Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences
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 repr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Plants |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2223-7747/10/2/362 |
| _version_ | 1797396576204750848 |
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| author | Ioannis Spyroglou Jan Skalák Veronika Balakhonova Zuzana Benedikty Alexandros G. Rigas Jan Hejátko |
| author_facet | Ioannis Spyroglou Jan Skalák Veronika Balakhonova Zuzana Benedikty Alexandros G. Rigas Jan Hejátko |
| author_sort | Ioannis Spyroglou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:53:23Z |
| format | Article |
| id | doaj.art-42ddd716f99b4fa19582ed41e4694382 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-09T00:53:23Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-42ddd716f99b4fa19582ed41e46943822023-12-11T17:01:13ZengMDPI AGPlants2223-77472021-02-0110236210.3390/plants10020362Mixed Models as a Tool for Comparing Groups of Time Series in Plant SciencesIoannis Spyroglou0Jan Skalák1Veronika Balakhonova2Zuzana Benedikty3Alexandros G. Rigas4Jan Hejátko5Plant Sciences Core Facility, CEITEC—Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech RepublicFunctional Genomics & Proteomics of Plants, CEITEC—Central European Institute of Technology and National Centre for Biotechnology Research, Faculty of Science, Kamenice 5, 62500 Brno, Czech RepublicFunctional Genomics & Proteomics of Plants, CEITEC—Central European Institute of Technology and National Centre for Biotechnology Research, Faculty of Science, Kamenice 5, 62500 Brno, Czech RepublicPhoton Systems Instruments, (PSI, spol. sr.o.), 66424 Drásov, Czech RepublicDepartment of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, GreeceFunctional Genomics & Proteomics of Plants, CEITEC—Central European Institute of Technology and National Centre for Biotechnology Research, Faculty of Science, Kamenice 5, 62500 Brno, Czech RepublicPlants 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.https://www.mdpi.com/2223-7747/10/2/362<i>Arabidopsis</i>linear mixed modelstime series analysisARIMA |
| spellingShingle | Ioannis Spyroglou Jan Skalák Veronika Balakhonova Zuzana Benedikty Alexandros G. Rigas Jan Hejátko Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences Plants <i>Arabidopsis</i> linear mixed models time series analysis ARIMA |
| title | Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences |
| title_full | Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences |
| title_fullStr | Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences |
| title_full_unstemmed | Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences |
| title_short | Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences |
| title_sort | mixed models as a tool for comparing groups of time series in plant sciences |
| topic | <i>Arabidopsis</i> linear mixed models time series analysis ARIMA |
| url | https://www.mdpi.com/2223-7747/10/2/362 |
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