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enrichmap

EnrichMap: Spatially-informed enrichment analysis for functional interpretation of spatial transcriptomics.

https://github.com/secrierlab/enrichmap

Science Score: 67.0%

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    Links to: biorxiv.org
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Keywords

geneset-enrichment spatial-transcriptomics
Last synced: 6 months ago · JSON representation ·

Repository

EnrichMap: Spatially-informed enrichment analysis for functional interpretation of spatial transcriptomics.

Basic Info
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  • Stars: 6
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
geneset-enrichment spatial-transcriptomics
Created 9 months ago · Last pushed 8 months ago
Metadata Files
Readme License Citation

README.md

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.

EnrichMap workflow

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.

bash conda create -n enrichmap_env python=3.11 conda activate enrichmap_env

Then, install enrichmap via pip.

bash pip install enrichmap

or directly from GitHub:

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

Basic usage

```python 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, genelist=geneset, scorekey="Tcellsignature", smoothing=True, # by default, correctspatialcovariates=True, # by default batchkey=None # Set batch_key if working with multiple slides )

Visualise

em.pl.spatialenrichmap( adata=adata, scorekey="Tcellsignature" ) ```

Important note: EnrichMap currently does not support reading in SpatialData format. However, users can simply convert SpatialData to legacy AnnData to use EnrichMap. ```python import spatialdata_io as sd

read in SpatialData

sdata = sd.visiumhd("PATHTODATAFOLDER/")

convert to AnnData

adata = tolegacyanndata(sdata, includeimages=True, tablename="square008um", coordinatesystem="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.

Owner

  • Name: Secrier Lab @ UCL Genetics Institute
  • Login: secrierlab
  • Kind: organization
  • Location: University College London, UK

Citation (CITATION.cff) 

cff-version: 1.0.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Celik"
  given-names: "Cenk"
- family-names: "Secrier"
  given-names: "Maria"
title: "EnrichMap: Spatially-informed enrichment analysis for functional interpretation of spatial transcriptomics"
version: 1.6.0
date-released: 2025-05-30
url: "https://github.com/secrierlab/enrichmap"

GitHub Events

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  • Issues event: 1
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 104 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 1
pypi.org: enrichmap

EnrichMap: Spatially informed enrichment analysis for functional interpretation of spatial transcriptomics

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 104 Last month
Rankings
Dependent packages count: 9.1%
Average: 30.1%
Dependent repos count: 51.1%
Maintainers (1)
cenkcelik
Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi
  • ipykernel *
  • myst-nb *
  • sphinx ==7.1.2
  • sphinx-book-theme *
pyproject.toml pypi
  • adjustText *
  • dask ==2024.11.2
  • matplotlib *
  • numpy *
  • pygam *
  • scanpy *
  • scikit-gstat *
  • scikit-learn *
  • scipy *
  • seaborn *
  • splot *
  • squidpy *
  • statannotations *