Evidence map›Paper›PMID 29915347›Full record

ArticleScientific reports2018

Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease.

Ling Hao, Jingxin Wang, David Page, Sanjay Asthana, Henrik Zetterberg, Cynthia Carlsson, Ozioma C Okonkwo, Lingjun Li

Abstract readComparative Study
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 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

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

  1. Pooled it
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  18. Simple In VitroInternational journal of molecular sciences · 2023
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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

8 authors.

Ling HaoSchool of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA.ORCID 0000-0002-0106-5266
Jingxin WangBaylor College of Medicine, Houston, TX, USA.
David PageDepartment of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Sanjay AsthanaWisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA.
Henrik ZetterbergClinical Neurochemistry Laboratory, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden.
Cynthia CarlssonWisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA.
Ozioma C OkonkwoWisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA.
Lingjun LiSchool of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA. lingjun.li@wisc.edu.

Funding

Wisconsin Alzheimer's Disease Research CenterP50AG033514 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI JOHNSON, STERLING C · 2009 to 2018
$16.2M
Role of Beta-Catenin in Urinary DysfunctionU54DK104310 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI RICKE, WILLIAM A · 2014 to 2023
$12.8M
Mass Spectrometric Studies of Neuropeptides in FeedingR01DK071801 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI LINGJUN LI · 2006 to 2026
$6.7M
Di-Leu-enabled multiplexed quantitation for biomarker discovery and validation in Alzheimer's diseaseRF1AG052324 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI LI, LINGJUN · 2018 to 2018
$2.4M
DiLeu-enabled multiplexed quantitation for biomarker discovery and validation in Alzheimer’s diseaseR01AG052324 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI LINGJUN LI · 2023 to 2026
$2.3M
Acquisition of a High-Field Dual Source FTICR-MS for Pharmaceutical ResearchS10RR029531 · NCRR · UNIVERSITY OF WISCONSIN-MADISON · PI LI, LINGJUN · 2011 to 2011
$2.1M
Urinary Biomarkers of Lower Urinary Traxct Symptoms (LUTS) in Men P20DK097826 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI BUSHMAN, WADE A · 2012 to 2014
$776k
NCRR NIH HHS S10 RR029531NIA NIH HHS P50 AG033514NIA NIH HHS R01 AG052324NIA NIH HHS RF1 AG052324NIDDK NIH HHS P20 DK097826NIDDK NIH HHS R01 DK071801NIDDK NIH HHS U54 DK104310
6 · The paper itself

Abstract

Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer's disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.

Indexed as

Mass SpectrometryMetabolomicsSoftwareAlzheimer DiseaseBiomarkersCase-Control StudiesHumansMetabolic Networks and PathwaysMetabolomeMiddle AgedMultivariate AnalysisBiomarkers

Identifiers

PMID29915347
PMCPMC6006240

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.