aljabadi / timeOmics_BioC

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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.

The main steps of timeOmics are:

a pre-processing step (B.) Normalize and filter low-expressed features, except those not varying in 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.

Installation

Latest GitHub Version

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_BioC")

Citing

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

Maintainer

Antoine Bodein (antoine.bodein.1@ulaval.ca)

Bugs/Feature requests

If you have any bugs or feature requests, let us know. Thanks!

About

License:GNU General Public License v3.0


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