Evidence map›Paper›PMID 26951559›Full record

ReviewJournal of the American Society for Mass Spectrometry2016

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments.

Elizabeth S Hecht, Ann L Oberg, David C Muddiman

Open access · greenAbstract readReview
In one paragraph

Review in Journal of the American Society for Mass Spectrometry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
5.9field-weighted citation impact, top 4% of its field
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

22 citing papers in PubMed, 74 citations in OpenAlex.

  1. Article
  2. Review
  3. Room-temperature mL-to-μL quantitative liquid concentration device for cyclone flow.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2024
    Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Optimization of LC-MSMetabolites · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Article
  20. Article
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

3 authors at 2 institutions in 1 country.

Elizabeth S HechtW. M. Keck FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA.
Ann L ObergDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
David C MuddimanW. M. Keck FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA. dcmuddim@ncsu.edu.
North Carolina State University · USMayo Clinic in Florida · US

Funding

Molecular Biotechnology Training at North Carolina State UniversityT32GM008776 · NIGMS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI KELLY, ROBERT M · 2000 to 2019
$3.9M
DRILL: Droplet Desolvation and Ion Transmission Interface for Mass SpectrometryR01GM112662 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI FEDOROV, ANDREI G, MUDDIMAN, DAVID C. · 2014 to 2017
$2.2M
NIGMS NIH HHS R01 GM112662NIGMS NIH HHS R01 GM112662 02NIGMS NIH HHS T32 GM008776NIGMS NIH HHS T32GM008776
6 · The paper itself

Abstract

Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes of molecules, with its scope rapidly expanding in the areas of post-translational modifications, MS instrumentation, and many others. Yet integration of novel analyte preparatory and purification methods with existing or novel mass spectrometers can introduce new challenges for MS sensitivity. The mechanisms that govern detection by MS are particularly complex and interdependent, including ionization efficiency, ion suppression, and transmission. Performance of both off-line and MS methods can be optimized separately or, when appropriate, simultaneously through statistical designs, broadly referred to as "design of experiments" (DOE). The following review provides a tutorial-like guide into the selection of DOE for MS experiments, the practices for modeling and optimization of response variables, and the available software tools that support DOE implementation in any laboratory. This review comes 3 years after the latest DOE review (Hibbert DB, 2012), which provided a comprehensive overview on the types of designs available and their statistical construction. Since that time, new classes of DOE, such as the definitive screening design, have emerged and new calls have been made for mass spectrometrists to adopt the practice. Rather than exhaustively cover all possible designs, we have highlighted the three most practical DOE classes available to mass spectrometrists. This review further differentiates itself by providing expert recommendations for experimental setup and defining DOE entirely in the context of three case-studies that highlight the utility of different designs to achieve different goals. A step-by-step tutorial is also provided.

Indexed as

Mass SpectrometryModels, StatisticalResearch DesignDesign of experimentsMass spectrometryModelingOptimizationTutorial

Identifiers

PMID26951559
PMCPMC4841694
OpenAlexW2296118565

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.