Last updated: 2025-11-03
Checks: 7 0
Knit directory:
Integrating-nir-genomic-kernel/
This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20250829) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 9a3bfcc. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Untracked files:
Untracked: analysis/analysis.Rmd
Untracked: analysis/climate_data.Rmd
Untracked: analysis/code_NIRS_kernels.R
Untracked: analysis/code_for_GBLUP.R
Untracked: analysis/code_for_GBLUP_pt.R
Untracked: analysis/components_variance.Rmd
Untracked: analysis/process_climate_data.Rmd
Untracked: data/Article_documents/
Untracked: data/GAPIT.Genotype.Numerical.txt
Untracked: data/Maize-NIRS-GBS-main/
Untracked: data/NIR.csv
Untracked: data/Pheno.rds
Untracked: output/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/about.Rmd) and HTML
(docs/about.html) files. If you’ve configured a remote Git
repository (see ?wflow_git_remote), click on the hyperlinks
in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9a3bfcc | WevertonGomesCosta | 2025-11-03 | update about, index and license |
| html | 9a3bfcc | WevertonGomesCosta | 2025-11-03 | update about, index and license |
| Rmd | 870efdc | WevertonGomesCosta | 2025-09-16 | update about, license and index |
| html | 870efdc | WevertonGomesCosta | 2025-09-16 | update about, license and index |
| html | 51754b1 | WevertonGomesCosta | 2025-08-29 | add index, about, and license .rmd and .html |
| Rmd | a2bc83b | WevertonGomesCosta | 2025-08-29 | Start workflowr project. |
Este repositório reúne e documenta os dados, códigos e análises utilizados no estudo “Integrating Near-Infrared Reflectance Spectroscopy and Genomic Data for Improved Phenomic Prediction Using Kernel Methods”, que investiga o potencial da integração de espectroscopia no infravermelho próximo (NIR) e dados genômicos para aprimorar a predição fenômica em programas de melhoramento genético.
O conjunto de dados inclui:
- Espectroscopia NIR: leituras espectrais de amostras
biológicas, abrangendo milhares de bandas no intervalo de 4.000 a 10.000
cm⁻¹, pré-processadas para remoção de ruído e correção de
espalhamento.
- Genótipo: dados de marcadores SNP obtidos por
tecnologias de sequenciamento de alta densidade e devidamente filtrados
e imputados.
- Fenótipo: características agronômicas em múltiplos
ambientes experimentais.
- Ambiente: informações sobre locais, anos,
delineamentos experimentais e repetições.
- Metadados: estrutura experimental detalhada para
permitir análises de variância multiambiente e modelagem de efeitos
aleatórios.
Objetivo: avaliar se a fusão de dados espectrais e genômicos, aliada a métodos de kernel, pode aumentar a acurácia da predição fenotípica, capturando relações não lineares e interações complexas entre genótipo, ambiente e espectro.
Este projeto integra as atividades do LICAE (Laboratório de Inteligência Computacional e Aprendizado Estatístico) da UFV.
This repository compiles and documents the datasets, code, and analyses used in the study “Integrating Near-Infrared Reflectance Spectroscopy and Genomic Data for Improved Phenomic Prediction Using Kernel Methods”, which investigates the potential of combining near-infrared reflectance spectroscopy (NIR) and genomic data to enhance phenomic prediction in plant breeding programs.
The dataset includes:
- NIR Spectroscopy: spectral readings from biological
samples, covering thousands of bands in the 4,000–10,000 cm⁻¹ range,
preprocessed for noise removal and scatter correction.
- Genotype: SNP marker data obtained through
high-density sequencing technologies, filtered and imputed.
- Phenotype: agronomic and yield traits measured across
multiple experimental environments.
- Environment: information on locations, years,
experimental designs, and replications.
- Metadata: detailed experimental structure enabling
multi-environment variance component analysis and random effects
modeling.
Goal: assess whether merging spectral and genomic data, combined with kernel methods, can improve phenotypic prediction accuracy by capturing non-linear relationships and complex genotype × environment × spectrum interactions.
This project is part of the activities at the LICAE Laboratory, UFV.
sessionInfo()R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
[5] LC_TIME=Portuguese_Brazil.utf8
time zone: America/Sao_Paulo
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 cli_3.6.5 knitr_1.50 rlang_1.1.6
[5] xfun_0.53 stringi_1.8.7 promises_1.3.3 jsonlite_2.0.0
[9] workflowr_1.7.2 glue_1.8.0 rprojroot_2.1.1 git2r_0.36.2
[13] htmltools_0.5.8.1 httpuv_1.6.16 sass_0.4.10 rmarkdown_2.29
[17] evaluate_1.0.5 jquerylib_0.1.4 tibble_3.3.0 fastmap_1.2.0
[21] yaml_2.3.10 lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.2
[25] compiler_4.5.1 fs_1.6.6 Rcpp_1.1.0 pkgconfig_2.0.3
[29] rstudioapi_0.17.1 later_1.4.4 digest_0.6.37 R6_2.6.1
[33] pillar_1.11.1 magrittr_2.0.4 bslib_0.9.0 tools_4.5.1
[37] cachem_1.1.0 Weverton Gomes da Costa, Pós-Doutorando, Departamento de Estatística - Universidade Federal de Viçosa, wevertonufv@gmail.com↩︎