from setuptools import Command, find_packages, setup

__lib_name__ = "SpatialGlue"

__lib_version__ = "1.1.5"

__description__ = "Deciphering spatial domains from spatial multi-omics with SpatialGlue"

__url__ = "https://github.com/JinmiaoChenLab/SpatialGlue"

__author__ = "Yahui Long"

__author_email__ = "longyh@immunol.a-star.edu.sg"

__license__ = "MIT"

__keywords__ = ["Spatial multi-omics", "Cross-omics integration", "Deep learning", "Graph neural networks", "Dual attention"]

__requires__ = ["requests",]

with open("README.rst", "r", encoding="utf-8") as f:

    __long_description__ = f.read()

setup(

    name = __lib_name__,

    version = __lib_version__,

    description = __description__,

    url = __url__,

    author = __author__,

    author_email = __author_email__,

    license = __license__,

    packages = ["SpatialGlue"],

    install_requires = __requires__,

    zip_safe = False,

    include_package_data = True,

    long_description = """Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. Here, we introduce SpatialGlue, a novel graph neural network with dual-attention mechanism, to decipher spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrate that SpatialGlue can more accurately resolve spatial domains at a higher resolution across different tissue types and technology platforms, to enable biological insights into cross-modality spatial correlations. SpatialGlue is computation resource efficient and can be applied for data from various spatial multi-omics technological platforms, including Spatial-epigenome-transcriptome, Stereo-CITE-seq, SPOTS, and 10x Visium. Next, we will extend SpatialGlue to more platforms, such as 10x Genomics Xenium and Nanostring CosMx. """,

    long_description_content_type="text/markdown"

)