scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder

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...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 22; no. 4
Main Authors: Yu, Bin, Chen, Chen, Qi, Ren, Zheng, Ruiqing, Skillman-Lawrence, Patrick J, Wang, Xiaolin, Ma, Anjun, Gu, Haiming
Format: Journal Article
Language:English
Published: Oxford Oxford University Press 01.07.2021
Oxford Publishing Limited (England)
Subjects:
Clustering
Computer applications
Data analysis
Gene expression
Gene sequencing
Independent component analysis
Probabilistic models
Ribonucleic acid
RNA
Source code
Technical services
cell clustering
scRNA-Seq
fast independent component analysis
autoencoder networks
Gaussian mixture model
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary: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/.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbaa316