dotools_py.pl.split_embedding

dotools_py.pl.split_embedding(adata, split_by, figsize=(6, 5), title_fontproperties=None, ncols=4, path=None, filename='UMAP.svg', show=True, basis='X_umap', visium=False, sp_size=1.5, **kwargs)

Scatter plot splitting categorical data in an embedding.

This function takes a categorical column in adata.obs and generate a plot of subplots highlighting the different categories of the obs column.

Parameters:

adata AnnData

Annotated data matrix.

split_by str

Column in adata.obs to split by.

figsize tuple[float, float] (default: (6, 5))

Figure size, the format is (width, height).

title_fontproperties Dict[Literal['size', 'weight'], str | int] (default: None)

Dictionary which should contain ‘size’ and ‘weight’ to define the fontsize and fontweight of the title of the figure.

ncols int (default: 4)

Number of subplots per row.

path str | PathLike[str] | Path (default: None)

Path to the folder to save the figure.

filename str (default: 'UMAP.svg')

Name of file to use when saving the figure.

show bool (default: True)

If set to False, returns a dictionary with the matplotlib axes.

basis str (default: 'X_umap')

Embedding to use, default UMAP.

visium bool (default: False)

Set to True if you anndata has visium data.

sp_size float (default: 1.5)

Spot size when plotting visium data.

kwargs

Additional arguments for sc.pl.embedding() or sc.pl.spatial() if visium is True.

Return type:

Axes | None

Returns:

Depending on show, returns the plot if set to True or a dictionary with the axes.

Example

import dotools_py as do
adata = do.dt.example_10x_processed()
do.pl.split_embedding(adata, 'annotation', ncols=3)