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>.
||R (≥ 3.0.2)
||doParallel (≥ 1.0.10), foreach (≥ 1.4.3), parallel (≥
3.0.2), pls (≥ 2.5.0), grDevices, graphics, stats, utils
||Christopher Pietras [aut, cre]
||Christopher Pietras <christopher.pietras at tufts.edu>
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