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

The accuracy of absolute differential abundance analysis from relative count data

View ORCID ProfileKimberly E. Roche, Sayan Mukherjee
doi: https://doi.org/10.1101/2021.12.06.471397
Kimberly E. Roche
1Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kimberly E. Roche
  • For correspondence: kimberly.roche@duke.edu
Sayan Mukherjee
1Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, United States
2Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, NC 27708, United States; Institute for Computer Science, Universität Leipzig and the Max Planck Institute for Mathematics in the Natural Sciences, Leipzig, 04103, Germany
3Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with median sensitivities (true positive rates) and specificities (1 - false positive rates) each of around 0.90, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data can give useful predictions of discrepancy in differential abundance calling in simulated data and provide useful bounds for worst-case outcomes in real data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Additional methods and reviewer-suggested changes added. These include the addition of 1) a duplicate results set obtained at a higher level of statistical stringency and 2) an brief analysis of the efficacy of "control genes" in normalizing data.

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 May 11, 2022.
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.
The accuracy of absolute differential abundance analysis from relative count data
(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
The accuracy of absolute differential abundance analysis from relative count data
Kimberly E. Roche, Sayan Mukherjee
bioRxiv 2021.12.06.471397; doi: https://doi.org/10.1101/2021.12.06.471397
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
The accuracy of absolute differential abundance analysis from relative count data
Kimberly E. Roche, Sayan Mukherjee
bioRxiv 2021.12.06.471397; doi: https://doi.org/10.1101/2021.12.06.471397

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 (3514)
  • Biochemistry (7365)
  • Bioengineering (5342)
  • Bioinformatics (20318)
  • Biophysics (10041)
  • Cancer Biology (7773)
  • Cell Biology (11348)
  • Clinical Trials (138)
  • Developmental Biology (6450)
  • Ecology (9979)
  • Epidemiology (2065)
  • Evolutionary Biology (13354)
  • Genetics (9370)
  • Genomics (12607)
  • Immunology (7724)
  • Microbiology (19087)
  • Molecular Biology (7459)
  • Neuroscience (41134)
  • Paleontology (300)
  • Pathology (1235)
  • Pharmacology and Toxicology (2142)
  • Physiology (3177)
  • Plant Biology (6878)
  • Scientific Communication and Education (1276)
  • Synthetic Biology (1900)
  • Systems Biology (5328)
  • Zoology (1091)