EnrichMap: Spatially-aware gene set enrichment

EnrichMap

EnrichMap is a lightweight tool designed to compute and visualise enrichment scores of a given gene set or signature in spatial transcriptomics datasets across different platforms. It offers flexible scoring, batch correction, spatial smoothing and visual outputs for intuitive exploration of biological signatures.

Features

Fast computation of enrichment scores
Support for batch correction and spatial covariates
Built-in spatial smoothing
Visualisation tools for intuitive mapping
Easy integration with AnnData (.h5ad) objects

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

Basic 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"
)

Important 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.

Metadata

Release files for enrichmap 0.2.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for enrichmap 0.2.3
File	Size	Uploaded
enrichmap-0.2.3.tar.gz	75.5 MB	Jun 25, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for enrichmap 0.2.3
File	Interpreter	ABI	Platform	Reset
enrichmap-0.2.3-py3-none-any.whl 70.7 kB Jun 25, 2026	Python 3	none	any	Details

Total release size: 75.6 MB

Release files / enrichmap-0.2.3.tar.gz

Download URL	enrichmap-0.2.3.tar.gz
Size	75.5 MB
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SHA-256 checksum How to use checksums	`3f5920d45a3e8bd1f77bc54f915e62f1d80386cd33cc57d2401e9dfb5ab792fc`
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Uploaded via	`twine/6.2.0 CPython/3.14.6`

Release files / enrichmap-0.2.3-py3-none-any.whl

Download URL	enrichmap-0.2.3-py3-none-any.whl
Size	70.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`433c85b13406257b8bfe351f8262f842cc41b31e4b3b36b41bb22458d29fda24`
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Upload date	Jun 25, 2026
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Uploaded via	`twine/6.2.0 CPython/3.14.6`