Evidence map›Paper›PMID 25821719›Full record

ArticleEuPA open proteomics2015

Detecting Significant Changes in Protein Abundance.

Kai Kammers, Robert N Cole, Calvin Tiengwe, Ingo Ruczinski

Abstract read
In one paragraph

Article in EuPA open proteomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 190 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
190citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

190 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

130 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kai KammersDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Robert N ColeMass Spectrometry and Proteomics Core Facility, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Calvin TiengweDepartment of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. ; Department of Microbiology and Immunology, School of Medicine and Biomedical Sciences, University at Bu alo, Bu alo, NY, USA.
Ingo RuczinskiDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Funding

STRUCTURE AND SYNTHESIS OF DNAR01AI058613 · NIAID · JOHNS HOPKINS UNIVERSITY · PI JENSEN, ROBERT E · 2004 to 2013
$7.4M
NIAID NIH HHS R01 AI058613
6 · The paper itself

Abstract

We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labeled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.

Identifiers

PMID25821719
PMCPMC4373093

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.