README.md
In SEPIRA: Systems EPigenomics Inference of Regulatory Activity

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:

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.
Estimation of transcription factor activity in this network, in any given dataset consisting of gene expression or promoter DNA methylation profiles.

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)

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

```{r eval=FALSE}

install.packages("devtools")

library(devtools) install_github("YC3/SEPIRA", build_vignettes=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.

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")

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

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.