ShinyAIM now includes comprehensive enhancements for GWAS data visualization and analysis:
- QQ Plots: Assess p-value distribution with genomic inflation factor (λ) calculation
- Multiple Testing Corrections: Bonferroni and FDR (Benjamini-Hochberg) methods
- Summary Statistics Dashboard: Real-time display of key metrics (total markers, significant hits, λ, thresholds)
- Custom Color Schemes: Choose from default, colorblind-friendly, grayscale, or custom palettes
- High-Resolution Export: Download plots in PNG, PDF, and SVG formats
- Interactive Data Tables: Searchable and sortable tables using DT package
- Loading Indicators: Visual feedback for long-running operations
- Session Save/Load: Save analysis state and reload later (RDS format)
- Data Preprocessing: Filter by MAF, missing rate, and p-value thresholds
- Multiple File Comparison: Compare two GWAS runs side-by-side
- Modern UI: Enhanced interface with bslib theme
- Progress Spinners: Visual feedback for data processing
- Improved Layout: Better spacing, cards, and responsive design
The application is hosted on Shinyapps.io here: https://chikudaisei.shinyapps.io/shinyaim/
The application can be run locally with just one command in R software or RStudio:
shiny::runGitHub("ShinyAIM", "whussain2")
After running the above code the required libraries including 'shiny', 'ggplot2', 'dplyr', 'grid', 'plotly', 'manhattanly', 'forcats', 'DT', 'shinycssloaders', 'bslib', 'colourpicker', 'shinyWidgets', 'ggrepel', 'scales', and 'viridis' will be automatically installed.
These packages can also be installed by running the following code in R or RStudio:
install.packages(c("shiny","ggplot2","dplyr","grid","plotly","manhattanly","forcats",
"DT","shinycssloaders","bslib","colourpicker","shinyWidgets",
"ggrepel","scales","viridis"))
- Upload GWAS results and visualize p-values across the genome
- Customize significance thresholds
- Choose color schemes for better visualization
- Apply multiple testing corrections
- Export high-resolution figures
- Assess whether observed p-values follow expected distribution
- Calculate and display genomic inflation factor (λ)
- Detect population stratification
- Export plots for publications
- View multiple timepoints or phenotypes simultaneously
- Adjust grid layout and visualization parameters
- Preprocess data with filtering options
- Export composite figures
- Compare associated markers across timepoints
- Side-by-side comparison of two GWAS runs
- Interactive selection of significance thresholds
- Histogram and density plots
- Boxplots across timepoints
- Interactive exploration of trait distributions
- Save complete analysis sessions
- Load previous sessions to continue work
- Compare multiple GWAS datasets
- Session information display
This shiny code is licensed under Artistic License 2.0. at https://opensource.org/licenses/Artistic-2.0 .
Copyright (c) 2018, Waseem Hussain, code licensed under Artistic License 2.0.
You may contact the author of this code, Waseem Hussain at waseem.hussain@ul.edu; waseemhussain907@gmail.com
The manuscript describing the application is published in Plant Direct Jouranl and can be found here https://doi.org/10.1002/pld3.91