Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture
Background: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. https ://doi.org/10.1186/s1300 7-016-0154-2; Plant Methods 13(62):1–29, 2017b. ht...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | México |
| Institución: | Centro Internacional de Mejoramiento de Maíz y Trigo |
| Repositorio: | Repositorio Institucional de Publicaciones Multimedia del CIMMYT |
| OAI Identifier: | oai:repository.cimmyt.org:10883/19576 |
| Acceso en línea: | https://hdl.handle.net/10883/19576 |
| Access Level: | acceso abierto |
| Palabra clave: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Hyperspectral Data Functional Regression Analysis Bayesian Functional Regression Functional Data Bayesian Ridge Regression DATA ANALYSIS REGRESSION ANALYSIS STATISTICAL METHODS BAYESIAN THEORY |
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Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| title |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| spellingShingle |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture Montesinos-López, A. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Hyperspectral Data Functional Regression Analysis Bayesian Functional Regression Functional Data Bayesian Ridge Regression DATA ANALYSIS REGRESSION ANALYSIS STATISTICAL METHODS BAYESIAN THEORY |
| title_short |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| title_full |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| title_fullStr |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| title_full_unstemmed |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| title_sort |
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture |
| dc.creator.none.fl_str_mv |
Montesinos-López, A. Montesinos-Lopez, O.A. De Los Campos, G. Crossa, J. Burgueño, J. Luna Vázquez, F.J. |
| author |
Montesinos-López, A. |
| author_facet |
Montesinos-López, A. Montesinos-Lopez, O.A. De Los Campos, G. Crossa, J. Burgueño, J. Luna Vázquez, F.J. |
| author_role |
author |
| author2 |
Montesinos-Lopez, O.A. De Los Campos, G. Crossa, J. Burgueño, J. Luna Vázquez, F.J. |
| author2_role |
author author author author author |
| dc.subject.none.fl_str_mv |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Hyperspectral Data Functional Regression Analysis Bayesian Functional Regression Functional Data Bayesian Ridge Regression DATA ANALYSIS REGRESSION ANALYSIS STATISTICAL METHODS BAYESIAN THEORY |
| topic |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Hyperspectral Data Functional Regression Analysis Bayesian Functional Regression Functional Data Bayesian Ridge Regression DATA ANALYSIS REGRESSION ANALYSIS STATISTICAL METHODS BAYESIAN THEORY |
| description |
Background: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. https ://doi.org/10.1186/s1300 7-016-0154-2; Plant Methods 13(62):1–29, 2017b. https ://doi.org/10.1186/s1300 7-017-0212- 4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis. Results: We used seven model-methods, one with the conventional model (M1), three methods using the B-splines model (M2, M4, and M6) and three methods using the Fourier basis model (M3, M5, and M7). The data set we used comprises 976 wheat lines under irrigated environments with 250 wavelengths. Under a Bayesian Ridge Regression (BRR), we compared the prediction accuracy of the model-methods proposed under different numbers of basis functions, and compared the implementation time (in seconds) of the seven proposed model-methods for different numbers of basis. Our results as well as previously analyzed data (Montesinos-López et al. 2017a, 2017b) support that around 23 basis functions are enough. Concerning the degree of the polynomial in the context of B-splines, degree 3 approximates most of the curves very well. Two satisfactory types of basis are the Fourier basis for period curves and the B-splines model for non-periodic curves. Under nine different basis, the seven method-models showed similar prediction accuracy. Regarding implementation time, results show that the lower the number of basis, the lower the implementation time required. Methods M2, M3, M6 and M7 were around 3.4 times faster than methods M1, M4 and M5. Conclusions: In this study, we promote the use of functional regression modeling for analyzing high-throughput phenotypic data and indicate the advantages and disadvantages of its implementation. In addition, many key elements that are needed to understand and implement this statistical technique appropriately are provided using a real data set. We provide details for implementing Bayesian functional regression using the developed genomic functional regression (GFR) package. In summary, we believe this paper is a good guide for breeders and scientists interested in using functional regression models for implementing prediction models when their data are curves. Keywords: Hyperspectral data, Functional regression analysis, Bayesian functional regression, Functional data, Bayesian Ridge Regression. |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018-08-16T19:10:19Z 2018-08-16T19:10:19Z 2018 |
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info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
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article |
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publishedVersion |
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https://hdl.handle.net/10883/19576 10.1186/s13007-018-0314-7 |
| url |
https://hdl.handle.net/10883/19576 |
| identifier_str_mv |
10.1186/s13007-018-0314-7 |
| dc.language.none.fl_str_mv |
English |
| language_invalid_str_mv |
English |
| dc.relation.none.fl_str_mv |
https://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm |
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Open Access info:eu-repo/semantics/openAccess |
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Open Access |
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openAccess |
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PDF application/pdf |
| dc.publisher.none.fl_str_mv |
BioMed Central |
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BioMed Central |
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14 Plant Methods 46 reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYT instname:Centro Internacional de Mejoramiento de Maíz y Trigo instacron:CIMMYT |
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Centro Internacional de Mejoramiento de Maíz y Trigo |
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CIMMYT |
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CIMMYT |
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Repositorio Institucional de Publicaciones Multimedia del CIMMYT |
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Repositorio Institucional de Publicaciones Multimedia del CIMMYT |
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1858175771274641408 |
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Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agricultureMontesinos-López, A.Montesinos-Lopez, O.A.De Los Campos, G.Crossa, J.Burgueño, J.Luna Vázquez, F.J.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYHyperspectral DataFunctional Regression AnalysisBayesian Functional RegressionFunctional DataBayesian Ridge RegressionDATA ANALYSISREGRESSION ANALYSISSTATISTICAL METHODSBAYESIAN THEORYBackground: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. https ://doi.org/10.1186/s1300 7-016-0154-2; Plant Methods 13(62):1–29, 2017b. https ://doi.org/10.1186/s1300 7-017-0212- 4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis. Results: We used seven model-methods, one with the conventional model (M1), three methods using the B-splines model (M2, M4, and M6) and three methods using the Fourier basis model (M3, M5, and M7). The data set we used comprises 976 wheat lines under irrigated environments with 250 wavelengths. Under a Bayesian Ridge Regression (BRR), we compared the prediction accuracy of the model-methods proposed under different numbers of basis functions, and compared the implementation time (in seconds) of the seven proposed model-methods for different numbers of basis. Our results as well as previously analyzed data (Montesinos-López et al. 2017a, 2017b) support that around 23 basis functions are enough. Concerning the degree of the polynomial in the context of B-splines, degree 3 approximates most of the curves very well. Two satisfactory types of basis are the Fourier basis for period curves and the B-splines model for non-periodic curves. Under nine different basis, the seven method-models showed similar prediction accuracy. Regarding implementation time, results show that the lower the number of basis, the lower the implementation time required. Methods M2, M3, M6 and M7 were around 3.4 times faster than methods M1, M4 and M5. Conclusions: In this study, we promote the use of functional regression modeling for analyzing high-throughput phenotypic data and indicate the advantages and disadvantages of its implementation. In addition, many key elements that are needed to understand and implement this statistical technique appropriately are provided using a real data set. We provide details for implementing Bayesian functional regression using the developed genomic functional regression (GFR) package. In summary, we believe this paper is a good guide for breeders and scientists interested in using functional regression models for implementing prediction models when their data are curves. Keywords: Hyperspectral data, Functional regression analysis, Bayesian functional regression, Functional data, Bayesian Ridge Regression.BioMed Central2018-08-16T19:10:19Z2018-08-16T19:10:19Z2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePDFapplication/pdfhttps://hdl.handle.net/10883/1957610.1186/s13007-018-0314-714Plant Methods46reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishhttps://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRmCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.Open Accessinfo:eu-repo/semantics/openAccessoai:repository.cimmyt.org:10883/195762024-10-11T19:55:17Z |
| score |
15.811543 |