Selective pivot logratio coordinates for partial least squares discriminant analysis modelling with applications in metabolomics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F23%3A73619388" target="_blank" >RIV/61989592:15310/23:73619388 - isvavai.cz</a>

Alternative codes found

RIV/00098892:_____/23:10157879 RIV/61989592:15640/23:73619388 RIV/61989592:15110/23:73619388

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/10.1002/sta4.592" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/sta4.592</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/sta4.592" target="_blank" >10.1002/sta4.592</a>

Result language

angličtina

Original language name

Selective pivot logratio coordinates for partial least squares discriminant analysis modelling with applications in metabolomics

Original language description

Data resulting from high-throughput biological experiments are frequently of relative nature. This implies that the most relevant information is on the shape of the data distribution over the biological features more than on the size of the measurements themselves. One well-established way to acknowledge this in statistical processing is through logratio analysis. In the current work, we introduce selective pivot logratio coordinates as a new type of orthonormal logratio coordinate representation for high-dimensional relative (a.k.a. compositional) data. This proposal is aimed to enhance the identification of biomarkers in the context of binary classification problems, which is a common setting of scientific studies in the field. These logratio coordinates are constructed so that the pivot coordinate representing a certain compositional part aggregates all pairwise logratios of that part to the rest but, unlike in the ordinary formulation, excludes those deviating from the main pattern. This novel coordinate system is embedded within a partial least squares discriminant analysis (PLS-DA) model for its practical application. Based on both synthetic and realworld metabolomic data sets, we demonstrate the enhanced performance of the novel approach when compared with other methods used in the area.

Czech name

—

Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

10406 - Analytical chemistry

Project

<a href="/en/project/NU20-08-00367" target="_blank" >NU20-08-00367: New biomarkers of inherited metabolic diseases</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Stat

ISSN

2049-1573

e-ISSN

2049-1573

Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

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UT code for WoS article

001013196600001

EID of the result in the Scopus database

2-s2.0-85163695944