Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses the anndata framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.


We strongly recommend installing scimap in a fresh virtual environment.

# If you have conda installed
conda create --name scimap python=3.7
conda activate scimap

Install scimap directly into an activated virtual environment:

$ pip install scimap

After installation, the package can be imported as:

$ python
>>> import scimap as sm

Get Started

Detailed documentation of scimap functions and tutorials are available here.

SCIMAP development is led by Ajit Johnson Nirmal at the Laboratory of Systems Pharmacology, Harvard Medical School.


This work is supported by the following NIH grant K99-CA256497

GitHub - labsyspharm/scimap at
Spatial Single-Cell Analysis Toolkit. Contribute to labsyspharm/scimap development by creating an account on GitHub.