Welcome to ARIEL’s documentation!
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We develop a fast, omics-driven sample integration tool called ARIEL, which enables effortless landmark detection, spatial alignment and information transfer for multi-sample spatial transcriptomic data. 
ARIEL can be applied in diverse situation, including cross-individual, cross-platform, cross-resolution, cross-omics, and cross-disease scenarios. 
We provide four demos to demonstrate the application of ARIEL, including DLPFC, Hippocampus, Brain, and Hepatic Lobule.
You can download the demo datasets and code here:

- Dataset (figshare): `ARIEL-data <https://figshare.com/articles/dataset/ARIEL-data/30425062>`_

- Code (GitHub): `shilab-ecnu/ARIEL <https://github.com/shilab-ecnu/ARIEL>`_

The main program of ARIEL can be summarized in the figure:

.. image:: _static/ARIEL_hippo12.png
   :alt:  The main program of ARIEL


.. toctree::
   :maxdepth: 2
   :caption: Contents:

   readme.ipynb

   DLPFC(Alignment).ipynb
   hippocampus.ipynb
   brain.ipynb
   hepatic lobule.ipynb






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