squidpy.im.calculate_image_features

squidpy.im.calculate_image_features(adata, img, layer=None, library_id=None, features='summary', features_kwargs=mappingproxy({}), key_added='img_features', copy=False, n_jobs=None, backend='loky', show_progress_bar=True, **kwargs)

Calculate image features for all observations in adata.

Parameters

• adata (AnnData) – Annotated data object.

• img (ImageContainer) – High-resolution image.

• layer (Optional[str]) – Image layer in img that should be processed. If None and only 1 layer is present, it will be selected.

• library_id (Union[str, Sequence[str], None]) –

  • If None, there should only exist one entry in anndata.AnnData.uns ['{spatial_key}'].

  • If a str, first search anndata.AnnData.obs ['{library_id}'] which contains the mapping from observations to library ids, then search anndata.AnnData.uns ['{spatial_key}'].

• features (Union[str, Sequence[str]]) –

  Features to be calculated. Valid options are:

  • ’texture’ - summary stats based on repeating patterns squidpy.im.ImageContainer.features_texture().

  • ’summary’ - summary stats of each image channel squidpy.im.ImageContainer.features_summary().

  • ’histogram’ - counts in bins of image channel’s histogram squidpy.im.ImageContainer.features_histogram().

  • ’segmentation’ - stats of a cell segmentation mask squidpy.im.ImageContainer.features_segmentation().

  • ’custom’ - extract features using a custom function squidpy.im.ImageContainer.features_custom().

• features_kwargs (Mapping[str, Mapping[str, Any]]) – Keyword arguments for the different features that should be generated, such as { 'texture': { ... }, ... }.

• key_added (str) – Key in anndata.AnnData.obsm where to store the calculated features.

• copy (bool) – If True, return the result, otherwise save it to the adata object.

• n_jobs (Optional[int]) – Number of parallel jobs.

• backend (str) – Parallelization backend to use. See joblib.Parallel for available options.

• show_progress_bar (bool) – Whether to show the progress bar or not.

• kwargs (Any) – Keyword arguments for squidpy.im.ImageContainer.generate_spot_crops().

Return type

Optional[DataFrame]

Returns

: If copy = True, returns a pandas.DataFrame where columns correspond to the calculated features.

Otherwise, modifies the adata object with the following key:

• anndata.AnnData.uns ['{key_added}'] - the above mentioned dataframe.

Raises

ValueError – If a feature is not known.