timeOmics

timeOmics integrates longitudinal multi-omics datasets and identifies temporal molecular patterns to characterize dynamic biological systems.

Key Features:

• Integration of longitudinal multi-omics data: Integrates multiple omics types such as genomics, transcriptomics, and metabolomics measured longitudinally from the same samples.
• Pre-processing capabilities: Provides preprocessing steps to standardize and clean raw multi-omics data prior to analysis.
• Modeling and clustering: Applies temporal statistical modeling and clustering to detect time-dependent patterns and group samples by temporal profiles.
• Identification of temporal features: Selects key temporal features that show strong associations within sample groups over time.

Scientific Applications:

• Seasonal and cyclical pattern analysis: Detects seasonal or cyclical variations in mRNA expression, metabolite levels, gut microbiota composition, and clinical variables.
• Clinical longitudinal multi-omics in diabetes mellitus: Applied to patient data from the integrative Human Microbiome Project to analyze temporal molecular and clinical changes in diabetes mellitus.

Methodology:

Data pre-processing; integration framework combining different omics types; temporal modeling to identify significant temporal patterns; clustering analysis to group samples by temporal profiles.