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

proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry

View ORCID ProfileConstantin Ahlmann-Eltze, View ORCID ProfileSimon Anders
doi: https://doi.org/10.1101/661496
Constantin Ahlmann-Eltze
1Center for Molecular Biology, University of Heidelberg, Germany
2Genome Biology Unit, European Laboratory for Molecular Biology (EMBL), Heidelberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Constantin Ahlmann-Eltze
Simon Anders
1Center for Molecular Biology, University of Heidelberg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Simon Anders
  • For correspondence: sanders@fs.tum.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Protein mass spectrometry with label-free quantification (LFQ) is widely used for quantitative proteomics studies. Nevertheless, well-principled statistical inference procedures are still lacking, and most practitioners adopt methods from transcriptomics. These, however, cannot properly treat the principal complication of label-free proteomics, namely many non-randomly missing values.

We present proDA, a method to perform statistical tests for differential abundance of proteins. It models missing values in an intensity-dependent probabilistic manner. proDA is based on linear models and thus suitable for complex experimental designs, and boosts statistical power for small sample sizes by using variance moderation. We show that the currently widely used methods based on ad hoc imputation schemes can report excessive false positives, and that proDA not only overcomes this serious issue but also offers high sensitivity. Thus, proDA fills a crucial gap in the toolbox of quantitative proteomics.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Differences to first version: - Expanded benchmarks and comparisons to other methods. - Expanded description of mathematical methods. There are no changes to the method itself.

  • https://github.com/const-ae/proDA

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-ND 4.0 International license.
Back to top
PreviousNext
Posted May 01, 2020.
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.
proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry
(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
proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry
Constantin Ahlmann-Eltze, Simon Anders
bioRxiv 661496; doi: https://doi.org/10.1101/661496
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry
Constantin Ahlmann-Eltze, Simon Anders
bioRxiv 661496; doi: https://doi.org/10.1101/661496

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 (2633)
  • Biochemistry (5221)
  • Bioengineering (3643)
  • Bioinformatics (15711)
  • Biophysics (7213)
  • Cancer Biology (5593)
  • Cell Biology (8045)
  • Clinical Trials (138)
  • Developmental Biology (4735)
  • Ecology (7462)
  • Epidemiology (2059)
  • Evolutionary Biology (10520)
  • Genetics (7698)
  • Genomics (10082)
  • Immunology (5148)
  • Microbiology (13823)
  • Molecular Biology (5354)
  • Neuroscience (30577)
  • Paleontology (211)
  • Pathology (871)
  • Pharmacology and Toxicology (1519)
  • Physiology (2234)
  • Plant Biology (4983)
  • Scientific Communication and Education (1036)
  • Synthetic Biology (1379)
  • Systems Biology (4130)
  • Zoology (803)