%matplotlib inline

Extract histogram features

This example shows how to extract histogram features from tissue image.

Histogram features give a more detailed view than summary features (examples_image_compute_summary_features) by computing a histogram of each image channel and returning bin-counts for each Visium spot.

In addition to feature_name and channels we can specify the following features_kwargs:

• bins - number of bins of the histogram, default is 10.

• v_range - range on which values are binned, default is the whole image range.

See also

See {doc}`compute_features` for general usage
of {func}`squidpy.im.calculate_image_features`.

import squidpy as sq

Lets load the fluorescence Visium dataset and calculate bin-counts (3 bins) of channels 0 and 1.

# get spatial dataset including high-resolution tissue image
img = sq.datasets.visium_fluo_image_crop()
adata = sq.datasets.visium_fluo_adata_crop()

# calculate histogram features and save in key "histogram_features"
sq.im.calculate_image_features(
    adata,
    img,
    features="histogram",
    features_kwargs={"histogram": {"bins": 3, "channels": [0, 1]}},
    key_added="histogram_features",
)

The result is stored in anndata.AnnData.obsm\ ['histogram_features'].

adata.obsm["histogram_features"].head()

	histogram_ch-0_bin-0	histogram_ch-0_bin-1	histogram_ch-0_bin-2	histogram_ch-1_bin-0	histogram_ch-1_bin-1	histogram_ch-1_bin-2
AAACGAGACGGTTGAT-1	28201	1649	2191	32041	0	0
AAAGGGATGTAGCAAG-1	30072	748	1221	31510	529	2
AAATGGCATGTCTTGT-1	29032	2252	757	30793	1002	246
AAATGGTCAATGTGCC-1	28672	2411	958	32041	0	0
AAATTAACGGGTAGCT-1	29996	1116	929	31367	674	0

Use squidpy.pl.extract to plot the histogram features on the tissue image or have a look at our interactive visualisation tutorial to learn how to use our interactive napari plugin. With these features we can e.g. appreciate the detailed distribution of intensity values of channel 0 (DAPI stain) on the different bins.

sq.pl.spatial_scatter(
    sq.pl.extract(adata, "histogram_features"),
    color=[
        None,
        "histogram_ch-0_bin-0",
        "histogram_ch-0_bin-1",
        "histogram_ch-0_bin-2",
    ],
    img_cmap="gray",
)