Gdaphen, R pipeline to identify the most important qualitative and quantitative predictor variables from phenotypic...

Area of Science:

• Bioinformatics and computational biology
• Statistical genetics
• Phenotypic data analysis

Background:

• Analyzing phenotypic data is crucial for understanding disease states, treatment responses, and sexual dimorphism.
• Challenges include small sample sizes, high dimensionality, and complex experimental designs.
• Existing tools are fragmented, complicating analysis for non-statisticians.

Purpose of the Study:

• To develop an integrated bioinformatics pipeline for efficient phenotypic data analysis.
• To identify key qualitative and quantitative variables for discriminating between biological groups (genotypes, treatments, sex).
• To provide researchers with an accessible tool for complex statistical assessments.

Main Methods:

• Utilizes Multiple Factor Analysis (MFA) to handle grouped variables and reduce dimensionality.
• Employs Principal Component Analysis (PCA) to select non-correlated variables for enhanced predictive modeling.
• Integrates General Linear Model (GLM)-based classifiers and Random Forest (RF) for variable discrimination.

Main Results:

• Gdaphen effectively identifies important predictor variables for discriminating between genotypes, treatments, or sex.
• The pipeline enhances classifier predictive power by using optimized, non-correlated variables.
• Provides efficacy metrics and publication-ready visualizations for clear result interpretation.

Conclusions:

• Gdaphen offers a comprehensive, user-friendly framework for phenotypic data analysis.
• It simplifies pre-processing, statistical assessment, and result visualization for researchers.
• The open-source tool facilitates easier identification of critical phenotypic discriminators.