timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.
|Bioconductor views||Classification Clustering DimensionReduction FeatureExtraction GenePrediction ImmunoOncology Metabolomics Metagenomics Microarray MultipleComparison Proteomics Regression Sequencing Software TimeCourse|
|Package repository||View on GitHub|
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