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) .


Reference manual

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install.packages("tempoR") by Christopher Pietras, a year ago

Browse source code at

Authors: Christopher Pietras [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports doParallel, foreach, parallel, pls, grDevices, graphics, stats, utils

Suggests knitr, rmarkdown

See at CRAN