MultiModalGraphics: an R package for graphical integration of multi-omics datasets.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Visualization

Background:

• Multimodal biological datasets are high-dimensional and complex.
• Existing visualization tools lack integrated statistical and computational annotations.
• Effective quantitative interpretation of multi-omics data is challenging.

Purpose of the Study:

• To introduce MultiModalGraphics, an R package for creating annotated scatterplots and heatmaps.
• To facilitate the quantitative interpretation of multi-omics and high-dimensional biological data.
• To streamline bioinformatics workflows from analysis to visualization.

Main Methods:

• Developed an R package, MultiModalGraphics.
• Implemented seamless embedding of statistical summaries (fold-changes, p-values, q-values, standard deviations).
• Ensured interoperability with Bioconductor packages (MultiAssayExperiment, limma, voom, iClusterPlus).

Main Results:

• MultiModalGraphics enables direct quantitative comparisons on visualizations.
• The package supports analysis of multi-omics and high-dimensional biological data.
• Case studies demonstrate practical utility on real-world datasets.

Conclusions:

• MultiModalGraphics enhances the quantitative interpretation of complex biological data.
• The package integrates statistical annotations directly into visualizations.
• It offers a streamlined solution for multi-omics data analysis and visualization workflows.