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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↩︎