SCIMAP


NameSCIMAP JSON
Version 2.2.6 PyPI version JSON
download
home_pagehttps://pypi.org/project/scimap/
SummarySpatial Single-Cell Analysis Toolkit
upload_time2024-11-26 21:11:19
maintainerNone
docs_urlNone
authorAjit Johnson Nirmal
requires_python<3.13,>=3.9
licenseMIT
keywords image analysis multiplex imaging single cell analysis
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data
<br>

[![build: Unix-Mac-Win](https://github.com/ajitjohnson/scimap/actions/workflows/build-unix-mac-win.yml/badge.svg)](https://github.com/ajitjohnson/scimap/actions/workflows/build-unix-mac-win.yml)
[![Docs](https://github.com/ajitjohnson/scimap/actions/workflows/docs.yml/badge.svg)](https://github.com/ajitjohnson/scimap/actions/workflows/docs.yml)
[![Downloads](https://pepy.tech/badge/scimap)](https://pepy.tech/project/scimap)
[![PyPI Version](https://img.shields.io/pypi/v/scimap.svg)](https://pypi.org/project/scimap)
[![PyPI License](https://img.shields.io/pypi/l/scimap.svg)](https://pypi.org/project/scimap)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.06604/status.svg)](https://doi.org/10.21105/joss.06604)

<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](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


            

Raw data

            {
    "_id": null,
    "home_page": "https://pypi.org/project/scimap/",
    "name": "SCIMAP",
    "maintainer": null,
    "docs_url": null,
    "requires_python": "<3.13,>=3.9",
    "maintainer_email": null,
    "keywords": "image analysis, multiplex imaging, single cell analysis",
    "author": "Ajit Johnson Nirmal",
    "author_email": "ajitjohnson.n@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/5d/62/8d3cdcbc3bbb830d80f0de26de06b73df9ca3daece405181fac94de6bbcf/scimap-2.2.6.tar.gz",
    "platform": null,
    "description": "# SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data\n<br>\n\n[![build: Unix-Mac-Win](https://github.com/ajitjohnson/scimap/actions/workflows/build-unix-mac-win.yml/badge.svg)](https://github.com/ajitjohnson/scimap/actions/workflows/build-unix-mac-win.yml)\n[![Docs](https://github.com/ajitjohnson/scimap/actions/workflows/docs.yml/badge.svg)](https://github.com/ajitjohnson/scimap/actions/workflows/docs.yml)\n[![Downloads](https://pepy.tech/badge/scimap)](https://pepy.tech/project/scimap)\n[![PyPI Version](https://img.shields.io/pypi/v/scimap.svg)](https://pypi.org/project/scimap)\n[![PyPI License](https://img.shields.io/pypi/l/scimap.svg)](https://pypi.org/project/scimap)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.06604/status.svg)](https://doi.org/10.21105/joss.06604)\n\n<br>\n\n<img src=\"./docs/assets/scimap_logo.jpg\" style=\"max-width:700px;width:100%\" >\n\n<br> \n\n*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.\n\n## Citing scimap\nNirmal 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#)\n\n## Installation\n\nWe strongly recommend installing `scimap` in a fresh virtual environment.\n\n```\n# If you have conda installed\nconda create --name scimap python=3.10\nconda activate scimap\n```\n\nInstall `scimap` directly into an activated virtual environment:\n  \n**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.**\n\nHere's how you can install both using pip:\n\n```python\npip install \"scimap[napari]\"\n```\n\n**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.**\n\nTo install just `scimap`:\n\n```python\npip install scimap\n```\n\nAfter installation, the package can be imported as:\n\n```python\n$ python\n>>> import scimap as sm\n```\n\n\n## Get Started\n\n#### Detailed documentation of `scimap` functions and tutorials are available [here](http://scimap.xyz/).\n\n*Scimap* development was led by [Ajit Johnson Nirmal](https://ajitjohnson.com/), Harvard Medical School.  \nCheck out other tools from the [Nirmal Lab](https://nirmallab.com/tools/). \n\n\n## Contibute\nInterested in contributing to the package? Check out our guidelines at [https://scimap.xyz/contribute/](https://scimap.xyz/contribute/) for detailed instructions.\n\n\n## Funding\nThis work was supported by the following NIH grant K99-CA256497\n\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "Spatial Single-Cell Analysis Toolkit",
    "version": "2.2.6",
    "project_urls": {
        "Documentation": "https://scimap.xyz",
        "Homepage": "https://pypi.org/project/scimap/",
        "Repository": "https://github.com/labsyspharm/scimap"
    },
    "split_keywords": [
        "image analysis",
        " multiplex imaging",
        " single cell analysis"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "638267e9a297208c8b5a8f9d403e5ce7d86be627b33a3d6983355bbf120bb3e2",
                "md5": "5ae9426d754e03de8b4f8d9cdbe9464e",
                "sha256": "3f997bb8aac957d8d9745f4e2ee123ad45657e61619a769d720bdc659a050165"
            },
            "downloads": -1,
            "filename": "scimap-2.2.6-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "5ae9426d754e03de8b4f8d9cdbe9464e",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": "<3.13,>=3.9",
            "size": 11511939,
            "upload_time": "2024-11-26T21:11:13",
            "upload_time_iso_8601": "2024-11-26T21:11:13.878908Z",
            "url": "https://files.pythonhosted.org/packages/63/82/67e9a297208c8b5a8f9d403e5ce7d86be627b33a3d6983355bbf120bb3e2/scimap-2.2.6-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "5d628d3cdcbc3bbb830d80f0de26de06b73df9ca3daece405181fac94de6bbcf",
                "md5": "2ba8438fdd69ef54d01695920bc2918c",
                "sha256": "a71ec09a84b7b75a1503b04de8171388018540d88775ba1e5a419863af25c9af"
            },
            "downloads": -1,
            "filename": "scimap-2.2.6.tar.gz",
            "has_sig": false,
            "md5_digest": "2ba8438fdd69ef54d01695920bc2918c",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": "<3.13,>=3.9",
            "size": 11455960,
            "upload_time": "2024-11-26T21:11:19",
            "upload_time_iso_8601": "2024-11-26T21:11:19.076509Z",
            "url": "https://files.pythonhosted.org/packages/5d/62/8d3cdcbc3bbb830d80f0de26de06b73df9ca3daece405181fac94de6bbcf/scimap-2.2.6.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-11-26 21:11:19",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "labsyspharm",
    "github_project": "scimap",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "scimap"
}
        
Elapsed time: 8.75822s