TraRe {TraRe} | R Documentation

## Transcriptional Rewiring package for R.

### Description

TraRe (Transcriptional Rewiring) is an R package which contains the necessary tools to carry out several functions. Identification of module-based gene regulatory networks (GRN); score-based classification of these modules via a rewiring test; visualization of rewired modules to analyze condition-based GRN deregulation and drop out genes recovering via cliques methodology. For each tool, an html report can be generated containing useful information about the generated GRN and statistical data about the performed tests. These tools have been developed considering sequenced data (RNA-Seq).

### Available tools

- LINKER generates, from an initial RNA-Seq dataset where drivers (Transcription Factors) and targets genes are provided, GRN modules in three different forms: as raw results, from the phase I output modules; as modules from the phase II output modules and in the form of bipartite graphs, where drivers and targets relationships are defined. This is done using the `LINKER_run()` function. `?LINKER_run` for more information.

- LINKER also provides a way of generating a single GRN from specified list of driver and target genes. This ease the task of analyzing relationships between drivers and targets by constraining all the provided genes to be only in a single GRN. This is done by `NET_run()` function. Type `?NET_run` for more information

- The Rewiring test performs a permutation test over a certain condition to infer if that condition is producing any deregulation on our generated GRN. Bootstrapping plays an important role, as the non-convex nature of this biological events makes necessary to ensure that a certain behavior is repeated across bootstraps, and to confirm this event does not come from a particular realization. As bootstrapping has been performed in LINKER, this step will take advantage of them and will try to group highly scored modules, to infer modules similar behavior GRN across bootstraps. It will outputs a correlation matrix in the form of a heatmap (sorted by hierarchical clustering to ease interpretation), containing similar highly scored modules. `preparerewiring()` will return an object containing the necessary information for calling `runrewiring()` and generate graph objects, reports and graphs. Type `?preparerewiring` and `?runrewiring` for more information.

- The Visualization module contains a graphical way of detecting condition-dependent deregulation on the selected rewired modules containing gene regulatory networks. Once we have selected a cluster of modules that across bootstraps have similar behavior as GRN, we can generate single GRNs of the genes that belong to those modules, filtering by samples that belong to the condition we want to evaluate. Check `plot_igraph`,`return_layout` and `return_layout_phenotype`.

- The Results module generate an excel file is containing drivers-targets relationships and cliques. The way LINKER method works can make some highly-correlated driver genes (TFs) may be dropped from the resultant model, as the role they play at the GRN inference process is very similar. Due to this, we propose a method based on cliques (Fully Connected Networks) to recover those dropped drivers. Check `excel_generation` and `generatecliques` for more information.

### Author(s)

Maintainer: Jesus De La Fuente Cedeño jdelafuentec@unav.es (ORCID) [copyright holder]

Authors:

- Mikel Hernaez mhernaez@unav.es (ORCID) [copyright holder, thesis advisor]

- Charles Blatti blatti@illinois.edu (ORCID) [copyright holder]

### See Also

Useful links:

- https://github.com/ubioinformat/TraRe/tree/master

[Package TraRe version 1.2.0 Index]