SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data

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.

Citing scimap

Nirmal et al., (2024). SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data. Journal of Open Source Software, 9(97), 6604, https://doi.org/10.21105/joss.06604

Installation

We strongly recommend installing scimap in a fresh virtual environment.

# If you have conda installed
conda create --name scimap python=3.10
conda activate scimap

Install scimap directly into an activated virtual environment:

Firstly, we suggest installing scimap and napari together to enable visualization out of the box. Keep in mind, napari needs a GUI toolkit, such as PyQt. If you run into any issues because of your computer's operating system, install scimap and napari separately by following the guidance in napari's documentation.

Here's how you can install both using pip:

pip install "scimap[napari]"

If you encounter a problem with PyQt6 during the installation, you can install scimap alone first. Later on, if you find you need napari, you can go ahead and install it by itself.

To install just scimap:

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 was led by Ajit Johnson Nirmal, Harvard Medical School.
Check out other tools from the Nirmal Lab.

Contibute

Interested in contributing to the package? Check out our guidelines at https://scimap.xyz/contribute/ for detailed instructions.

Funding

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