scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data

Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection. In this study, we propose a Gaussian mixture model-based variational...

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Bibliographic Details
Published in:	Machine learning: science and technology Vol. 4; no. 3; pp. 35013 - 35024
Main Authors:	Lin, Eric, Liu, Boyuan, Lac, Leann, Fung, Daryl L X, Leung, Carson K, Hu, Pingzhao
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01.09.2023
Subjects:	Algorithms cell type Cluster analysis Clustering Deep learning Gaussian mixture model Gene sequencing Graph neural networks Machine learning Medical research model integration Parameter estimation Probabilistic models Ribonucleic acid RNA scGMM-VGAE single cell RNA-seq variational graph autoencoder
ISSN:	2632-2153, 2632-2153
Online Access:	Get full text
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