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Integrating NIR Spectroscopy & Genomic Data

Bem-vindo! Este repositório reúne código, dados e relatórios associados ao estudo da integração de espectroscopia no infravermelho próximo (NIR) e dados genômicos para aprimorar a predição fenômica, utilizando métodos de kernel para capturar relações não lineares e interações complexas entre genótipo, ambiente e espectro.
O objetivo é avaliar se a fusão dessas fontes de dados pode aumentar a acurácia preditiva e apoiar programas de melhoramento genético mais eficientes.

Pipeline de Análise
Pipeline de Análise

Publicação Associada

Este trabalho faz parte do Projeto de Pesquisa:

  • Processo:
  • Chamada:
  • Período:

Sobre o LICAE

Este projeto foi desenvolvido no âmbito das pesquisas do Laboratório de Inteligência Computacional e Aprendizado Estatístico (LICAE) da Universidade Federal de Viçosa (UFV), especializado em aplicações avançadas de inteligência computacional e modelagem estatística em problemas genômicos complexos.

Recursos Disponíveis

  1. Código de Análise: Scripts R para pré-processamento, modelagem e avaliação.
  2. Dados:
    • NIR: espectros pré-processados de amostras biológicas.
    • Genotípicos: SNPs de genótipos avaliados.
    • Fenotípicos: produtividade e características agronômicas.
    • Ambientais: informações de locais, anos e delineamentos experimentais.
  3. Notebooks: Exemplos interativos de análise exploratória, modelagem preditiva e integração de dados.
  4. Visualizações: Gráficos de variância por componente, acurácia (R²), RMSE e rankings de genótipos.
Arquitetura do Modelo
Arquitetura do Modelo

Como Utilizar

  1. Clone o repositório:

    git clone https://github.com/wevertongomescosta/Integrating-nir-genomic-kernel.git

  2. Instale as dependências:

    renv::restore()

  3. Execute o pipeline principal:

    Rscript scripts/main_analysis.R

Contribuição

Contribuições são bem-vindas mediante:
- Abertura de issues para discussão de melhorias
- Submissão de pull requests para correções críticas
- Sugestões de extensões metodológicas

Licença

Este trabalho está licenciado sob CC BY-NC-SA 4.0.
Para uso comercial ou modificações significativas, contate os autores.

Contato

Coordenador
Moyses Nascimento
Professor Adjunto - Departamento de Estatística - UFV

Bolsista
Weverton Gomes da Costa
Pós-Doutorando - Departamento de Estatística - UFV

Laboratório LICAE:
| https://www.licae.ufv.br/

Integrating NIR Spectroscopy & Genomic Data

Welcome! This repository contains code, data, and reports related to the study of integrating near-infrared reflectance spectroscopy (NIR) and genomic data to improve phenomic prediction, using kernel methods to capture non-linear relationships and complex genotype × environment × spectrum interactions.
The goal is to assess whether merging these data sources can increase predictive accuracy and support more efficient breeding programs.

Analysis Pipeline
Analysis Pipeline

Associated Publication

This work is part of the Research Project:

  • Process:
  • Call:
  • Period:

About LICAE

This project was developed within the research activities of the Computational Intelligence and Statistical Learning Laboratory (LICAE) at the Federal University of Viçosa (UFV), specialized in advanced computational intelligence and statistical modeling for complex genomic problems.

Available Resources

  1. Analysis Code: R scripts for preprocessing, modeling, and evaluation.
  2. Data:
    • NIR: preprocessed spectra from biological samples.
    • Genotypic: SNP data from evaluated genotypes.
    • Phenotypic: yield and agronomic traits.
    • Environmental: location, year, and experimental design information.
  3. Notebooks: Interactive examples of exploratory data analysis, predictive modeling, and data integration.
  4. Visualizations: Variance component plots, accuracy (R²), RMSE, and genotype rankings.
Model Architecture
Model Architecture

How to Use

  1. Clone the repository:

    git clone https://github.com/wevertongomescosta/Integrating-nir-genomic-kernel.git

  2. Install dependencies:

    renv::restore()

  3. Run the main pipeline:

    Rscript scripts/main_analysis.R

Contribution

Contributions are welcome via:
- Opening issues for improvement discussions
- Submitting pull requests for critical fixes
- Suggesting methodological extensions

License

This work is licensed under CC BY-NC-SA 4.0.
For commercial use or significant modifications, please contact the authors.

Contact

Project Coordinator
Moyses Nascimento
Associate Professor – Department of Statistics – UFV

Research Fellow
Weverton Gomes da Costa
Post-Doctoral Researcher – Department of Statistics – UFV

LICAE Laboratory:
| https://www.licae.ufv.br/


sessionInfo()
R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


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_0.52         stringi_1.8.7     promises_1.3.3    jsonlite_2.0.0   
 [9] workflowr_1.7.1   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_1.5.1    
[25] compiler_4.4.1    fs_1.6.6          Rcpp_1.1.0        pkgconfig_2.0.3  
[29] rstudioapi_0.17.1 later_1.4.2       digest_0.6.37     R6_2.6.1         
[33] pillar_1.11.0     magrittr_2.0.3    bslib_0.9.0       tools_4.4.1      
[37] cachem_1.1.0

  1. Weverton Gomes da Costa, Pós-Doutorando, Departamento de Estatística - Universidade Federal de Viçosa, ↩︎