Evidence map›Paper›PMID 26078916›Full record

ArticleCurrent Metabolomics

Multivariate Analysis in Metabolomics.

Bradley Worley, Robert Powers

Abstract read
In one paragraph

Article in Current Metabolomics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 648 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
648citing papers in PubMed, 2 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

648 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  15. UPLC-qToF-MSACS omega · 2026
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588 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

2 authors.

Bradley WorleyDepartment of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304.
Robert PowersDepartment of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304.

Funding

EXTRAMURAL RESEARCH FACILITIES CONSTRUCTIONC06RR015468 · NCRR · UNIVERSITY OF NEBRASKA LINCOLN · PI PAUL, PREM S · 2000 to 2000
$2.0M
NCRR NIH HHS C06 RR015468
6 · The paper itself

Abstract

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

Indexed as

metabolomicsmetabonomicsMultivariate analysisOPLS-DAPCAPLS-DA

Identifiers

PMID26078916
PMCPMC4465187

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.