https://github.com/labsyspharm/scimap
Spatial Single-Cell Analysis Toolkit
https://github.com/labsyspharm/scimap
image-analysis pypi python single-cell-analysis spatial-analysis
Last synced: 16 days ago
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Spatial Single-Cell Analysis Toolkit
- Host: GitHub
- URL: https://github.com/labsyspharm/scimap
- Owner: labsyspharm
- License: mit
- Created: 2020-06-09T20:08:44.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2026-01-06T02:41:33.000Z (about 2 months ago)
- Last Synced: 2026-01-24T08:06:16.981Z (about 1 month ago)
- Topics: image-analysis, pypi, python, single-cell-analysis, spatial-analysis
- Language: Python
- Homepage: https://scimap.xyz/
- Size: 43.6 MB
- Stars: 107
- Watchers: 1
- Forks: 35
- Open Issues: 52
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data
[](https://github.com/labsyspharm/scimap/actions/workflows/build-unix-mac-win.yml)
[](https://github.com/labsyspharm/scimap/actions/workflows/docs.yml)
[](https://pepy.tech/project/scimap)
[](https://pypi.org/project/scimap)
[](https://pypi.org/project/scimap)
[](https://doi.org/10.21105/joss.06604)
*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](https://anndata.readthedocs.io/en/stable/anndata.AnnData.html) 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](https://joss.theoj.org/papers/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:
```python
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`:
```python
pip install scimap
```
After installation, the package can be imported as:
```python
$ python
>>> import scimap as sm
```
## Get Started
#### Detailed documentation of `scimap` functions and tutorials are available [here](http://scimap.xyz/).
*Scimap* development was led by [Ajit Johnson Nirmal](https://ajitjohnson.com/), Harvard Medical School.
Check out other tools from the [Nirmal Lab](https://nirmallab.com/tools/).
## Contibute
Interested in contributing to the package? Check out our guidelines at [https://scimap.xyz/contribute/](https://scimap.xyz/contribute/) for detailed instructions.
## Funding
This work was supported by the following NIH grant K99-CA256497