Metadata-Version: 2.1
Name: sodirac
Version: 0.1.4
Summary: Domain Invariant Representation through Adversarial Calibration (DIRAC), a graph neural network to integrate spatial multi-omic data into a unified domain 
Home-page: https://github.com/EsdenRun/DIRAC
Author: XU CHANG
Author-email: changxu@nus.edu.sg
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scanpy==1.10.3
Requires-Dist: igraph==0.11.8
Requires-Dist: leidenalg

# DIRAC (Domain Invariant Respresentation through Adversatial Calibration)

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## Spatially resolved integration of multi-omics with DIRAC highlights cell-specific remodeling

DIRAC is a Python package, written in [PyTorch](https://pytorch.org/) and based on [Scanpy](https://scanpy.readthedocs.io/en/stable/).

DIRAC is a graph neural network to integrate spatial multi-omic data into a unified domain-invariant embedding space and to automate cell-type annotation by transferring labels from reference spatial or single-cell multi-omic data.

DIRAC primarily includes two integration paradigms: vertical integration and horizontal integration, which differ in their selection of anchors. In vertical integration, multiple data modalities from the same cells are jointly analyzed, using cell correspondences in single-cell data or spot correspondences in spatial data as anchors for alignment. In horizontal integration, the same data modality from distinct groups of cells is aligned using genomic features as anchors. The best way to familiarize yourself with DIRAC is to check out [our tutorial](https://github.com/EsdenRun/DIRAC/tree/main/docs/source/notebooks) and [our documentation](https://dirac-tutorial.readthedocs.io/en/latest/).


![Model architecture](https://raw.githubusercontent.com/EsdenRun/DIRAC/main/docs/Figs/Workflow.png)

For more details, please check out our [publication](https://github.com/EsdenRun/DIRAC).

## Directory structure

```
.
├── sodirac                  # Main Python package
├── data                    # Data files
├── evaluation              # Method evaluation pipelines
├── experiments             # Experiments and case studies
├── tests                   # Unit tests for the Python package
├── docs                    # Documentation files
├── custom                  # Customized third-party packages
├── packrat                 # Reproducible R environment via packrat
├── env.yaml                # Reproducible Python environment via conda
├── pyproject.toml          # Python package metadata
├── LICENSE
└── README.md
```

## How to install DIRAC

To install DIRAC, make sure you have [PyTorch](https://pytorch.org/) and [Scanpy](https://scanpy.readthedocs.io/en/stable/) installed. If you need more details on the dependences, look at the `environment.yml` file. 

* set up conda environment for DIRAC
```
    conda env create -f environment.yml
```
* install sodirac from shell:
```
    conda activate dirac-env
    pip install sodirac
```
* To start using DIRAC, import DIRAC in your jupyter notebooks or/and scripts 
```
    import sodirac as sd
```

> Installing within a
> [conda environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
> is recommended.

## Usage

Please checkout the documentations and tutorials at
[dirac.readthedocs.io](https://rundirac.readthedocs.io/en/latest/).
