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

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

Abstract 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...

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Bibliographic Details
Main Authors: Eugene Lin, Sudipto Mukherjee, Sreeram Kannan
Format: Article
Language:English
Published: BMC 2020-02-01
Series:BMC Bioinformatics
Subjects:
Adversarial autoencoder
Variational autoencoder
Dimensionality reduction
Generative adversarial networks
Single-cell RNA sequencing
Online Access:http://link.springer.com/article/10.1186/s12859-020-3401-5

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http://link.springer.com/article/10.1186/s12859-020-3401-5

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