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

Understanding sequencing data as compositions: an outlook and review

Thomas P. Quinn, Ionas Erb, Mark F. Richardson, Tamsyn M. Crowley
doi: https://doi.org/10.1101/206425
Thomas P. Quinn
1Bioinformatics Core Research Group, Deakin University, Geelong, 3220, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ionas Erb
2Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
3Universitat Pompeu Fabra (UPF), Barcelona, Spain
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mark F. Richardson
1Bioinformatics Core Research Group, Deakin University, Geelong, 3220, Australia
4Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University Geelong, 3220, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tamsyn M. Crowley
1Bioinformatics Core Research Group, Deakin University, Geelong, 3220, Australia
5Poultry Hub Australia, University of New England, Armidale, New South Wales, 2351, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Motivation Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g., gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e., library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that renders invalid many conventional analyses, including distance measures, correlation coefficients, and multivariate statistical models.

Results The purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study.

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-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted October 19, 2017.
Download PDF
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.
Understanding sequencing data as compositions: an outlook and review
(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
Understanding sequencing data as compositions: an outlook and review
Thomas P. Quinn, Ionas Erb, Mark F. Richardson, Tamsyn M. Crowley
bioRxiv 206425; doi: https://doi.org/10.1101/206425
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Understanding sequencing data as compositions: an outlook and review
Thomas P. Quinn, Ionas Erb, Mark F. Richardson, Tamsyn M. Crowley
bioRxiv 206425; doi: https://doi.org/10.1101/206425

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 (4658)
  • Biochemistry (10310)
  • Bioengineering (7629)
  • Bioinformatics (26222)
  • Biophysics (13464)
  • Cancer Biology (10638)
  • Cell Biology (15357)
  • Clinical Trials (138)
  • Developmental Biology (8462)
  • Ecology (12771)
  • Epidemiology (2067)
  • Evolutionary Biology (16782)
  • Genetics (11368)
  • Genomics (15421)
  • Immunology (10566)
  • Microbiology (25081)
  • Molecular Biology (10170)
  • Neuroscience (54214)
  • Paleontology (398)
  • Pathology (1659)
  • Pharmacology and Toxicology (2878)
  • Physiology (4321)
  • Plant Biology (9206)
  • Scientific Communication and Education (1582)
  • Synthetic Biology (2543)
  • Systems Biology (6759)
  • Zoology (1455)