SCIMAP
SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data - Published in JOSS (2024)
Science Score: 95.0%
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✓DOI references
Found 7 DOI reference(s) in README and JOSS metadata -
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2 of 13 committers (15.4%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Scientific Fields
Repository
Spatial Single-Cell Analysis Toolkit
Basic Info
- Host: GitHub
- Owner: labsyspharm
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://scimap.xyz/
- Size: 43.3 MB
Statistics
- Stars: 98
- Watchers: 2
- Forks: 31
- Open Issues: 47
- Releases: 15
Topics
Metadata Files
README.md
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:
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.
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
Owner
- Name: Laboratory of Systems Pharmacology @ Harvard
- Login: labsyspharm
- Kind: organization
- Email: hits@harvard.edu
- Location: Boston, MA
- Website: https://labsyspharm.org/
- Repositories: 150
- Profile: https://github.com/labsyspharm
Reinventing the fundamental science underlying the development of new medicines and their use in individual patients.
JOSS Publication
SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data
Authors
Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States of America, Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States of America
Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States of America, Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States of America
Editor
Charlotte Soneson
Tags
multiplexed imaging data analysis spatial analysis spatial biologyGitHub Events
Total
- Create event: 1
- Release event: 1
- Issues event: 27
- Watch event: 23
- Issue comment event: 45
- Push event: 63
- Pull request event: 6
- Fork event: 8
Last Year
- Create event: 1
- Release event: 1
- Issues event: 28
- Watch event: 23
- Issue comment event: 45
- Push event: 63
- Pull request event: 6
- Fork event: 8
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Ajit Johnson Nirmal | a****n@g****m | 753 |
| amorje | a****e@g****m | 49 |
| dependabot[bot] | 4****] | 11 |
| Yu-An Chen | a****2@g****m | 6 |
| sarafiller | 8****r | 6 |
| Radkevich | e****h@M****g | 2 |
| tdurieux | d****s@h****m | 1 |
| emmanuel-contreras | e****s@w****u | 1 |
| SarkkinenJ | 9****J | 1 |
| Jeremy Muhlich | j****h@b****g | 1 |
| Charlotte Soneson | c****n@g****m | 1 |
| Artem Sokolov | a****v@g****m | 1 |
| Sara Filler | s****r@s****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 84
- Total pull requests: 64
- Average time to close issues: about 2 months
- Average time to close pull requests: about 1 month
- Total issue authors: 51
- Total pull request authors: 13
- Average comments per issue: 2.04
- Average comments per pull request: 0.41
- Merged pull requests: 42
- Bot issues: 0
- Bot pull requests: 31
Past Year
- Issues: 24
- Pull requests: 5
- Average time to close issues: 13 days
- Average time to close pull requests: about 4 hours
- Issue authors: 17
- Pull request authors: 3
- Average comments per issue: 0.96
- Average comments per pull request: 1.6
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- ajitjohnson (6)
- josenimo (5)
- sailseem (4)
- rach-crc (3)
- kevinyamauchi (3)
- yerahko (3)
- andreevg04 (3)
- bugie19 (3)
- PietroD (3)
- virlyananda (2)
- vraghu90 (2)
- emmanuel-contreras (2)
- tuuliavallius (2)
- batukav (2)
- emir-radkevich (2)
Pull Request Authors
- dependabot[bot] (31)
- amorje (11)
- sarafiller (5)
- Yu-AnChen (4)
- emmanuel-contreras (2)
- emir-radkevich (2)
- SarkkinenJ (2)
- csoneson (2)
- ArtemSokolov (1)
- jmuhlich (1)
- ruiheesi (1)
- adamjtaylor (1)
- tdurieux (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 3,363 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 260
- Total maintainers: 1
pypi.org: scimap
Spatial Single-Cell Analysis Toolkit
- Homepage: https://pypi.org/project/scimap/
- Documentation: https://scimap.xyz
- License: MIT
-
Latest release: 2.3.5
published 6 months ago
Rankings
Maintainers (1)
Dependencies
- 197 dependencies
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- actions/cache v3 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
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- docker/setup-qemu-action v1 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- python 3.8 build