PLS-DA for compositional data with application to metabolomics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F15%3A33152014" target="_blank" >RIV/61989592:15310/15:33152014 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15110/15:33152014 RIV/00098892:_____/15:#0000906

Výsledek na webu

<a href="http://onlinelibrary.wiley.com/doi/10.1002/cem.2657/pdf" target="_blank" >http://onlinelibrary.wiley.com/doi/10.1002/cem.2657/pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cem.2657" target="_blank" >10.1002/cem.2657</a>

Jazyk výsledku

angličtina

Název v původním jazyce

PLS-DA for compositional data with application to metabolomics

Popis výsledku v původním jazyce

When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS-DA)-the most widely used method in chemometrics for multivariate classification-can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters(metabolites) using a small data set resulting fromtargeted metabolomics, where just a subset of potential markers is selected. The second example-the approach of untargeted metabolomics-was used for the analysis detecting almost 500 met

Název v anglickém jazyce

PLS-DA for compositional data with application to metabolomics

Popis výsledku anglicky

When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS-DA)-the most widely used method in chemometrics for multivariate classification-can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters(metabolites) using a small data set resulting fromtargeted metabolomics, where just a subset of potential markers is selected. The second example-the approach of untargeted metabolomics-was used for the analysis detecting almost 500 met

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Journal of Chemometrics

ISSN

0886-9383

e-ISSN

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Svazek periodika

29

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

21-28

Kód UT WoS článku

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EID výsledku v databázi Scopus

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