episcanpy.pl.matrixplot¶
-
episcanpy.pl.
matrixplot
(adata, var_names, groupby=None, use_raw=None, log=False, num_categories=7, figsize=None, dendrogram=False, gene_symbols=None, var_group_positions=None, var_group_labels=None, var_group_rotation=None, layer=None, standard_scale=None, swap_axes=False, show=None, save=None, **kwds)¶ Creates a heatmap of the mean expression values per cluster of each var_names If groupby is not given, the matrixplot assumes that all data belongs to a single category.
- Parameters
- adata :
AnnData
AnnData
Annotated data matrix.
- var_names :
str
,Sequence
[str
],Mapping
Union
[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
,None
Optional
[str
] (default:None
) The key of the observation grouping to consider.
- use_raw :
bool
,None
Optional
[bool
] (default:None
) Use raw attribute of adata if present.
- log :
bool
bool
(default:False
) Plot on logarithmic axis.
- num_categories :
int
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.
- figsize :
Tuple
[float
,float
],None
Optional
[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
,str
Union
[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
Optional
[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
]],None
Optional
[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
],None
Optional
[Sequence
[str
]] (default:None
) Labels for each of the var_group_positions that want to be highlighted.
- var_group_rotation :
float
,None
Optional
[float
] (default:None
) Label rotation degrees. By default, labels larger than 4 characters are rotated 90 degrees.
- layer :
str
,None
Optional
[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.
- standard_scale :
Literal_
[var, group],None
Optional
[Literal_
[var, group]] (default:None
) Whether or not to standardize that dimension between 0 and 1, meaning for each variable or group, subtract the minimum and divide each by its maximum.
- show :
bool
,None
Optional
[bool
] (default:None
) Show the plot, do not return axis.
- save :
bool
,str
,None
Union
[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
A matplotlib axes object. Only works if plotting a single component.
- **kwds
Are passed to
matplotlib.pyplot.pcolor()
.
- adata :
- Returns
List of
Axes
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)
See also
rank_genes_groups_matrixplot()
to plot marker genes identified using the
rank_genes_groups()
function.