A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis

Background Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data...

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Veröffentlicht in:BMC bioinformatics Jg. 21; H. 1; S. 64 - 11
Hauptverfasser: Lin, Eugene, Mukherjee, Sudipto, Kannan, Sreeram
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 21.02.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Adversarial autoencoder
Algorithms
Analysis
Artificial intelligence
Bioinformatics
Biomedical and Life Sciences
Cluster Analysis
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Dimensionality reduction
Gene sequencing
Generative adversarial networks
Humans
Learning algorithms
Life Sciences
Machine learning
Machine Learning and Artificial Intelligence in Bioinformatics
Machine learning for computational and systems biology
Methodology
Methodology Article
Microarrays
Novels
Ribonucleic acid
RNA
RNA sequencing
RNA-Seq - methods
Sequence analysis
Single-Cell Analysis - methods
Single-cell RNA sequencing
Technology
Variational autoencoder
Dimensionality reduction
Variational autoencoder
Generative adversarial networks
Single-cell RNA sequencing
Adversarial autoencoder
ISSN:1471-2105, 1471-2105
Online-Zugang:Volltext
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Zusammenfassung:Background Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements). Results To overcome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data. Conclusions Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-3401-5