Evidence map›Paper›PMID 29330539›Full record

ArticleScientific reports2018

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.

Runmin Wei, Jingye Wang, Mingming Su, Erik Jia, Shaoqiu Chen, Tianlu Chen, Yan Ni

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 420 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
420citing 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

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

  1. Pooled it
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  10. Longitudinal untargeted metabolomics of cognitive impairment in Parkinson's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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  13. Gut Microbial Topology and Metabolic Signatures Associated With Colorectal Neoplasia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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360 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

7 authors.

Runmin WeiUniversity of Hawaii Cancer Center, Honolulu, HI, 96813, USA.ORCID http://orcid.org/0000-0001-7707-0160
Jingye WangUniversity of Hawaii Cancer Center, Honolulu, HI, 96813, USA.ORCID http://orcid.org/0000-0003-4626-0003
Mingming SuUniversity of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
Erik JiaPunahou School, Honolulu, HI, 96822, USA.
Shaoqiu ChenUniversity of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
Tianlu ChenShanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China.
Yan NiUniversity of Hawaii Cancer Center, Honolulu, HI, 96813, USA. yni@cc.hawaii.edu.ORCID http://orcid.org/0000-0003-1779-7266

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student's t-test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics ( https://metabolomics.cc.hawaii.edu/software/MetImp/ ).

Indexed as

Cluster AnalysisComputational BiologyLeast-Squares AnalysisMass SpectrometryMetabolomicsPrincipal Component Analysis

Identifiers

PMID29330539
PMCPMC5766532

What OpenQuestion holds

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Registered trials

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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.