SpatialAIM — Annotation-Independent Mapping

Quick install

# Install from PyPi
pip install spatial-aim
# Run GUI
spatialaim gui

SpatialAIM maps an unannotated scRNA-seq reference (scRNA) onto single-cell-resolution spatial transcriptomics (ST) in a GUI or CLI. For a scRNA/ST pair it:

1. Over-clusters the scRNA reference once with Leiden into L start clusters.
2. Builds one agglomeration tree over those start clusters.
3. Sweeps K: for every K in (1,L) it cuts the tree into K cell states and assigns exactly one to each spot, reconstructing spot expression from the state profiles.
4. Finds best K: computes metrics (reconstruction quality, spatial coherence and transcriptional coherence) and helps the user find the most sensible cell state granularity via an interactive GUI.

The spot→state step is modular. In principle, any scRNA to ST alignment tool can be used here. We implement two baseline mappers (nearest_centroid, wann) and three reference mappers (tangram, tacco, dot). You are invited to add and try out your mapper of choice!

If you want to replace the Leiden overclustering from Step 1 with your own annotations, --start_from_annotation takes an existing annotation's L cell types as the start clusters instead.

Outline

• Why SpatialAIM?
• Input data format
• For users
• For developers

Why SpatialAIM?

Conventional mapping methods blindly map a pre-annotated scRNA reference onto spots, even if there is not enough signal in the spatial data to reliably distinguish between them. The hierarchical cell type clustering is based on both the scRNA and the ST data at hand. The user can find a cell type granularity that can be mapped reliably.

Input data format

To run SpatialAIM, you need a pair of a scRNA dataset and a ST dataset, both as raw counts in .h5ad format.

• scRNA <Name>.h5ad: X = raw counts (cells × genes); var_names = uppercase gene symbols.
• ST <Name>.h5ad: X = raw counts (spots × genes); var_names = uppercase gene symbols; obsm["spatial"] = (x, y)-coordinates of the spots: (n_spots × 2).

Single pair

Just have the two .h5ad files available and you are good to go.

Batch processing

SpatialAIM also supports batch processing of multiple pairs. Organise the data relationally the following way so one scRNA reference can be reused across many ST slices:

DATA/
├── scRNA/
│   ├── index.csv          # one row per scRNA dataset
│   └── <scName>.h5ad
├── ST/
│   ├── index.csv          # one row per ST dataset
│   ├── <stName>.h5ad
│   └── Segmentation/      # optional cell segmentation masks (index.csv column `Segmentation`)
└── pairs.csv              # links scRNA ↔ ST: PairID, scName, stName, …

See sample_dataset/ for a minimal example, and validate your own layout with spatialaim validate.

For users

Installation

SpatialAIM is installable from PyPI:

pip install spatial-aim

This installs one command, spatialaim, with subcommands (spatialaim --help). You can now already run spatialaim with the baseline mappers.

If you want to be able to select one of the reference aligners within SpatialAIM, you need conda installed on your machine. Please install the corresponding conda environments on your computer.

For SpatialAIM to use the environments, please make sure SpatialAIM can find your conda executable. Either set the environment variable CONDA_EXE or add your path to your conda installation to PATH. Make sure exe = os.environ.get("CONDA_EXE") or shutil.which("conda") returns the path to your conda.

Validate a dataset pair (spatialaim validate)

Check whether your data meets the main requirements.

spatialaim validate --scdata path/to/sc.h5ad --stdata path/to/st.h5ad [--segmentation path/to/mask.tif]

or for every pair of a pairs.csv laid out as in Batch Processing:

spatialaim validate --pairs_csv path/to/DATA/pairs.csv

Validations per dataset:

• scRNA reference data alone
  • raw count matrices (no NaN values, np negative values, integer-looking, not library-size normalized or log1p'd)
  • unique uppercase gene names
• ST data alone
  • raw count matrices (no NaN values, np negative values, integer-looking, not library-size normalized or log1p'd)
  • unique uppercase gene names
  • obsm["spatial"] of shape (n_spots, 2).
• Optional cell segmentation mask of an ST slice
  • a 2D .tif/.tiff of dtype uint32: 0 = background, cells labelled 1..max without gaps (labels without a cell in the h5ad are fine)
  • the ST h5ad carries the integer obs["segmentation_id"] (each cell's mask label, 0 = no segmentation): non-negative, non-zero ids unique and present in the mask
  • uns["segmentation_pixel_size_um"] (µm per pixel) is optional - a warning if missing
• Per pair
  • the size of the shared-gene intersection
  • whether any ST spot is left all-zero by it.
• If an index.csv sits next to the .h5ad files, its recorded counts and cell-type keys are cross-checked as well.

Run via GUI (spatialaim gui)

Interactive Streamlit app to run SpatialAIM from the browser and interactively browse the results for one scRNA/ST pair without any .

spatialaim gui [--server_port 8501]

Open the printed URL (default http://localhost:8501), set the inputs, pick one or multiple mapper(s), and click Run. As soon as both .h5ad paths are set, the GUI runs the same checks as spatialaim validate on that pair: a clean pair is confirmed with a tick in the sidebar, any error or warning is shown in a dialog you can close to continue anyway. The GUI writes each mapper's sweep to <output_dir>/<mapper>/. The results using each mapper are visualized in different tabs. When having run multiple mappers, also a "Compare" tab appears. An optional Segmentation path (the ST slice's cell segmentation mask, see the sidebar's Info) is validated with the pair and passed to the runs; their mapper tabs then get a "Shape features" card (shape consistency and the cells' area / eccentricity / solidity per cell type), and the cell-type modal a "Shape features" view. Without it the GUI is unchanged.

Run via CLI (spatialaim run)

Run SpatialAIM via CLI on a single pair or in batch mode. The spot→state mapper is chosen with argument --mapping:

• nearest_centroid (default): Per spot, select the cell state by highest cosine similarity between the state's centroid and the spot's expression. One-hot assignment by default.
• wann: Weighted Adaptive Nearest Neighbor (Di Salvo et al., TMLR 2025): each reference cell gets a label-reliability score; each spot inherits the neighbourhood size of its nearest reference cell and votes over its nearest neighbours (labelled single reference cells by cosine distance) weighted by reliability.
• tangram / tacco / dot: delegate the spot→state step to given external aligner (one alignment per K, run in its own conda env; slower). Requires the corresponding reference env (see Installation).

The linkage of the agglomeration tree over the start clusters is chosen with --linkage_method:

• average (default): UPGMA — average pairwise distance; no size term, tends to peel small tight groups off a growing dominant state.
• ward: Ward's criterion (R's ward.D); carries a size term and tends to produce balanced states.

The start clusters the tree is built over come from the Leiden over-clustering by default. Pass --start_from_annotation <obs_column> to use a pre-existing annotation instead: its cell types become the start clusters, no over-clustering is computed at all, and K sweeps from the number of annotated types down (cells with no label are dropped). Give such runs their own --output_dir: a run root is named after the mapper alone, so the two modes would otherwise overwrite each other.

Single pair

spatialaim run --scdata path/to/sc.h5ad \
		--stdata path/to/st.h5ad \
		--output_dir path/to/out_dir \
		[--segmentation path/to/mask.tif] \
		[--mapping nearest_centroid|wann|tangram|tacco|dot] \
		[--linkage_method average|ward] \
		[--start_from_annotation <obs_column>] \
		[--leiden_resolution 3.0] \
		[--k_min 1] [--k_max <L>] [--k_step 1] \
		[--logging verbose]

--segmentation is the ST slice's optional cell segmentation mask; with it the analysis also scores shape consistency (column shape of k_comparison.csv), without it that score is NaN.

Batch mode (all pairs in pairs.csv): each slice's mask is taken from the Segmentation column of ST/index.csv (file in ST/Segmentation/), if set.

spatialaim run --pairs_csv path/to/pairs.csv \
		--sc_dir path/to/scRNA \
		--st_dir path/to/ST \
		--output_dir path/to/out_dir \
		[--mapping nearest_centroid|wann|tangram|tacco|dot] \
		[--linkage_method average|ward] \
		[--start_from_annotation <obs_column>] \
		[--leiden_resolution 3.0] \
		[--k_min 1] [--k_max <L>] [--k_step 1] \
		[--logging verbose]

Each pair is written to <out_dir>/<PairID>_<scName>__<stName>/.

Output layout (per pair):

<out_dir>/
├── reference_scaffold.h5ad        # cached reference preparation, shared by all mappers
└── <mapping>/                     # one folder per mapper, e.g. nearest_centroid/
    ├── config.yaml                # the run's settings
    ├── start_clustering.h5ad      # each reference cell's start cluster
    ├── cell_shape_features.csv    # only with a segmentation mask: area, eccentricity, solidity per cell
    ├── k_<kkk>/                   # one folder per K
    │   ├── spot_to_state_mapping_soft.h5ad   # the mapping P (spots × K), plus confidence if the mapper has one
    │   ├── start_cluster_to_state.csv        # which state each start cluster belongs to
    │   └── analysis/data/                    # the metrics for this K (the GUI plots them)
    ├── k_comparison.csv           # one row per K: all metrics and the K-selection scores
    └── k_selection.json           # the suggested K, overall and per criterion

Sample-dataset example:

spatialaim run \
	--scdata     sample_dataset/scRNA/sample_sc.h5ad \
	--stdata     sample_dataset/ST/sample_st.h5ad \
	--output_dir sample_output/sample

The sample dataset is for sample usage only, it has no meaning.

Annotation-based baseline

There is no separate command for it: spatialaim run --start_from_annotation <obs_column> covers it. The annotated types become the start clusters, and the sweep's K = number-of-types level is the baseline.

For developers

Installation of dev environment

If you want to contribute, clone this repository and create a local conda environment. This will include everything apart from the reference aligner methods.

conda env create -f environment.yml
conda activate spatialaim_env

# Optional: Install the SpatialAIM-CLI from local
pip install -e . --no-deps

Environments of reference aligners

If you want to use one of the reference aligners within SpatialAIM or standalone, you have to additionally create the following environments. Each reference aligner runs in its own conda environment.

SpatialAIM orchestrates them out-of-process via conda run, so you need conda on PATH. The env .yml files live in the repo under src/spatialaim/reference_aligners/.

Adding another reference aligner

Reference aligners are pluggable. Each runs the same way: a wrapper launched out-of-process in its own conda env. The framework learns about it from a single registry,src/spatialaim/reference_aligners/registry.py.

Adding a reference aligner is three steps:

1. Add the conda environment file and create it, e.g. conda env create -f src/spatialaim/reference_aligners/environment_<name>.yml.

2. Write src/spatialaim/reference_aligners/run_<name>.py satisfying the CLI API:

Invoked with only those four args, the wrapper must reproduce its canonical mapping.

3. Register it

Add one line to REFERENCE_ALIGNERS (src/spatialaim/reference_aligners/registry.py).

ReferenceAligner("<name>", "<name>_env", "spatialaim.reference_aligners.run_<name>"),

It is then selectable everywhere automatically:

• spatialaim gui (it will be selectable in the sidebar)
• spatialaim run --mapping <name> (as an SpatialAIM reference mapper, one alignment per K) — including --start_from_annotation, where it maps onto a pre-existing annotation

4. Create PR

Create a PR with those changes to make it available for all users!