https://github.com/bioconductor-source/sepira

https://github.com/bioconductor-source/sepira

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Created almost 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Changelog

README.md

SEPIRA-package

Systens EPigenomics Inference of Regulatory Activity

SEPIRA is a novel algorithm which estimates transcription factor activity in any given sample from its genome-wide mRNA expression or DNA methylation profile[1]. It encompasses two main steps:

  1. Construction of a tissue-specific transcription factor regulatory network, consisting of transcription factors that are more highly expressed in the user-specified tissue type (the 'tissue type of interest') compared to other tissue types, plus an associated set of high-confidence downstream targets.
  2. Estimation of transcription factor activity in this network, in any given dataset consisting of gene expression or promoter DNA methylation profiles.

Usage

Inferring tissue-specific network

{r eval=FALSE} net.o <- sepiraInfNet(data=data.m, tissue=colnames(data.m), toi = "Lung", cft = "Blood", TFs = TFeid, sdth = 0.25, sigth = 0.05, pcorth = 0.2, degth = c(0.05, 0.05), lfcth = c(log2(1.5), 0), minNtgts = 3, ncores = 1) ** Note: data.m should be a normalized gene expression data set.

Estimating transcription factor activity

{r eval=FALSE} sepiraRegAct(data = data.m, type = "DNAm", regnet = net.o$netTOI, norm = "z", ncores = 1)

Installation

An easy way to install SEPIRA is by facilitating the devtools R package.

```{r eval=FALSE}

install.packages("devtools")

library(devtools) installgithub("YC3/SEPIRA", buildvignettes=TRUE) ``` Alternatively, the package can also be cloned or downloaded from this github-rep, built via R CMD build and installed via the R CMD INSTALL command.

Getting started

The SEPIRA package contains a tutorial showing people how to implement SEPIRA in their work. The tutorial can be found in the package-vignette:

library(SEPIRA) vignette("SEPIRA")

Acknowledgements

Thanks to my supervisor Andrew Teschendorff for reviewing and commenting on the package and providing the code.

References

Chen Y, Widschwendter M, and Teschendorff AE. 2017. “Systems-Epigenomics Inference of Transcription Factor Activity Implicates Aryl-Hydrocarbon-Receptor Inactivation as a Key Event in Lung Cancer Development.” Genome Biol 18:236.

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  • Email: maintainer@bioconductor.org

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Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • corpcor >= 1.6.9 imports
  • limma >= 3.32.5 imports
  • parallel >= 3.3.1 imports
  • stats * imports
  • igraph * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests