timeOmics

timeOmics integrates multi-Omics longitudinal data measured on the same biological samples and selects key temporal features with strong associations within the same sample group.

Key Features:

• Pre-processing: Platform and time-specific normalization and filtering steps prepare multi-omics data for downstream analysis.
• Temporal Modeling: Each biological feature is modeled into one time expression profile to capture dynamic patterns.
• Clustering: Features with the same expression profile over time are grouped together to identify co-regulated molecular patterns.
• Post-hoc Validation: A validation step assesses the robustness of identified temporal features and clusters.

Scientific Applications:

• Longitudinal Multi-Omics Integration: timeOmics enables the global analysis of biological systems by integrating dynamic relationships between molecules across multiple omics layers.
• Seasonal Pattern Detection: The framework detects seasonal patterns of mRNA, metabolites, gut taxa and clinical variables in longitudinal studies.
• Diabetes Research: timeOmics has been applied to identify temporal patterns in patients with diabetes mellitus from the integrative Human Microbiome Project.

Methodology:

The framework consists of four main steps: platform and time-specific normalization and filtering; modeling each biological feature into one time expression profile; clustering features with the same expression profile over time; and a post-hoc validation step.