README.md

In TReNA: Fit transcriptional regulatory networks using gene expression, priors, machine learning

TReNA

Fit transcriptional regulatory networks using gene expression, priors, machine learning

Getting Started

To build and test:

• clone this repository
• install R 3.2.3 or later; RUnit 0.4.31 or later (see below)
• install the following solver packages:
• glmnet R package 2.0.3 or later
• randomForest
• vbsr
• flare
• lassopv
• cd TReNA
• R CMD INSTALL .

The most reliable way to install package dependencies (and other of their dependencies):

source("http://bioconductor.org/biocLite.R")
biocLite(c("glmnet", "RUnit"))

Using TReNA

• open an R session
• source("inst/unitTests/test_TReNA.R")
• runTests()

The unitTests perform double duty: they ensure the package performs as (currently) expected; they introduce the package to the user and developer. Thus test_TReNA.R is one entry point into this project.

We have also created a Jupyter Notebook demonstrating use of TReNA with 4 different solvers

TReNA documentation built on Nov. 17, 2017, 12:35 p.m.