Weighted pivot coordinates for partial least squares-based marker discovery in high-throughput compositional data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73610091" target="_blank" >RIV/61989592:15310/21:73610091 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/sam.11514" target="_blank" >https://onlinelibrary.wiley.com/doi/full/10.1002/sam.11514</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/sam.11514" target="_blank" >10.1002/sam.11514</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Weighted pivot coordinates for partial least squares-based marker discovery in high-throughput compositional data

Popis výsledku v původním jazyce

High-throughput data representing large mixtures of chemical or biological signals are ordinarily produced in the molecular sciences. Given a number of samples, partial least squares (PLS) regression is a well-established statistical method to investigate associations between them and any continuous response variables of interest. However, technical artifacts generally make the raw signals not directly comparable between samples. Thus, data normalization is required before any meaningful scientific information can be drawn. This often allows to characterize the processed signals as compositional data where the relevant information is contained in the pairwise log-ratios between the components of the mixture. The (log-ratio) pivot coordinate approach facilitates the aggregation into single variables of the pairwise log-ratios of a component to all the remaining components. This simplifies interpretability and the investigation of their relative importance but, particularly in a high-dimensional context, the aggregated log-ratios can easily mix up information from different underlaying processes. In this context, we propose a weighting strategy for the construction of pivot coordinates for PLS regression which draws on the correlation between response variable and pairwise log-ratios. Using real and simulated data sets, we demonstrate that this proposal enhances the discovery of biological markers in high-throughput compositional data.

Název v anglickém jazyce

Weighted pivot coordinates for partial least squares-based marker discovery in high-throughput compositional data

Popis výsledku anglicky

High-throughput data representing large mixtures of chemical or biological signals are ordinarily produced in the molecular sciences. Given a number of samples, partial least squares (PLS) regression is a well-established statistical method to investigate associations between them and any continuous response variables of interest. However, technical artifacts generally make the raw signals not directly comparable between samples. Thus, data normalization is required before any meaningful scientific information can be drawn. This often allows to characterize the processed signals as compositional data where the relevant information is contained in the pairwise log-ratios between the components of the mixture. The (log-ratio) pivot coordinate approach facilitates the aggregation into single variables of the pairwise log-ratios of a component to all the remaining components. This simplifies interpretability and the investigation of their relative importance but, particularly in a high-dimensional context, the aggregated log-ratios can easily mix up information from different underlaying processes. In this context, we propose a weighting strategy for the construction of pivot coordinates for PLS regression which draws on the correlation between response variable and pairwise log-ratios. Using real and simulated data sets, we demonstrate that this proposal enhances the discovery of biological markers in high-throughput compositional data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA19-07155S" target="_blank" >GA19-07155S: Identifikace regulačních sítí kontrolujících vývoj osemení hrachu pomocí RNA sekvenování, proteinové a metabolomické analýzy.</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Statistical Analysis and Data Mining

ISSN

1932-1864

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

315-330

Kód UT WoS článku

000651867400001

EID výsledku v databázi Scopus

2-s2.0-85106329473