Principal balances of compositional data for regression and classification using partial least squares

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F23%3A73622796" target="_blank" >RIV/61989592:15310/23:73622796 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15110/23:73622796 RIV/62690094:18450/23:50020789 RIV/00098892:_____/23:10158301

Výsledek na webu

<a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3518" target="_blank" >https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3518</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cem.3518" target="_blank" >10.1002/cem.3518</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Principal balances of compositional data for regression and classification using partial least squares

Popis výsledku v původním jazyce

High-dimensional compositional data are commonplace in the modern omics sciences, among others. Analysis of compositional data requires the proper choice of a log-ratio coordinate representation, since their relative nature is not compatible with the direct use of standard statistical methods. Principal balances, a particular class of orthonormal log-ratio coordinates, are well suited to this context as they are constructed so that the first few coordinates capture most of the compositional variability of data set. Focusing on regression and classification problems in high dimensions, we propose a novel partial least squares (PLS) procedure to construct principal balances that maximize the explained variability of the response variable and notably ease interpretability when compared to the ordinary PLS formulation. The proposed PLS principal balance approach can be understood as a generalized version of common log contrast models since, instead of just one, multiple orthonormal log-contrasts are estimated simultaneously. We demonstrate the performance of the proposed method using both simulated and empirical data sets.

Název v anglickém jazyce

Principal balances of compositional data for regression and classification using partial least squares

Popis výsledku anglicky

High-dimensional compositional data are commonplace in the modern omics sciences, among others. Analysis of compositional data requires the proper choice of a log-ratio coordinate representation, since their relative nature is not compatible with the direct use of standard statistical methods. Principal balances, a particular class of orthonormal log-ratio coordinates, are well suited to this context as they are constructed so that the first few coordinates capture most of the compositional variability of data set. Focusing on regression and classification problems in high dimensions, we propose a novel partial least squares (PLS) procedure to construct principal balances that maximize the explained variability of the response variable and notably ease interpretability when compared to the ordinary PLS formulation. The proposed PLS principal balance approach can be understood as a generalized version of common log contrast models since, instead of just one, multiple orthonormal log-contrasts are estimated simultaneously. We demonstrate the performance of the proposed method using both simulated and empirical data sets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

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)

Rok uplatnění

2023

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

1099-128X

Svazek periodika

37

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

22

Strana od-do

"e3518-1"-"e3518-22"

Kód UT WoS článku

001114643400005

EID výsledku v databázi Scopus

2-s2.0-85171649327