Metadata-Version: 2.1
Name: scmvae
Version: 1.0.0
Summary: a comprehensive single-cell multimodal analysis python package based on mixed variational autoencoder
Home-page: https://github.com/studentiz/scmvae
Author: studentiz
Author-email: studentiz@live.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.5
Description-Content-Type: text/markdown
Provides-Extra: gpu
Provides-Extra: cpu
License-File: LICENSE.txt


The paired measurement of multiple modalities, known as multimodal analysis, is an exciting frontier for connecting single-cell genomics with epitopes and functions. Mapping of transcriptomes in single-cells and the integration with cell phenotypes enable a better understanding of cellular states. However, assembling these paired omics into a unified representation of the cellular state remains challenging with the unique technical characteristics of each measurement. In this work, we present a python package for single-cell multimodal analysis based on a mixing variational autoencoder that not only joins single-cell transcriptomes and epitopes but also enables simultaneous execution of independent modal analysis.

