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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/GA19-07155S" target="_blank" >GA19-07155S: Identification of regulatory networks controlling pea seed coat development using combination of RNA sequencing, protein and metabolites analysis.</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Name of the periodical

Statistical Analysis and Data Mining

ISSN

1932-1864

e-ISSN

—

Volume of the periodical

14

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

315-330

UT code for WoS article

000651867400001

EID of the result in the Scopus database

2-s2.0-85106329473