Metadata-Version: 2.1
Name: COSCST
Version: 0.1
Summary: Single-cell RNA sequencing data excels in providing high sequencing depth and precision at the single-cell level, but lacks spatial information. Simultaneously, spatial transcriptomics technology visualizes gene expression patterns in their spatial context but has low resolution. Here, we present COSCST that combines these two datasets through autoencoder and supervised learning model to map single-cell RNA-seq data with spatial coordination and spatial transcriptomics with precise cell type annotation. 
Home-page: https://github.com/shiy-shiy/SCST/
Author: Yi Shi, Gang Hu
Author-email: shiyi@nankai.mail.edu.cn, huggs@nankai.edu.cn
License: MIT
Download-URL: https://github.com/shiy-shiy/SCST/archive/refs/heads/main.zip
Keywords: single cell,spatial transcriptome
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Dist: matplotlib (>=2.2)
Requires-Dist: tensorflow
Requires-Dist: scanpy
Requires-Dist: louvain
Requires-Dist: python-igraph
Requires-Dist: h5py
Requires-Dist: pandas

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