_rotation=None, layer=None, standard_scale=None, values_df=None, swap_axes=False, show=None, save=None, ax=None, return_fig=False, vmin=None, vmax=None, vcenter=None, norm=None, **kwds)[source]#
Creates a heatmap of the mean expression values per group of each var_names.
This function provides a convenient interface to the
MatrixPlot
class. If you need more flexibility, you should useMatrixPlot
directly.- Parameters:
- adata
AnnData
Annotated data matrix.
- var_names
Union
[str
,Sequence
[str
],Mapping
[str
,Union
[str
,Sequence
[str
]]]] var_names
should be a valid subset ofadata.var_names
. Ifvar_names
is a mapping, then the key is used as label to group the values (seevar_group_labels
). The mapping values should be sequences of validadata.var_names
. In this case either coloring or ‘brackets’ are used for the grouping of var names depending on the plot. Whenvar_names
is a mapping, then thevar_group_labels
andvar_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 ofadata
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 thercParam['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 usingscanpy.tl.dendrogram()
. Iftl.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 defaultvar_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 parametervar_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 fourthvar_name
and the tenthvar_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, thenadata.X
is plotted. Iflayer
is set to a valid layer name, then the layer is plotted.layer
takes precedence overuse_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:'viridis'
) 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
(default:False
) By default, the x axis contains
var_names
(e.g. genes) and the y axis thegroupby
categories. By settingswap_axes
then x are thegroupby
categories and y thevar_names
.- return_fig
bool
|None
(default:False
) Returns
DotPlot
object. Useful for fine-tuning the plot. Takes precedence overshow=False
.- show
bool
|None
(default:None
) Show the plot, do not return axis.
- save
str
|bool
|None
(default:None
) If
True
or astr
, 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
https://matplotlib.org/stable/tutorials/colors/colormapnorms.html
for details.- kwds
Are passed to
matplotlib.pyplot.pcolor()
.
- adata
- Return type:
MatrixPlot
|dict
[str
,Axes
] |None
- Returns:
If
return_fig
isTrue
, returns aMatrixPlot
object, else ifshow
is false, return axes dict
See also
MatrixPlot
The MatrixPlot class can be used to to control several visual parameters not available in this function.
rank_genes_groups_matrixplot()
to plot marker genes identified using the
rank_genes_groups()
function.
Examples
import scanpy as sc adata = sc.datasets.pbmc68k_reduced() markers = ['C1QA', 'PSAP', 'CD79A', 'CD79B', 'CST3', 'LYZ'] sc.pl.matrixplot(adata, markers, groupby='bulk_labels', dendrogram=True)
Using var_names as dict:
markers = {'T-cell': 'CD3D', 'B-cell': 'CD79A', 'myeloid': 'CST3'} sc.pl.matrixplot(adata, markers, groupby='bulk_labels', dendrogram=True)
Get Matrix object for fine tuning:
mp = sc.pl.matrixplot(adata, markers, 'bulk_labels', return_fig=True) mp.add_totals().style(edge_color='black').show()
The axes used can be obtained using the get_axes() method
axes_dict = mp.get_axes()