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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Vydané v:	Machine learning: science and technology Ročník 4; číslo 3; s. 35013 - 35024
Hlavní autori:	Lin, Eric, Liu, Boyuan, Lac, Leann, Fung, Daryl L X, Leung, Carson K, Hu, Pingzhao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Bristol IOP Publishing 01.09.2023
Predmet:	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
On-line prístup:	Získať plný text
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