Back to Search
Start Over
scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder
- Source :
- Briefings in Bioinformatics.
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 2020.
-
Abstract
- The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides strong technical support for accurate and efficient analyzing single-cell gene expression data. However, the analysis of scRNA-Seq is accompanied by many obstacles, including dropout events and the curse of dimensionality. Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA). Specifically, scGMAI utilizes autoencoder networks to reconstruct gene expression values from scRNA-Seq data and FastICA is used to reduce the dimensions of reconstructed data. The integration of these computational techniques in scGMAI leads to outperforming results compared to existing tools, including Seurat, in clustering cells from 17 public scRNA-Seq datasets. In summary, scGMAI is an effective tool for accurately clustering and identifying cell types from scRNA-Seq data and shows the great potential of its applicative power in scRNA-Seq data analysis. The source code is available at https://github.com/QUST-AIBBDRC/scGMAI/.
- Subjects :
- Computer science
Gaussian
0206 medical engineering
02 engineering and technology
03 medical and health sciences
symbols.namesake
RNA-Seq
Cluster analysis
Molecular Biology
Dropout (neural networks)
030304 developmental biology
0303 health sciences
business.industry
Pattern recognition
Mixture model
Autoencoder
Independent component analysis
symbols
FastICA
Artificial intelligence
Single-Cell Analysis
business
Algorithms
Software
020602 bioinformatics
Information Systems
Curse of dimensionality
Subjects
Details
- ISSN :
- 14774054 and 14675463
- Database :
- OpenAIRE
- Journal :
- Briefings in Bioinformatics
- Accession number :
- edsair.doi.dedup.....424e93ee10d1f11e45e86b99b5831123