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) .


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install.packages("tempoR")

1.0.4.4 by Christopher Pietras, 7 months ago


Browse source code at https://github.com/cran/tempoR


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