GitHub - MahShaaban/target: target: An R Package to Predict Combined Function of Transcription Factors (original) (raw)

target

Predict Combined Function of Transcription Factors

Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013). Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data.

Installation

The target package can be installed from Bioconductor using BiocManager.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("target")

Getting started

The target package contains two simulated datasets. sim_peaks is random peaks with random distances from the transcripts of chromosome 1 of the mm10 mouse genome. sim_transcripts is the same transcripts with random singed statistics assigned to each. In the following two examples, we introduce changes in these statistics to simulate conditions where two factors are working cooperatively or competitively on the same transcripts.

load libraries

library(target)

load data

data("sim_peaks") data("sim_transcripts")

To help visualize these cases, a plotting function plot_profiles was constructed to introduce the changes change in the statistics of the transcripts near the n number of peaks. The source code for the function is available in inst/extdata/plot-profiles.R which we source to use here. The output of the function is a series of plots to visualize the statistics of the two factors before and after introducing the changes, the peaks distances and scores and the predicted functions of the factors individually and combined.

source the plotting function

source(system.file('extdata', 'plot-profiles.R', package = 'target'))

The first two inputs to the plotting function is the simulated peaks and transcripts. We chose to introduce positive changes to the statistics of the transcripts with the top 5000 nearby peaks of the two factors.

simulate and plot cooperative factors

plot_profiles(sim_peaks, sim_transcripts, n = 5000, change = c(3, 3))

The changes introduced above are illustrated in the right upper quadrant of the scatter plot. The predicted functions of the two factors are similar, as shown by distribution function of the regulatory potential of their targets. Finally, when the targets are predicted based on the two statistics combined, the sign of the statistics product determines the direction of the factor interactions. Here, more higher ranking transcripts had positive/red/ cooperative change associated with the two factors.

References

Wang S, Sun H, Ma J, et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc. 2013;8(12):2502–2515. doi:10.1038/nprot.2013.150

Citation

For citing the package use:

# citing the package
citation("target")