Metadata-Version: 2.1
Name: scimap
Version: 0.10.5
Summary: Single-Cell Image Analysis Package
Home-page: https://pypi.org/project/scimap
License: MIT
Keywords: image analysis,multiplex imaging,single cell analysis
Author: Ajit Johnson Nirmal
Author-email: ajitjohnson.n@gmail.com
Requires-Python: >=3.7,<4.0
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: PhenoGraph (>=1.5.7,<2.0.0)
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Requires-Dist: mkdocs (>=1.1.2,<2.0.0)
Requires-Dist: napari (>=0.4.2,<0.5.0)
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Requires-Dist: scanpy (>=1.6.0,<2.0.0)
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Requires-Dist: zarr (>=2.5.0,<3.0.0)
Project-URL: Documentation, https://scimap.readthedocs.io
Project-URL: Repository, https://github.com/ajitjohnson/scimap
Description-Content-Type: text/markdown

# Single-Cell Image Analysis Package

Scimap is a scalable toolkit for analyzing single-cell multiplex imaging data. 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 such as [scanpy](https://scanpy.readthedocs.io/en/latest/#). It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with datasets of more than one million cells.


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## Installation

We strongly recommend installing `scimap` in a fresh virtual environment.

```
# If you have conda installed
conda create --name scimap python=3.7
conda activate scimap
```

Install `scimap` directly into an activated virtual environment:

```python
$ pip install scimap
$ pip install napari[all] # For visualizing images
```

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](https://scimap-doc.readthedocs.io/en/latest/).

