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

Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq

View ORCID ProfileValentine Svensson, View ORCID ProfileEduardo da Veiga Beltrame, View ORCID ProfileLior Pachter
doi: https://doi.org/10.1101/762773
Valentine Svensson
1Division of Biology and Biological Engineering, California Institute of Technology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Valentine Svensson
Eduardo da Veiga Beltrame
1Division of Biology and Biological Engineering, California Institute of Technology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Eduardo da Veiga Beltrame
Lior Pachter
1Division of Biology and Biological Engineering, California Institute of Technology
2Department of Computing and Mathematical Sciences, California Institute of Technology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lior Pachter
  • For correspondence: lpachter@caltech.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

The allocation of a sequencing budget when designing single cell RNA-seq experiments requires consideration of the tradeoff between number of cells sequenced and the read depth per cell. One approach to the problem is to perform a power analysis for a univariate objective such as differential expression. However, many of the goals of single-cell analysis requires consideration of the multivariate structure of gene expression, such as clustering. We introduce an approach to quantifying the impact of sequencing depth and cell number on the estimation of a multivariate generative model for gene expression that is based on error analysis in the framework of a variational autoencoder. We find that at shallow depths, the marginal benefit of deeper sequencing per cell significantly outweighs the benefit of increased cell numbers. Above about 15,000 reads per cell the benefit of increased sequencing depth is minor. Code for the workflow reproducing the results of the paper is available at https://github.com/pachterlab/SBP_2019/.

Footnotes

  • https://github.com/pachterlab/SBP_2019/

  • https://doi.org/10.22002/d1.1276

Copyright 
The copyright holder has placed this preprint in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors.
Back to top
PreviousNext
Posted September 09, 2019.
Download PDF
Data/Code
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.
Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq
(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
Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq
Valentine Svensson, Eduardo da Veiga Beltrame, Lior Pachter
bioRxiv 762773; doi: https://doi.org/10.1101/762773
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq
Valentine Svensson, Eduardo da Veiga Beltrame, Lior Pachter
bioRxiv 762773; doi: https://doi.org/10.1101/762773

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

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3505)
  • Biochemistry (7346)
  • Bioengineering (5323)
  • Bioinformatics (20263)
  • Biophysics (10016)
  • Cancer Biology (7743)
  • Cell Biology (11300)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9951)
  • Epidemiology (2065)
  • Evolutionary Biology (13322)
  • Genetics (9361)
  • Genomics (12583)
  • Immunology (7701)
  • Microbiology (19021)
  • Molecular Biology (7441)
  • Neuroscience (41036)
  • Paleontology (300)
  • Pathology (1229)
  • Pharmacology and Toxicology (2137)
  • Physiology (3160)
  • Plant Biology (6860)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1896)
  • Systems Biology (5311)
  • Zoology (1089)