--- a
+++ b/setup.py
@@ -0,0 +1,30 @@
+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"
+)