Holomics

Holomics provides a well-defined workflow for feature selection, correlation identification, and biomarker discovery in multi-omics data integration and analysis, leveraging methods from the mixOmics package.

Key Features:

• Single-omics data upload and pre-filtering: Supports initial data upload and pre-filtering of single-omics datasets as the starting point of the workflow.
• Single-omics analysis: Refines pre-filtered data by focusing on key features to reduce dataset dimensionality.
• Multi-omics analysis: Analyzes reduced datasets to unveil correlations between 2-n datasets.
• mixOmics integration: Leverages methods from the mixOmics package for underlying analytical computations.

Scientific Applications:

• Multi-omics data integration: Integrates diverse omics datasets including microbiomics, transcriptomics, and metabolomics for holistic biological understanding.
• Biomarker discovery: Identifies key components and biomarkers applicable in breeding programs or disease diagnostics.
• Correlation identification: Reveals correlations across different biological layers to understand interactions among genes, proteins, metabolites, and environmental factors.
• Sugar beet storability research: Demonstrated utility in integrating microbiomics, transcriptomics, and metabolomics data to elucidate factors associated with improved sugar beet storability.

Methodology:

Holomics implements a workflow beginning with upload and pre-filtering of single-omics data, followed by single-omics analysis to focus on key features, then multi-omics analyses to unveil correlations between 2-n datasets using methods from the mixOmics package.