%matplotlib inline

Plot segmentation masks

This example shows how to use squidpy.pl.spatial_segment to plot segmentation masks and features in anndata.AnnData.

This plotting is useful when segmentation masks and underlying image are available.

See also

See {doc}`plot_scatter` for scatter plot.

import squidpy as sq

adata = sq.datasets.mibitof()
adata.uns["spatial"].keys()

dict_keys(['point16', 'point23', 'point8'])

In this dataset we have 3 unique keys, which means that there are 3 unique `library_id[. As detailed in {ref}`sphx_glr_auto_tutorials_tutorial_read_spatial.py]{.title-ref}, it means that there are 3 unique field of views (FOV) in this dataset. The information to link the library ids to the observations are stored in adata.obs.

adata.obs

	row_num	point	cell_id	X1	center_rowcoord	center_colcoord	cell_size	category	donor	Cluster	batch	library_id
3034-0	3086	23	2	60316.0	269.0	7.0	408.0	carcinoma	21d7	Epithelial	0	point23
3035-0	3087	23	3	60317.0	294.0	6.0	408.0	carcinoma	21d7	Epithelial	0	point23
3036-0	3088	23	4	60318.0	338.0	4.0	304.0	carcinoma	21d7	Imm_other	0	point23
3037-0	3089	23	6	60320.0	372.0	6.0	219.0	carcinoma	21d7	Myeloid_CD11c	0	point23
3038-0	3090	23	8	60322.0	417.0	5.0	303.0	carcinoma	21d7	Myeloid_CD11c	0	point23
...	...	...	...	...	...	...	...	...	...	...	...	...
47342-2	48953	16	1103	2779.0	143.0	1016.0	283.0	carcinoma	90de	Fibroblast	2	point16
47343-2	48954	16	1104	2780.0	814.0	1017.0	147.0	carcinoma	90de	Fibroblast	2	point16
47344-2	48955	16	1105	2781.0	874.0	1018.0	142.0	carcinoma	90de	Imm_other	2	point16
47345-2	48956	16	1106	2782.0	257.0	1019.0	108.0	carcinoma	90de	Fibroblast	2	point16
47346-2	48957	16	1107	2783.0	533.0	1019.0	111.0	carcinoma	90de	Fibroblast	2	point16

3309 rows × 12 columns

Specifically, the key `library_id[ in {attr}`adata.obs]{.title-ref} contains the same unique values contained in adata.uns. We can visualize the 3 spatial dataset with squidpy.pl.spatial_segment.

sq.pl.spatial_segment(
    adata, color="Cluster", library_key="library_id", seg_cell_id="cell_id"
)

There are several parameters that can be controlled. For instance, it is possible to plot segmentation masks as “contours”, in order to visualize the underlying image. Let’s visualize it for one specific cropped FOV.

sq.pl.spatial_segment(
    adata,
    color="Cluster",
    library_key="library_id",
    library_id="point8",
    seg_cell_id="cell_id",
    seg_contourpx=10,
    crop_coord=[(0, 0, 300, 300)],
)

It’s also possible to add an outline to better distinguish segmentation masks’ boundaries. Furthermore, the underlying image can be removed, gray scaled or single channels can be plotted.

sq.pl.spatial_segment(
    adata,
    color="Cluster",
    groups=["Fibroblast", "Epithelial"],
    library_key="library_id",
    library_id=["point8", "point16"],
    seg_cell_id="cell_id",
    seg_outline=True,
    img_channel=0,
    img_cmap="magma",
)

If groups of observations are plotted (as above), it’s possible to modify whether to “visualize” the segmentation masks that do not belong to any selected group. It is set as “transparent” by default (see above) but in cases where e.g. no image is present it can be useful to visualize them nonetheless

sq.pl.spatial_segment(
    adata,
    color="Cluster",
    groups=["Fibroblast", "Epithelial"],
    library_key="library_id",
    seg_cell_id="cell_id",
    seg_outline=True,
    img=False,
    frameon=False,
)

Finally, a scale bar can be added, where size and pixel units must be passed. The size for this example are not the real values and are for purely visualization purposes.

sq.pl.spatial_segment(
    adata,
    color="CD68",
    library_key="library_id",
    seg_cell_id="cell_id",
    img=False,
    cmap="inferno",
    scalebar_dx=2.0,
    scalebar_kwargs={"scale_loc": "bottom", "location": "lower right"},
)