Metadata-Version: 2.1
Name: CytoBulk
Version: 0.1.7
Summary: Integrating transcriptional data to decipher the tumor microenvironment with the graph frequency domain model
Home-page: https://github.com/kristaxying/CytoBulk
Author: Xueying WANG
Author-email: your_email@example.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: anndata >=0.10.0
Requires-Dist: cellpose >=3.0.10
Requires-Dist: imageio
Requires-Dist: matplotlib
Requires-Dist: numpy >=1.23.0
Requires-Dist: openslide-python
Requires-Dist: ortools ==9.3.10497
Requires-Dist: pandas >=2.2.0
Requires-Dist: Pillow
Requires-Dist: POT ==0.9.5
Requires-Dist: rpy2 >=3.5.0
Requires-Dist: scanpy
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: torch >2.1.0
Requires-Dist: torchvision
Requires-Dist: tqdm
Requires-Dist: requests
Requires-Dist: openslide-bin
Requires-Dist: scikit-image

# CytoBulk
The algorithm for mapping bulk data to spatial HE image.


## Usage

For testing the CytoBulk class, you may refer the test.py file.
- CytoBulk is the major class which contains the full framework.
- graph_deconv offer the function of deconvolution and mapping sc to bulk file.
- image_prediction could predict the cell type and expression from HE image.
- spatial_mapping use spot data and mapped sc data to reconstruct the spot data at single cell resolution. Then will refine the single cell coordinates based on HE cell segmentation results.
- utils give the basic functions.

- to check the doc, run
```bash
pip install mkdocs mkdocs-material
mkdocs serve
```

## 




### Maintainer
WANG Xueying xywang85-c@my.cityu.edu.hk

WANG Yian yianwang5-c@my.cityu.edu.hk
