Skip to contents

qtl2ggplot Developer Guide Overview & Architecture

Source: vignettes/devel_guide/index.Rmd
index.Rmd

qtl2ggplot Developer Guide Overview & Architecture

Package Purpose & Ecosystem

qtl2ggplot provides ggplot2-based visualization tools for quantitative trait loci (QTL) experiments and genome scans within the R/qtl2 ecosystem. It modernizes and extends graphics routines from qtl2plot, offering flexible customization, faceting, and interactive integration.

Ecosystem relationships:

  • qtl2: Core statistical engine for genome scans, kinship matrices, and genotype probability mapping.
  • qtl2ggplot: Modern ggplot2 visualization layer for genome scans, coefficient tracks, SNP associations, and gene locus annotations.
  • qtl2pattern: Allele pattern support, top SNP filtering, and contrast calculations for R/qtl2.
  • qtl2shiny: Interactive Shiny application leveraging qtl2ggplot for interactive data exploration.

High-Level Architecture & Visual Data Flow

qtl2ggplot maps statistical output objects from qtl2 into structured ggplot2 graphics layers:

flowchart TD
    scan1Obj["qtl2 scan1 Object (LOD scores & map)"]
    scan1coefObj["qtl2 scan1coef Object (founder allele effects)"]
    snpInfoObj["snpinfo Data Frame (SNPs & positions)"]
    genesObj["genes Data Frame (gene annotations & exons)"]

    autoplotScan1["autoplot.scan1() / ggplot_scan1()"]
    autoplotCoef["autoplot.scan1coef() / ggplot_coef()"]
    ggplotSnpasso["ggplot_snpasso()"]
    autoplotGenes["autoplot.genes() / ggplot_genes()"]

    ggplotCoefLod["ggplot_coef_and_lod() (Dual Panel)"]
    ggplotSnpGenes["ggplot_snpasso_and_genes() (Stacked Track)"]
    ggplotPxG["ggplot_pxg() (Phenotype by Genotype)"]

    ggplotOut["ggplot2 Output Object"]

    scan1Obj --> autoplotScan1
    scan1coefObj --> autoplotCoef
    snpInfoObj --> ggplotSnpasso
    genesObj --> autoplotGenes

    autoplotScan1 --> ggplotCoefLod
    autoplotCoef --> ggplotCoefLod
    ggplotSnpasso --> ggplotSnpGenes
    autoplotGenes --> ggplotSnpGenes

    autoplotScan1 --> ggplotOut
    autoplotCoef --> ggplotOut
    ggplotCoefLod --> ggplotOut
    ggplotSnpGenes --> ggplotOut
    ggplotPxG --> ggplotOut

    classDef input fill:#1f77b4,stroke:#333,stroke-width:2px,color:#fff
    classDef s3 fill:#ff7f0e,stroke:#333,stroke-width:2px,color:#fff
    classDef combo fill:#2ca02c,stroke:#333,stroke-width:2px,color:#fff
    classDef output fill:#d62728,stroke:#333,stroke-width:2px,color:#fff

    class scan1Obj,scan1coefObj,snpInfoObj,genesObj input
    class autoplotScan1,autoplotCoef,ggplotSnpasso,autoplotGenes s3
    class ggplotCoefLod,ggplotSnpGenes,ggplotPxG combo
    class ggplotOut output

Developer Quick Start

Local Development Workflow 

# 1. Load local package sources dynamically
devtools::load_all()

# 2. Re-generate documentation & NAMESPACE
devtools::document()

# 3. Run R package checks
devtools::check(vignettes = FALSE)

Explore the sub-guides for detailed breakdowns of exported S3 generics, plotting functions, and underlying data transformations: