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timeOmics

Time-Course Multi-Omics data integration

Bioconductor version: 3.23 · Package version: 1.24.0

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("timeOmics")

Details

MaintainerAntoine Bodein <antoine.bodein.1@ulaval.ca>
AuthorAntoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut]
LicenseGPL-3
Bug Reportshttps://github.com/abodein/timeOmics/issues
Downloads rank284
Source branchRELEASE_3_23
biocViewsClassification, Clustering, DimensionReduction, FeatureExtraction, GenePrediction, ImmunoOncology, Metabolomics, Metagenomics, Microarray, MultipleComparison, Proteomics, Regression, Sequencing, Software, TimeCourse

Documentation

Download

Dependencies

Depends: mixOmics, R (>= 4.0)

Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, lmtest, plyr, checkmate

Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots