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[![Build Status](]( [![codecov](]( [![License: GPL v3](]( # timeOmics ***timeOmics*** is a generic data-driven framework to integrate multi-Omics longitudinal data (**A.**) measured on the same biological samples and select key temporal features with strong associations within the same sample group. ![](./man/figures/method_overview.png) The main steps of ***timeOmics*** are: * a pre-processing step (**B.**) Normalize and filter low-expressed features, except those not varying over time, * a modelling step (**C.**) Capture inter-individual variability in biological/technical replicates and accommodate heterogeneous experimental designs, * a clustering step (**D.**) Group features with the same expression profile over time. Feature selection step can also be used to identify a signature per cluster, * a post-hoc validation step (**E.**) Ensure clustering quality. ***timeOmics*** can be applied on both single-Omic or multi-Omics experimental design. *<font color="green"> If you came to this page thanks to our article and you wish to access its example scripts please follow this <a href=""> link </a>.</font>* ## Installation ### Latest `GitHub` Version Install the devtools package in R, then load it and install the latest stable version of `timeOmics` from `GitHub` ```r ## install devtools if not installed if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") ## install timeOmics devtools::install_github("abodein/timeOmics") ``` ## Citing *Bodein A, Chapleur O, Droit A and LĂȘ Cao K-A (2019) A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types. Front. Genet. 10:963. <a href=""> doi:10.3389/fgene.2019.00963</a>* ## Maintainer Antoine Bodein (<>) ## Bugs/Feature requests If you have any bugs or feature requests, [let us know]( Thanks!