tempoR: Characterizing Temporal Dysregulation

TEMPO (TEmporal Modeling of Pathway Outliers) is a pathway-based outlier detection approach for finding pathways showing significant changes in temporal expression patterns across conditions. Given a gene expression data set where each sample is characterized by an age or time point as well as a phenotype (e.g. control or disease), and a collection of gene sets or pathways, TEMPO ranks each pathway by a score that characterizes how well a partial least squares regression (PLSR) model can predict age as a function of gene expression in the controls and how poorly that same model performs in the disease. TEMPO v1.0.3 is described in Pietras (2018) <doi:10.1145/3233547.3233559>.

Depends: R (≥ 3.0.2)
Imports: doParallel (≥ 1.0.10), foreach (≥ 1.4.3), parallel (≥ 3.0.2), pls (≥ 2.5.0), grDevices, graphics, stats, utils
Suggests: knitr, rmarkdown
Published: 2019-05-27
Author: Christopher Pietras [aut, cre]
Maintainer: Christopher Pietras <christopher.pietras at tufts.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: tempoR results


Reference manual: tempoR.pdf
Vignettes: run-example
Package source: tempoR_1.0.4.4.tar.gz
Windows binaries: r-prerelease: tempoR_1.0.4.4.zip, r-release: tempoR_1.0.4.4.zip, r-oldrel: tempoR_1.0.4.4.zip
macOS binaries: r-prerelease: tempoR_1.0.4.4.tgz, r-release: tempoR_1.0.4.4.tgz, r-oldrel: tempoR_1.0.4.4.tgz


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