Mixed Models as a Tool for Comparing Groups of Time Series in
Plant Sciences

J 2021

Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

SPYROGLOU, Ioannis, Jan SKALÁK, Veronika BALAKHONOVA, Z. BENEDIKTY, A.G. RIGAS et. al.

Základní údaje

Originální název

Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

Autoři

SPYROGLOU, Ioannis (300 Řecko, domácí), Jan SKALÁK (203 Česká republika, domácí), Veronika BALAKHONOVA (643 Rusko, domácí), Z. BENEDIKTY, A.G. RIGAS a Jan HEJÁTKO (203 Česká republika, garant, domácí)

Vydání

PLANTS-BASEL, BASEL, MDPI, 2021, 2223-7747

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10611 Plant sciences, botany

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.658

Kód RIV

RIV/00216224:14740/21:00119682

Organizační jednotka

Středoevropský technologický institut

DOI

http://dx.doi.org/10.3390/plants10020362

UT WoS

000622990500001

Klíčová slova anglicky

Arabidopsis; linear mixed models; time series analysis; ARIMA

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 2. 3. 2022 09:19, Mgr. Pavla Foltynová, Ph.D.

Anotace

V originále

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

Návaznosti

EF16_026/0008446, projekt VaV

Název: Integrace signálu a epigenetické reprogramování pro produktivitu rostlin

GJ19-23108Y, projekt VaV

Název: Vlivy a důsledky interakce cytokininové a etylénové signální dráhy na růst Arabidopsis

Investor: Grantová agentura ČR, Vlivy a důsledky interakce cytokininové a etylénové signální dráhy na růst Arabidopsis

LQ1601, projekt VaV

Název: CEITEC 2020 (Akronym: CEITEC2020)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CEITEC 2020

Citovat

SPYROGLOU, Ioannis, Jan SKALÁK, Veronika BALAKHONOVA, Z. BENEDIKTY, A.G. RIGAS a Jan HEJÁTKO. Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences. PLANTS-BASEL. BASEL: MDPI, 2021, roč. 10, č. 2, s. 362-377. ISSN 2223-7747. Dostupné z: https://dx.doi.org/10.3390/plants10020362.

@article{1838140,
   author = {Spyroglou, Ioannis and Skalák, Jan and Balakhonova, Veronika and Benedikty, Z. and Rigas, A.G. and Hejátko, Jan},
   article_location = {BASEL},
   article_number = {2},
   doi = {http://dx.doi.org/10.3390/plants10020362},
   keywords = {Arabidopsis; linear mixed models; time series analysis; ARIMA},
   language = {eng},
   issn = {2223-7747},
   journal = {PLANTS-BASEL},
   title = {Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences},
   url = {https://www.mdpi.com/2223-7747/10/2/362},
   volume = {10},
   year = {2021}
}

TY  - JOUR
ID  - 1838140
AU  - Spyroglou, Ioannis - Skalák, Jan - Balakhonova, Veronika - Benedikty, Z. - Rigas, A.G. - Hejátko, Jan
PY  - 2021
TI  - Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences
JF  - PLANTS-BASEL
VL  - 10
IS  - 2
SP  - 362
EP  - 362
PB  - MDPI
SN  - 22237747
KW  - Arabidopsis
KW  - linear mixed models
KW  - time series analysis
KW  - ARIMA
UR  - https://www.mdpi.com/2223-7747/10/2/362
N2  - Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.
ER  -

SPYROGLOU, Ioannis, Jan SKALÁK, Veronika BALAKHONOVA, Z. BENEDIKTY, A.G. RIGAS a Jan HEJÁTKO. Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences. \textit{PLANTS-BASEL}. BASEL: MDPI, 2021, roč.~10, č.~2, s.~362-377. ISSN~2223-7747. Dostupné z: https://dx.doi.org/10.3390/plants10020362.

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