Evidence map›Paper›PMID 31382415›Full record

ArticleMetabolites2019

rMSIKeyIon: An Ion Filtering R Package for Untargeted Analysis of Metabolomic LDI-MS Images.

Esteban Del Castillo, Lluc Sementé, Sònia Torres, Pere Ràfols, Noelia Ramírez, Manuela Martins-Green, Manel Santafe, Xavier Correig

Abstract read
In one paragraph

Article in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Software tools, databases and resources in metabolomics: updates from 2018 to 2019.Metabolomics : Official journal of the Metabolomic Society · 2020
    Review
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.

Esteban Del CastilloDepartment of Electronic Engineering, Rovira i Virgili University, IISPV, 43007 Tarragona, Spain.ORCID 0000-0002-1743-656X
Lluc SementéDepartment of Electronic Engineering, Rovira i Virgili University, IISPV, 43007 Tarragona, Spain.
Sònia TorresDepartment of Electronic Engineering, Rovira i Virgili University, IISPV, 43007 Tarragona, Spain.ORCID 0000-0001-8881-0793
Pere RàfolsDepartment of Electronic Engineering, Rovira i Virgili University, IISPV, 43007 Tarragona, Spain. pere.rafols@urv.cat.ORCID 0000-0002-9240-4058
Noelia RamírezDepartment of Electronic Engineering, Rovira i Virgili University, IISPV, 43007 Tarragona, Spain.ORCID 0000-0002-9251-0790
Manuela Martins-GreenDepartment of Molecular, Cell and Systems Biology, University of California, Riverside, CA 92521, USA.
Manel SantafeUnit of Histology and Neurobiology, Department of Basic Medical Sciences, Faculty of Medicine and Health Sciences, Rovira i Virgili University, Carrer St. Llorenç, No. 21, 43201 Reus, Spain.ORCID 0000-0002-5462-5108
Xavier CorreigDepartment of Electronic Engineering, Rovira i Virgili University, IISPV, 43007 Tarragona, Spain.ORCID 0000-0002-6902-3054

Funding

Generalitat de Catalunya 2017-SGR-1119Ministerio de Economía y Competitividad RTI2018-096061-B-I00Ministerio de Economía y Competitividad TEC2015-69076-P
6 · The paper itself

Abstract

Many MALDI-MS imaging experiments make a case versus control studies of different tissue regions in order to highlight significant compounds affected by the variables of study. This is a challenge because the tissue samples to be compared come from different biological entities, and therefore they exhibit high variability. Moreover, the statistical tests available cannot properly compare ion concentrations in two regions of interest (ROIs) within or between images. The high correlation between the ion concentrations due to the existence of different morphological regions in the tissue means that the common statistical tests used in metabolomics experiments cannot be applied. Another difficulty with the reliability of statistical tests is the elevated number of undetected MS ions in a high percentage of pixels. In this study, we report a procedure for discovering the most important ions in the comparison of a pair of ROIs within or between tissue sections. These ROIs were identified by an unsupervised segmentation process, using the popular k-means algorithm. Our ion filtering algorithm aims to find the up or down-regulated ions between two ROIs by using a combination of three parameters: (a) the percentage of pixels in which a particular ion is not detected, (b) the Mann-Whitney U ion concentration test, and (c) the ion concentration fold-change. The undetected MS signals (null peaks) are discarded from the histogram before the calculation of (b) and (c) parameters. With this methodology, we found the important ions between the different segments of a mouse brain tissue sagittal section and determined some lipid compounds (mainly triacylglycerols and phosphatidylcholines) in the liver of mice exposed to thirdhand smoke.

Indexed as

biostatisticsion selection algorithmsmass spectrometry imagingmetabolomics imaging

Identifiers

PMID31382415
PMCPMC6724114

What OpenQuestion holds

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LicenceCC BY
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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.