Last updated: 2025-12-01

Checks: 6 1

Knit directory: Importance-of-markers-for-QTL-detection-by-machine-learning-methods/

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.


The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

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(20221222) 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 27e9067. 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:    .Rproj.user/
    Ignored:    analysis/figure/
    Ignored:    output/imp.tot.RData
    Ignored:    output/mod.rda

Untracked files:
    Untracked:  analysis/consolidated_analysis.Rmd
    Untracked:  analysis/genomic_prediction.Rmd
    Untracked:  analysis/models_gapit_gwas.Rmd
    Untracked:  output/DP_heatmap.png
    Untracked:  output/F1_Score_heatmap.png
    Untracked:  output/FP_heatmap.png
    Untracked:  output/Precision_heatmap.png
    Untracked:  output/Specificity_heatmap.png
    Untracked:  output/acerto_erro_resumo.xlsx
    Untracked:  output/bagging/
    Untracked:  output/boosting/
    Untracked:  output/dt/
    Untracked:  output/gwas_cv/
    Untracked:  output/gwas_multimodel/
    Untracked:  output/mars/
    Untracked:  output/rf/

Unstaged changes:
    Modified:   analysis/about.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/license.Rmd
    Deleted:    data/imp/imp.BAG.RData
    Deleted:    data/imp/imp.BO.RData
    Deleted:    data/imp/imp.DT.RData
    Deleted:    data/imp/imp.G-BLUP.RData
    Deleted:    data/imp/imp.MARS1.RData
    Deleted:    data/imp/imp.MARS2.RData
    Deleted:    data/imp/imp.MARS3.RData
    Deleted:    data/imp/imp.MLP.RData
    Deleted:    data/imp/imp.RBF.RData
    Deleted:    data/imp/imp.RF.RData
    Modified:   output/acerto_erro.xlsx

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/index.Rmd) and HTML (docs/index.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
html ab4a546 WevertonGomesCosta 2025-10-29 update index.html
Rmd efd3fc0 WevertonGomesCosta 2025-10-29 update index.rmd
Rmd e798e38 WevertonGomesCosta 2025-07-29 update index .rmd and .html
html e798e38 WevertonGomesCosta 2025-07-29 update index .rmd and .html
html 471e9e3 WevertonGomesCosta 2025-03-25 add index.html
Rmd 2d83433 WevertonGomesCosta 2025-03-25 update index.Rmd
Rmd 6de3e22 WevertonGomesCosta 2025-03-25 Sua mensagem de commit explicando as alterações
html e044f37 WevertonGomesCosta 2025-03-25 add index.html
Rmd 081eb18 WevertonGomesCosta 2022-12-22 Start workflowr project.

Português

Detecção de QTLs via Machine Learning e GWAS Multi-Modelo

Bem-vindo ao repositório oficial deste projeto de pesquisa. Este estudo visa realizar um benchmark robusto entre métodos estatísticos avançados e inteligência computacional para a identificação de marcadores moleculares em características complexas.

🎯 Objetivos do Projeto

  • Pipeline Computacional: Implementação de um fluxo reprodutível integrando GWAS e ML.
  • Benchmark de Algoritmos: Comparação entre Machine Learning (Random Forest, Bagging, Boosting, Decision Tree, MARS) e GWAS Moderno (FarmCPU, BLINK, SUPER, MLMM).
  • Métricas de Avaliação: Análise rigorosa de Poder de Detecção, Precisão, Falsos Positivos e Estabilidade (via Validação Cruzada).
  • Visualização: Mapas de calor (Heatmaps) e gráficos interativos para interpretação biológica.

🏫 Sobre o LICAE

Este projeto foi desenvolvido no âmbito do Laboratório de Inteligência Computacional e Aprendizado Estatístico (LICAE) da Universidade Federal de Viçosa (UFV), especializado em aplicações avançadas de inteligência computacional para desafios genômicos.

💻 Como Reproduzir

  1. Clonar o Repositório: git clone https://github.com/WevertonGomesCosta/Importance-of-markers-for-QTL-detection-by-machine-learning-methods.git
  2. Abrir o Projeto no RStudio: Abra o arquivo .Rproj.
  3. Instalar Dependências: renv::restore()
  4. Compilar o Site: workflowr::wflow_build()

English

QTL Detection via Machine Learning and Multi-Model GWAS

Welcome to the official repository for this research project. This study aims to perform a robust benchmark between advanced statistical methods and computational intelligence for identifying molecular markers in complex traits.

🎯 Project Objectives

  • Computational Pipeline: Implementation of a reproducible workflow integrating GWAS and ML.
  • Algorithm Benchmark: Comparison between Machine Learning (Random Forest, Bagging, Boosting, Decision Tree, MARS) and Modern GWAS (FarmCPU, BLINK, SUPER, MLMM).
  • Evaluation Metrics: Rigorous analysis of Detection Power, Precision, False Positive Rate, and Stability (via Cross-Validation).
  • Visualization: Heatmaps and interactive plots for biological interpretation.

🚀 Quick Navigation

Use the top menu or the links below to access the results:

🏫 About LICAE

This project was developed at the Computational Intelligence and Statistical Learning Laboratory (LICAE) of the Federal University of Viçosa (UFV), specializing in advanced computational intelligence applications to complex genomic problems.

💻 How to Reproduce

  1. Clone the Repository: git clone https://github.com/WevertonGomesCosta/Importance-of-markers-for-QTL-detection-by-machine-learning-methods.git
  2. Open Project in RStudio: Open the .Rproj file.
  3. Install Dependencies: renv::restore()
  4. Build the Website: workflowr::wflow_build()

Contact

Project Coordinator: Weverton Gomes da Costa
Post-Doctoral Researcher - Statistics Department - UFV
weverton.costa@ufv.br

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


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_0.54         stringi_1.8.7     otel_0.2.0        promises_1.5.0   
 [9] jsonlite_2.0.0    workflowr_1.7.2   glue_1.8.0        rprojroot_2.1.1  
[13] git2r_0.36.2      htmltools_0.5.8.1 httpuv_1.6.16     sass_0.4.10      
[17] rmarkdown_2.30    evaluate_1.0.5    jquerylib_0.1.4   tibble_3.3.0     
[21] fastmap_1.2.0     yaml_2.3.10       lifecycle_1.0.4   whisker_0.4.1    
[25] stringr_1.6.0     compiler_4.5.1    fs_1.6.6          Rcpp_1.1.0       
[29] pkgconfig_2.0.3   rstudioapi_0.17.1 later_1.4.4       digest_0.6.39    
[33] R6_2.6.1          pillar_1.11.1     magrittr_2.0.4    bslib_0.9.0      
[37] tools_4.5.1       cachem_1.1.0