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.
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.
If you came to this page thanks to our article and you wish to access its example scripts please follow this link .
Install the devtools package in R, then load it and install the latest stable version of timeOmics from GitHub
## install devtools if not installed
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
## install timeOmics
devtools::install_github("abodein/timeOmics")Bodein A, Chapleur O, Droit A and Lê Cao K-A (2019) A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types. Front. Genet. 10:963. doi:10.3389/fgene.2019.00963
Antoine Bodein (antoine.bodein.1@ulaval.ca)
If you have any bugs or feature requests, let us know. Thanks!