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Basic tutorial

Installation

A conda environment is strongly recommended with python ≥ 3.10.

conda create -n enrichmap_env python=3.11
conda activate enrichmap_env

Then, install enrichmap via pip.

pip install enrichmap

Or directly from GitHub:

pip install git+https://github.com/secrierlab/enrichmap.git

Usage

import scanpy as sc
import enrichmap as em

# Load your AnnData object
adata = sc.read_h5ad("PATH/TO/YOUR/DATA.h5ad")

# Define a gene set
gene_set = ["CD3D", "CD3E", "CD8A"]

# Run scoring
em.tl.score(
    adata=adata,
    gene_set=gene_set,
    score_key="T_cell_signature",
    smoothing=True,  # by default
    correct_spatial_covariates=True,  # by default
    batch_key=None  # Set batch_key if working with multiple slides
)

# Visualise
em.pl.spatial_enrichmap(
    adata=adata,
    score_key="T_cell_signature_score"
)

Note

EnrichMap currently does not support reading in SpatialData format. However, users can simply convert SpatialData to legacy AnnData to use EnrichMap.

import spatialdata_io as sd
# Read in SpatialData
sdata = sd.visium_hd("PATH_TO_DATA_FOLDER/")
# Convert to AnnData
adata = to_legacy_anndata(
    sdata,
    include_images=True,
    table_name="square_008um",
    coordinate_system="downscaled_hires"
)

Documentation

Comprehensive documentation is available at: https://enrichmap.readthedocs.io/en/latest

Contributing

If you have ideas for new features or spot a bug, please open an issue or submit a pull request.

License

This project is licensed under the GNU GENERAL PUBLIC LICENSE.

Citation

Celik C & Secrier M (2025). EnrichMap: Spatially-informed enrichment analysis for functional interpretation of spatial transcriptomics. biorxiv.com

Copyright

This code is free and is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. See the GNU General Public License for more details.