Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
Confirmatory Results

Single-Cell Data Analysis Using MMD Variational Autoencoder

View ORCID ProfileChao Zhang
doi: https://doi.org/10.1101/613414
Chao Zhang
Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Chao Zhang
  • For correspondence: c.zhang@vu.nl
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Variational Autoencoder (VAE) is a generative model from the computer vision community; it learns a latent representation of the images and generates new images in an unsupervised way. Recently, Vanilla VAE has been applied to analyse single-cell datasets, in the hope of harnessing the representation power of latent space to evade the “curse of dimensionality” of the original dataset. However, some research points out that Vanilla VAE is suffering from the issue of the less informative latent space, which raises a question concerning the reliability of Vanilla VAE latent space in representing the high-dimensional single-cell datasets. Therefore a study is set up to examine this issue from the perspective of bioinformatics.

This paper confirms the issue of Vanilla VAE by comparing it to MMD-VAE, a variant of VAE which has overcome this issue, across a series of mass cytometry and single-cell RNAseq datasets. The result shows MMD-VAE is superior to Vanilla VAE in retaining the information not only in the latent space but also the reconstruction space, which suggests that MMD-VAE be a better option for single-cell data analysis.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted April 18, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Single-Cell Data Analysis Using MMD Variational Autoencoder
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Single-Cell Data Analysis Using MMD Variational Autoencoder
Chao Zhang
bioRxiv 613414; doi: https://doi.org/10.1101/613414
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Single-Cell Data Analysis Using MMD Variational Autoencoder
Chao Zhang
bioRxiv 613414; doi: https://doi.org/10.1101/613414

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (1522)
  • Biochemistry (2475)
  • Bioengineering (1731)
  • Bioinformatics (9655)
  • Biophysics (3892)
  • Cancer Biology (2964)
  • Cell Biology (4185)
  • Clinical Trials (135)
  • Developmental Biology (2622)
  • Ecology (4092)
  • Epidemiology (2031)
  • Evolutionary Biology (6884)
  • Genetics (5202)
  • Genomics (6490)
  • Immunology (2181)
  • Microbiology (6928)
  • Molecular Biology (2750)
  • Neuroscience (17245)
  • Paleontology (126)
  • Pathology (425)
  • Pharmacology and Toxicology (705)
  • Physiology (1055)
  • Plant Biology (2484)
  • Scientific Communication and Education (642)
  • Synthetic Biology (828)
  • Systems Biology (2684)
  • Zoology (429)