#Plant Breeding Analytics Suite

A unified, professional R Shiny dashboard combining three analytical modules for plant breeding research:

Module	Description
D² Genetic Diversity Analyser	Mahalanobis D² distances, Tocher clustering, PCA, MANOVA, Pearson correlation
MET Analysis Suite (AMMI / GGE)	Multi-environment trial analysis — stability, AMMI, GGE biplots
Multi-Trait Selection Suite	MTSI, MGIDI, FAI-BLUP, Smith-Hazel index, GT/GYT biplots, Venn diagrams

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📸 Screenshots

(Add screenshots to docs/screenshots/ and update paths below)

D² Diversity	MET Stability	Multi-Trait Selection

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🧬 Dataset Description

The repository includes real wheat (Triticum aestivum L.) phenotyping data collected under terminal heat stress conditions:

data/MET_wheat_data.csv — Multi-Environment Trial

• Crop: Wheat (T. aestivum L.)
• Environments: 6 (E1–E6)
• Genotypes: 36
• Replications: 3
• Traits (11): HDNG, DTM, GFD, PH, SL, GWPS, GNPS, SPMS, YPMS, BYPMS, TGW
• Use: MET Analysis Suite & Multi-Trait Selection Suite

data/DWR_raw_data.csv — D² Raw Data (Single Environment)

• Condition: Terminal Heat Stress environment
• Genotypes: 36 | Replications: 3
• Traits (11): HDNG, DTM, GFD, PH, SL, GWPS, GNPS, SPMS, YPMS, BYPMS, TGW
• Column format: GEN, REP, trait1, trait2, …
• Use: D² Genetic Diversity Analyser (Raw Data upload)

data/DWR_genotype_means.csv — D² Genotype Means

• Genotypes: 36 | Traits: 11
• Column format: GEN, trait1, trait2, …
• Use: D² Genetic Diversity Analyser (Genotype Means upload)

Trait Abbreviations

Abbreviation	Full Name	Unit
HDNG	Heading date	Days
DTM	Days to maturity	Days
GFD	Grain filling duration	Days
PH	Plant height	cm
SL	Spike length	cm
GWPS	Grain weight per spike	g
GNPS	Grain number per spike	No.
SPMS	Spikes per m²	No.
YPMS	Yield per m²	kg
BYPMS	Biological yield per m²	kg
TGW	Thousand grain weight	g

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✨ Features

Module 1 — D² Genetic Diversity Analyser

• Upload raw replicated data or pre-computed genotype means
• MANOVA + univariate ANOVA per trait
• Mahalanobis D² distance matrix with interactive heatmap
• Tocher clustering — membership table, inter/intra-cluster distances, dendrogram, network plot
• PCA — scree plot, biplot, eigenvalues, loadings, cluster overlay
• Pearson correlation heatmap with significance table
• Full CSV + PDF export

Module 2 — MET Analysis Suite

• Descriptive statistics + GxE heatmap
• Individual & pooled ANOVA + Bartlett homogeneity test
• ANOVA-based stability — Ecovalence (Wricke), Shukla's σ²
• Regression stability — Eberhart–Russell (bi, S²di)
• Non-parametric stability — Lin & Binns superiority (Pi), Fox top-third
• Factor Analysis of GE interaction
• Comprehensive wrap — ge_stats() (all parameters in one table)
• AMMI — AMMI1, AMMI2, AMMI biplot; ASV + WAAS index
• GGE — 7 biplot types × 3 SVP options

Module 3 — Multi-Trait Selection Suite

• Mixed-model fitting: gamem_met() + waasb() from metan
• Variance components (BLUP-based)
• MTSI — Multi-Trait Stability Index
• MGIDI — Multi-trait Genotype-Ideotype Distance Index
• FAI-BLUP — Factor Analysis and Ideotype-Design
• Smith-Hazel classical selection index
• Direct selection on yield trait
• Selection differentials (Table 3), coincidence index, 4-way Venn diagram
• GT / GYT biplots
• Radar chart for multi-trait comparison
• Strengths & Weaknesses plots for selected genotypes
• Full XLSX + PDF export

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🛠️ Installation

Prerequisites

• R ≥ 4.1.0
• RStudio (recommended)

Install Required Packages

pkgs <- c(
  "shiny", "shinydashboard", "shinyWidgets", "shinyjs", "shinycssloaders",
  "DT", "plotly", "ggplot2", "dplyr", "tidyr", "readxl", "writexl",
  "reshape2", "RColorBrewer", "ggrepel", "scales", "viridis",
  "factoextra", "FactoMineR", "Hmisc", "biotools", "ggdendro", "dendextend",
  "metan", "corrplot", "ggforce", "patchwork", "tibble", "purrr",
  "fmsb", "grDevices"
)
install.packages(pkgs)

Run the App

# Option 1 — from RStudio
shiny::runApp("app.R")

# Option 2 — directly
source("app.R")

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📁 Repository Structure

PlantBreedingSuite/
│
├── app.R                          # Main Shiny application (all 3 modules)
│
├── data/
│   ├── MET_wheat_data.csv         # Multi-environment trial data (6E × 36G × 3R)
│   ├── DWR_raw_data.csv           # D² raw data — terminal heat stress (36G × 3R)
│   └── DWR_genotype_means.csv     # D² genotype means (36G × 11 traits)
│
├── docs/
│   ├── USER_GUIDE.md                                # Detailed step-by-step usage guide
│   ├── PlantBreedingSuite_User_Manual.pdf           # App user manual
│   └── screenshots/                                 # App screenshots
│
├── README.md                      # This file
├── LICENSE                        # MIT License
└── .gitignore                     # R / RStudio / OS ignores

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🚀 Quick Start

D² Diversity Analysis

1. Open the app → D² Upload tab
2. Upload data/DWR_raw_data.csv as Raw Data (trait start column = 3)
3. Upload data/DWR_genotype_means.csv as Genotype Means
4. Click Load & Validate Data
5. Run MANOVA → D² Distances → Tocher → PCA → Correlation in sequence

MET / Stability Analysis

1. Go to MET — Data Upload
2. Upload data/MET_wheat_data.csv; set ENV = ENV, GEN = GEN, REP = REP
3. Click Load Data
4. Navigate to any analysis tab and click Run

Multi-Trait Selection

1. Go to MT — Data & Settings
2. Upload data/MET_wheat_data.csv; map ENV, GEN, REP columns
3. Set trait goals (↑ higher / ↓ lower) and selection intensity (%)
4. Go to MT — Fit Models → click Fit gamem_met + waasb
5. Run MTSI → MGIDI → FAI-BLUP → Smith-Hazel in any order

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📖 Citation

If you use this application or the data in your research, please cite:

Meena, V. K. (2026). PlantBreedingSuite: An integrated open-source R Shiny dashboard for genetic diversity analysis, multi-environment trial stability, and multi-trait selection in crop breeding programmes. SoftwareX, 35, 102878. https://doi.org/10.1016/j.softx.2026.102878 

@article{Meena2026PlantBreedingSuite, author = {Vijay Kamal Meena}, title = {PlantBreedingSuite: An integrated open-source R Shiny dashboard for genetic diversity analysis, multi-environment trial stability, and multi-trait selection in crop breeding programmes}, journal = {SoftwareX}, volume = {35}, pages = {102878}, year = {2026}, issn = {2352-7110}, doi = {10.1016/j.softx.2026.102878}, publisher = {Elsevier} }

Key Packages to Cite

• metan: Olivoto & Lúcio (2020) The Plant Phenome Journal — doi:10.1002/ppj2.20017
• biotools: Silva (2017) R package — CRAN
• FactoMineR: Lê et al. (2008) Journal of Statistical Software

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👨‍🔬 Developer

Dr. Vijay Kamal Meena
Assistant Professor (GPB)
Agriculture University Jodhpur, Rajasthan, India

🎓	M.Sc. & Ph.D. — ICAR-IARI, New Delhi
🏆	ICAR-ARS 2021
🏛️	Agriculture University Jodhpur
📧	vjkamal93@gmail.com
📧	vijaykamal@aujodhpur.ac.in

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📄 License

This project is licensed under the MIT License — see LICENSE for details.

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Contributing

Contributions, bug reports, and feature requests are welcome!
Please open an issue or submit a pull request.

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Plant Breeding Analytics Suite v1.0 | 2025 | Agriculture University Jodhpur