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SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data
<br> <br> <img src="./docs/assets/scimap_logo.jpg" style="max-width:700px;width:100%" > <br>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