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tempoR
View on CRAN: Click
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Download and install tempoR package within the R console
Install from CRAN:
install.packages("tempoR")
Install from Github:
library("remotes")
install_github("cran/tempoR")
Install by package version:
library("remotes")
install_version("tempoR", "1.0.4.4")
Attach the package and use:
library("tempoR")
Maintained by
Christopher Pietras
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-05-27
Latest Update: 2023-04-07
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) .
How to cite:
Christopher Pietras (2019). tempoR: Characterizing Temporal Dysregulation. R package version 1.0.4.4, https://cran.r-project.org/web/packages/tempoR
Previous versions and publish date:
1.0.4.4 (2019-05-27 10:30)
Other packages that cited tempoR R package
View tempoR citation profile
Other R packages that tempoR depends,
imports, suggests or enhances
Functions, R codes and Examples using
the tempoR R package
Some associated functions: dflatExample . gse32472Example . gse32472ExampleTempoResults . loadCLS . loadGCT . loadGMT . tempo.mkplot . tempo.permutationTest . tempo.run . tempo.runInstance . tempo.writeOutput .
Some associated R codes: data.R . tempoCore.R . tempoInput.R . tempoOutput.R . Full tempoR package functions and examples
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