pl.plot_spatial – SpaNorm

pl.plot_spatial(
    adata,
    what='annotation',
    color=None,
    layer=None,
    basis=None,
    img=False,
    crop=False,
    img_alpha=1.0,
    size=None,
    circles=False,
    ax=None,
    show=None,
    library_id=None,
    img_key='hires',
    spatial_key='spatial',
    **kwargs,
)

Plot spots at their spatial coordinates (R plotSpatial).

Parameters

Name	Description	Default
adata	Annotated data matrix with `obsm[spatial_key]`.	required
what	`"annotation"` (`color` is an `obs` column), `"expression"` (`color` is a gene, read from `layer` or `X`) or `"reduceddim"` (`color` is a column name or index of `obsm[basis]`).	`'annotation'`
color	What to colour spots by; `None` draws plain spots.	`None`
layer	Expression layer (default `X`) and the `obsm` key (`"X_"` prefix optional).	`None`
basis	Expression layer (default `X`) and the `obsm` key (`"X_"` prefix optional).	`None`
img	Draw the tissue image from `uns["spatial"][library_id]["images"][img_key]` (scanpy Visium layout); coordinates are scaled by `tissue_<img_key>_scalef`. An error is raised when no image is present.	`False`
crop	Limit the axes to the extent of the spots (padded by the spot radius). Requires `img=True` and is ignored otherwise, as in R.	`False`
img_alpha	Image opacity in [0, 1].	`1.0`
size	Marker area (points^2), or the circle radius in full-resolution pixels when `circles=True` (default 100, as in R; the radius is multiplied by the image scale factor).	`None`
circles	Draw spots as true circles in data units (slower for large data).	`False`
ax	Axes to draw on; a new figure is created when `None`.	`None`
show	Call `plt.show()` when true (`None` is the same as `False`). The axes are always returned.	`None`
library_id	Entry of `uns["spatial"]` (default: the first), image resolution (`"hires"`/`"lowres"`) and `obsm` key of the coordinates.	`None`
img_key	Entry of `uns["spatial"]` (default: the first), image resolution (`"hires"`/`"lowres"`) and `obsm` key of the coordinates.	`None`
spatial_key	Entry of `uns["spatial"]` (default: the first), image resolution (`"hires"`/`"lowres"`) and `obsm` key of the coordinates.	`None`
**kwargs	Passed to the scatter (or `PatchCollection` when `circles=True`).	`{}`

Examples

Note

The code below runs when the site is built, on a small pre-fitted subset of HumanDLPFC (301 genes x 4015 spots; spanorm(sample_p=0.05, df_tps=2), spanorm_svg() and spanorm_pca() already applied) that is fitted once per build and loaded as adata. The outputs are those of the code shown.

import spanormpy as sn
# adata: the HumanDLPFC AnnData (raw counts in X, coordinates in obsm["spatial"])
# colour spots by an annotation
ax = sn.pl.plot_spatial(adata, color="AnnotatedCluster", size=3)

# colour spots by the expression of a gene
ax = sn.pl.plot_spatial(adata, what="expression", color="MOBP", size=3)

# use a layer (here the normalised values written by sn.spanorm) and a different colour map
ax = sn.pl.plot_spatial(adata, what="expression", color="MOBP", layer="logcounts",
                        size=3, cmap="magma")