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https://github.com/mahshaaban/target
target: An R Package to Predict Combined Function of Transcription Factors
https://github.com/mahshaaban/target
algorithm chip-seq dna-binding gene-regulation r-package transcription-factors
Last synced: about 2 months ago
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target: An R Package to Predict Combined Function of Transcription Factors
- Host: GitHub
- URL: https://github.com/mahshaaban/target
- Owner: MahShaaban
- License: gpl-3.0
- Created: 2019-08-28T05:37:02.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-04-18T03:30:14.000Z (over 4 years ago)
- Last Synced: 2024-10-28T12:44:31.143Z (2 months ago)
- Topics: algorithm, chip-seq, dna-binding, gene-regulation, r-package, transcription-factors
- Language: HTML
- Homepage: https://bioconductor.org/packages/target/
- Size: 4.5 MB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
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# 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`.
```{r install_biocmanager,eval=FALSE}
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.
```r
# load libraries
library(target)
```
```r
# 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.
```r
# 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.
```r
# 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:
```{r citation, warning=FALSE}
# citing the package
citation("target")