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

Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis

View ORCID ProfileFlorian Wagner, Dalia Barkley, View ORCID ProfileItai Yanai
doi: https://doi.org/10.1101/655365
Florian Wagner
1Institute for Computational Medicine, NYU School of Medicine, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Florian Wagner
  • For correspondence: florian.wagner@nyu.edu
Dalia Barkley
1Institute for Computational Medicine, NYU School of Medicine, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Itai Yanai
1Institute for Computational Medicine, NYU School of Medicine, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Itai Yanai
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Single-cell RNA-Seq measurements are commonly affected by high levels of technical noise, posing challenges for data analysis and visualization. A diverse array of methods has been proposed to computationally remove noise by sharing information across similar cells or genes, however their respective accuracies have been difficult to establish. Here, we propose a simple denoising strategy based on principal component analysis (PCA). We show that while PCA performed on raw data is biased towards highly expressed genes, this bias can be mitigated with a cell aggregation step, allowing the recovery of denoised expression values for both highly and lowly expressed genes. We benchmark our resulting ENHANCE algorithm and three previously described methods on simulated data that closely mimic real datasets, showing that ENHANCE provides the best overall denoising accuracy, recovering modules of co-expressed genes and cell subpopulations. Implementations of our algorithm are available at https://github.com/yanailab/enhance.

Footnotes

  • ↵3 Email: itai.yanai{at}nyulangone.org

  • We have expanded the introduction and discussion, and added a new figure panel with a visual comparison of the results obtained from the different denoising methods.

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 June 17, 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.
Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis
Florian Wagner, Dalia Barkley, Itai Yanai
bioRxiv 655365; doi: https://doi.org/10.1101/655365
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis
Florian Wagner, Dalia Barkley, Itai Yanai
bioRxiv 655365; doi: https://doi.org/10.1101/655365

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 (3698)
  • Biochemistry (7809)
  • Bioengineering (5689)
  • Bioinformatics (21330)
  • Biophysics (10595)
  • Cancer Biology (8199)
  • Cell Biology (11961)
  • Clinical Trials (138)
  • Developmental Biology (6777)
  • Ecology (10419)
  • Epidemiology (2065)
  • Evolutionary Biology (13900)
  • Genetics (9726)
  • Genomics (13094)
  • Immunology (8164)
  • Microbiology (20058)
  • Molecular Biology (7871)
  • Neuroscience (43147)
  • Paleontology (321)
  • Pathology (1280)
  • Pharmacology and Toxicology (2264)
  • Physiology (3362)
  • Plant Biology (7246)
  • Scientific Communication and Education (1315)
  • Synthetic Biology (2010)
  • Systems Biology (5547)
  • Zoology (1132)