Export measurements to AnnData

Create an .h5ad file containing measured features and object metadata for analysis in AnnData or Scanpy. Start with a spaCR project containing measurements/measurements.db. For a worked example, download the project from the Annotate tutorial.

Export from the application

  1. From Home, open Measure, then AnnData Export.

  2. Select the measured project as the source.

  3. Set Anndata out to a new .h5ad filename. Leaving it empty writes beneath the project’s results folder. A successful export replaces an existing file at the chosen path; use separate names to keep both versions.

  4. Set Anndata single table to cell for one row per cell using that table’s features. Use nucleus or pathogen for those object types. Leave it empty for a cell-level joined export, where child measurements are aggregated onto their parent cells.

  5. Choose Anndata nan policy, then click Run. Read the completion message and open the output file to check its shape and feature names.

Choose how to handle missing features

  • keep retains missing values. Choose this when you want to decide how to handle them in the downstream analysis.

  • drop_features removes feature columns containing missing values.

  • drop_objects removes object rows containing missing feature values.

  • mean replaces missing values with the observed feature mean.

  • zero replaces missing values with zero. Use it only when zero is an appropriate value for the intended analysis.

The imputing policies retain the original missing positions in layers['missing']. These choices apply to the feature matrix, not to unknown metadata such as unavailable calibration values. Check object and feature counts after choosing a dropping policy. Scanpy operations such as scaling and PCA need missing feature values to be handled before use.

Inspect the output

X contains the object-by-feature matrix, obs contains object metadata, and var describes the features. Keep the source images and their paths: image pixels are not embedded in the export. With the Annotate example, a cell-only export using keep has 2,341 objects, 261 features and 28 missing feature values.

Export from Python

The following uses the same entry point as the application’s Run button:

import anndata as ad
from spacr.anndata_export import run_anndata_export

result = run_anndata_export({
    "src": "/path/to/project",
    "anndata_out": "/path/to/project/results/cells_keep.h5ad",
    "anndata_single_table": "cell",
    "anndata_nan_policy": "keep",
})
exported = ad.read_h5ad(result.path)
print(exported.shape)
print(exported.obs.head())

For explicit function arguments, see spacr.anndata_export.export_anndata().

Follow the video

The AnnData video walks through these controls using the downloadable measurement example. It shows joined, cell-only and nucleus-only exports and compares the missing-value policies. The normal exporter handles entirely missing metadata directly. If writing a replacement fails, an existing completed output is preserved.