Evidence map›Paper›PMID 31119672›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2019

Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data.

Dominik Reinhold, Harrison Pielke-Lombardo, Sean Jacobson, Debashis Ghosh, Katerina Kechris

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Advances in Microbial NMR Metabolomics.Advances in experimental medicine and biology · 2023
    Article
  11. Review
  12. Article
  13. Tear Metabolomics in Dry Eye Disease: A Review.International journal of molecular sciences · 2019
    Review
  14. 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

5 authors.

Dominik ReinholdPPD, Wilmington, NC, USA.
Harrison Pielke-LombardoComputational Bioscience Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Sean JacobsonCenter for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA.
Debashis GhoshDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Katerina KechrisDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. katerina.kechris@ucdenver.edu.

Funding

NIH Prior Approval Process ProfessionalUL1TR002535 · NCATS · UNIVERSITY OF COLORADO DENVER · PI SOKOL, RONALD J. · 2018 to 2022
$51.1M
Computational Bioscience Program Training GrantT15LM009451 · NLM · UNIVERSITY OF COLORADO DENVER · PI Katherina Kechris-Mays, Arjun Krishnan · 2007 to 2026
$11.7M
Metabolic profiles of COPD phenotypesP20HL113445 · NHLBI · NATIONAL JEWISH HEALTH · PI BOWLER, RUSSELL PAUL · 2012 to 2016
$4.7M
Addressing Sparsity in Metabolomics Data AnalysisU01CA235488 · NCI · UNIVERSITY OF COLORADO DENVER · PI GHOSH, DEBASHIS, KECHRIS-MAYS, KATHERINA · 2018 to 2021
$1.8M
NCATS NIH HHS UL1 TR002535NCI NIH HHS U01 CA235488NHLBI NIH HHS P20 HL113445NLM NIH HHS T15 LM009451
6 · The paper itself

Abstract

Metabolomics is the science of characterizing and quantifying small molecule metabolites in biological systems. These metabolites give organisms their biochemical characteristics, providing a link between genotype, environment, and phenotype. With these opportunities also come data challenges, such as compound annotation, missing values, and batch effects. We present the steps of a general pipeline to process untargeted mass spectrometry data to alleviate the latter two challenges. We assume to have a matrix with metabolite abundances, with metabolites in rows and samples in columns. The steps in the pipeline include summarizing technical replicates (if available), filtering, imputing, transforming, and normalizing the data. In each of these steps, a method and parameters should be chosen based on assumptions one is willing to make, the question of interest, and diagnostic tools. Besides giving a general pipeline that can be adapted by the reader, our goal is to review diagnostic tools and criteria that are helpful when making decisions in each step of the pipeline and assessing the effectiveness of normalization and batch correction. We conclude by giving a list of useful packages and discuss some alternative approaches that might be more appropriate for the reader's data.

Indexed as

Databases, FactualGenotypeHumansMass SpectrometryMetabolomicsPhenotypeFilteringImputationMass spectrometryMetabolomicsNormalizationPre-analyticProcessingTechnical replicatesUntargeted

Identifiers

PMID31119672
PMCPMC7346099

What OpenQuestion holds

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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.