goatpy.filter

A wrapper around spatialdata’s filter_by_table_query that preserves images, points, and non-pixel shapes rather than dropping them.

Usage

>>> from goatpy.filter import filter_spatialdata

# Filter by any obs column >>> sub = filter_spatialdata(sdata, “annotation == ‘Tumor’”) >>> sub = filter_spatialdata(sdata, “GPCA_clusters == ‘3’”) >>> sub = filter_spatialdata(sdata, “MPI > 1000”)

# Filter by ion intensity (var_names) >>> sub = filter_spatialdata(sdata, “1581.6 > 500”, on=”expression”)

# Keep points unsubsetted (just retain all centroids) >>> sub = filter_spatialdata(sdata, “annotation == ‘Tumor’”, subset_points=False)

Functions

filter_spatialdata(→ spatialdata.SpatialData)

Subset a SpatialData object by a query string, preserving images, points,

Module Contents

goatpy.filter.filter_spatialdata(sdata: spatialdata.SpatialData, query: str, on: Literal['obs', 'expression'] = 'obs', table_name: str = 'maldi_adata', subset_points: bool = True, subset_annotations: bool = False) → spatialdata.SpatialData[source]

Subset a SpatialData object by a query string, preserving images, points, and annotation shapes.

Internally builds a boolean mask from the query, filters the table and pixel shapes consistently, then re-attaches images, centroids, and annotation polygons so nothing is silently dropped.

Parameters:

  • sdata (SpatialData)

  • query (str) – A pandas .query() string.

  • on ("obs" | "expression") –

    "obs" — query runs against maldi_adata.obs columns

    (annotation, GPCA_clusters, MPI, he_x, he_y …).

    "expression" — query runs against ion intensity columns

    (m/z var_names, dots auto-sanitised to underscores).

  • table_name (str) – Default "maldi_adata".

  • subset_points (bool) – If True (default), centroids are subsetted to match the filtered pixels. If False, all original centroids are kept as-is.

  • subset_annotations (bool) – If True, annotation polygon shapes are filtered to only keep classes that are present in the filtered pixels’ annotation obs column. If False (default), all annotation polygons are kept as-is.

Returns:

  • images — always kept unchanged

  • shapes["pixels"] — subsetted to matching pixels

  • shapes["annotations"] — kept or filtered depending on subset_annotations

  • points["centroids"] — subsetted or kept depending on subset_points

  • tables[table_name] — subsetted to matching rows

Return type:

SpatialData with

Examples

>>> filter_spatialdata(sdata, "annotation == 'Tumor'")
>>> filter_spatialdata(sdata, "GPCA_clusters == '3'")
>>> filter_spatialdata(sdata, "MPI > 1000")
>>> filter_spatialdata(sdata, "annotation == 'Tumor' and MPI > 500")
>>> filter_spatialdata(sdata, "1581.6 > 500", on="expression")