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.
|Author||Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut]|
|Bioconductor views||Classification Clustering DimensionReduction FeatureExtraction GenePrediction ImmunoOncology Metabolomics Metagenomics Microarray MultipleComparison Proteomics Regression Sequencing Software TimeCourse|
|Maintainer||Antoine Bodein <email@example.com>|
|Package repository||View on Bioconductor|
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