imDEV

imDEV performs multivariate statistical analysis and visualization of omics and high-throughput biological datasets using R-based methods.

Key Features:

• Multivariate statistical methods: Implements hierarchical clustering, principal component analysis (PCA), independent component analysis (ICA), partial least squares (PLS) regression, and discriminant analysis.
• Multiple comparisons: Supports multiple comparisons with false discovery correction.
• Visualization outputs: Produces scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots, correlation networks, and two- and three-dimensional representations.
• Large-scale data handling: Supports analysis and exploration of large high-throughput biological datasets.

Scientific Applications:

• Omics data analysis: Analysis of genomics, proteomics, metabolomics, and other high-throughput biological datasets.
• Pattern discovery and inference: Generation of information-rich visualizations to reveal underlying patterns and relationships in complex biological data.

Methodology:

Implemented using R for statistical computations and VBA (Visual Basic for Applications) to integrate R-based analyses into Excel, performing multivariate analyses such as PCA, ICA, PLS regression, hierarchical clustering, and discriminant analysis.