Evidence map›Paper›PMID 38299197›Full record

ReviewGenes & diseases2024

Pretreating and normalizing metabolomics data for statistical analysis.

Jun Sun, Yinglin Xia

Open access · diamondAbstract readReview
In one paragraph

Review in Genes & diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 1 pooled it
17.2field-weighted citation impact, top 1% 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

71 citing papers in PubMed, 1 synthesis or guideline pooled it, 112 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Article
  7. Article
  8. Protein Variability Patterns in Ovarian Serous Carcinoma.International journal of molecular sciences · 2026
    Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Lipidomic profile of meningiomas harboring different NF2 mutation status.Metabolomics : Official journal of the Metabolomic Society · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article

11 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 at 1 institution in 1 country.

Jun SunDivision of Gastroenterology and Hepatology, Department of Medicine, Department of Microbiology/Immunology, UIC Cancer Center, University of Illinois Chicago, Jesse Brown VA Medical Center Chicago (537), Chicago, IL 60612, USA.
Yinglin XiaDivision of Gastroenterology and Hepatology, Department of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA.
University of Illinois Chicago · US

Funding

How Vitamin D Receptor Influences Intestinal BarriersR01DK114126 · NIDDK · UNIVERSITY OF ILLINOIS AT CHICAGO · PI Jun Sun · 2018 to 2026
$3.0M
Vitamin D Receptor Regulation of Microbiota in Intestinal EpitheliaR01DK134343 · NIDDK · UNIVERSITY OF ILLINOIS AT CHICAGO · PI Jun Sun · 2023 to 2026
$3.0M
Vitamin D Receptor Regulation of Microbiota in Intestinal EpitheliaR01DK105118 · NIDDK · UNIVERSITY OF ILLINOIS AT CHICAGO · PI SUN, JUN · 2016 to 2020
$1.8M
BLRD VA I01 BX004824NIDDK NIH HHS R01 DK105118NIDDK NIH HHS R01 DK114126NIDDK NIH HHS R01 DK134343
6 · The paper itself

Abstract

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Indexed as

Data centering and scalingData normalizationData transformationMissing valuesMS-Based data preprocessingNMR Data preprocessingOutliersPreprocessing/pretreatment

Identifiers

PMID38299197
PMCPMC10827599
OpenAlexW4383501045

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.