Package: timeOmics 1.25.0

Antoine Bodein

timeOmics: Time-Course Multi-Omics data integration

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.

Authors:Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut]

timeOmics_1.25.0.tar.gz
timeOmics_1.25.0.zip(r-4.7)timeOmics_1.25.0.zip(r-4.6)timeOmics_1.25.0.zip(r-4.5)
timeOmics_1.25.0.tgz(r-4.6-any)timeOmics_1.25.0.tgz(r-4.5-any)
timeOmics_1.25.0.tar.gz(r-4.7-any)timeOmics_1.25.0.tar.gz(r-4.6-any)
timeOmics_1.25.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
timeOmics/json (API)

# Install 'timeOmics' in R:

install.packages('timeOmics', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/abodein/timeomics/issues

On BioConductor:timeOmics-1.25.0(bioc 3.24)timeOmics-1.24.0(bioc 3.23)

clustering featureextraction timecourse dimensionreduction software sequencing microarray metabolomics metagenomics proteomics classification regression immunooncology geneprediction multiplecomparison cluster integration multi-omics time-series

5.76 score 26 stars 11 scripts 384 downloads 1 mentions 14 exports 80 dependencies

Last updated from:d20cc9f5ff. Checks:1 ERROR, 7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	216
linux-devel-x86_64	WARNING	385
source / vignettes	OK	258
linux-release-x86_64	WARNING	302
macos-release-arm64	WARNING	162
macos-oldrel-arm64	WARNING	169
windows-devel	WARNING	568
windows-release	WARNING	202
windows-oldrel	WARNING	562
wasm-release	OK	191

Exports:get_demo_cluster get_demo_silhouette getCluster getNcomp getSilhouette getUpDownCluster lmms.filter.lines plotLong proportionality remove.low.cv tuneCluster.block.spls tuneCluster.spca tuneCluster.spls unscale

Dependencies:backports base64enc BH BiocParallel bslib cachem checkmate cli codetools corpcor cpp11 digest dplyr ellipse evaluate farver fastmap fontawesome formatR fs futile.logger futile.options generics ggplot2 ggrepel glue gridExtra gtable highr htmltools htmlwidgets igraph isoband jquerylib jsonlite knitr labeling lambda.r lattice lifecycle lmtest magrittr MASS Matrix matrixStats memoise mime mixOmics pillar pkgconfig plyr purrr R6 rappdirs rARPACK RColorBrewer Rcpp RcppEigen reshape2 rgl rlang rmarkdown RSpectra S7 sass scales snow stringi stringr tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml zoo

timeOmics

Antoine Bodein, Olivier Chapleur, Kim-Anh Lê Cao, Arnaud Droit

Rendered fromvignette.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2024-04-09
Started: 2019-09-23

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
dmatrix.spearman.dissimilarity	dmatrix.spearman.dissimilarity
get_demo_cluster	get_demo_cluster
Get data for silhouette demo	get_demo_silhouette
Get variable cluster from (s)PCA, (s)PLS or block.(s)PLS	getCluster
Get optimal number of components	getNcomp
Get Silhouette Coefficient from (s)PCA, (s)PLS or block.(s)PLS clusters	getSilhouette
Up-Down clustering	getUpDownCluster
Filter Linear Profiles from Linear Mixed Model output	lmms.filter.lines
Plot Longitudinal Profiles by Cluster	plotLong
Proportionality Distance	proportionality
Remove features with low variation	remove.low.cv
Feature Selection Optimization for block (s)PLS method	tuneCluster.block.spls
Feature Selection Optimization for sPCA method	tuneCluster.spca
Feature Selection Optimization for sPLS method	tuneCluster.spls
Unscales a scaled data.frame	unscale