Extract summary features
This example shows how to extract summary features from the tissue image.
Summary features give a good overview over the intensity of each image channels at the location of the Visium spots.
They are calculated by using features = 'summary', which calls squidpy.im.ImageContainer.features_summary().
In addition to feature_name and channels we can specify the following features_kwargs:
quantiles- quantiles that are computed. By default, the 0.9th, 0.5th, and 0.1th quantiles are calculated.
See also
See Extract image features for general usage of
squidpy.im.calculate_image_features().
import squidpy as sq
First, let’s load the fluorescence Visium dataset.
# get spatial dataset including hires tissue image
img = sq.datasets.visium_fluo_image_crop()
adata = sq.datasets.visium_fluo_adata_crop()
Then, we calculate the 0.1th quantile, mean and standard deviation for the Visium spots
of the fluorescence channels 0 (DAPI) and 1 (GFAP).
In order to get statistics of only the tissue underneath the spots, we use the argument mask_circle = True.
When not setting this flag, statistics are calculated using a square crop centered on the spot.
# calculate summary features and save in key "summary_features"
sq.im.calculate_image_features(
adata,
img,
features="summary",
features_kwargs={
"summary": {
"quantiles": [0.1],
"channels": [0, 1],
}
},
key_added="summary_features",
mask_circle=True,
show_progress_bar=False,
)
The result is stored in adata.obsm['summary_features'].
adata.obsm["summary_features"].head()
Use squidpy.pl.extract() to plot the summary features on the tissue image or have a look at
our interactive visualization tutorial to learn
how to use our interactive napari plugin.
Note how the spatial distribution of channel means is different for fluorescence channels 0 (DAPI stain)
and 1 (GFAP stain).
sq.pl.spatial_scatter(
sq.pl.extract(adata, "summary_features"), color=[None, "summary_ch-0_mean", "summary_ch-1_mean"], img_cmap="gray"
)
Total running time of the script: ( 2 minutes 55.149 seconds)
Estimated memory usage: 384 MB