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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Vydané v:	BMC bioinformatics Ročník 21; číslo 1; s. 64 - 11
Hlavní autori:	Lin, Eugene, Mukherjee, Sudipto, Kannan, Sreeram
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 21.02.2020 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	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
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