Characterizing Temporal Dysregulation
Description
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) .