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>.
| Version: | 1.0.4.4 |
| 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-devel: 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-release: tempoR_1.0.4.4.tgz, r-oldrel: tempoR_1.0.4.4.tgz |
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