Within this repository you will find all the code and data files as used/described in the Cole et al paper (see CITATION).
It has also been adapted for work on a collaboration with Ryan O'Shaughnessy (UCL) on RPTOR gene expression (see CITATION).
The repository has the following structure:
app/ - Shiny app for visualising gene expression
bin/ - scripts for processing the source data
data/ - source data
CITATION - information on how to cite this work
README.md - information on the code and data (this file)
LICENCE - information on the licencing of the code
Makefile - code for running scripts via make
Makefile.RPTOR - code for performing RPTOR-specific analysis
This code requires you have make and R on your $PATH and have the
following R packages installed:
edgeR (tested with 3.14.0)
sqldf (tested with 0.4.10)
gplots (tested with 3.0.1)
For the Shiny App the shiny R library will also be required.
R version 3.3.0 was used during development (but should work with anything newer than 2.15.1).
To run all the scripts and generate all figures and data, type:
make
To run just the analyses and generate data, type:
make analysis
To run generate just the plots, type:
make figures
To delete all outputs, type:
make clean
To run RPTOR-specific analysis as per our 2016 paper (see CITATION):
make -f Makefile.RPTOR
This github repository accompanies two papers and covers the differential gene expression analysis in a cohort of Irish peadiatric eczema cases in comparison to their filaggrin, FLG, genotype.
The simplest way to use the code is via the Quickstart guide above, but each script found under bin/ can run be individually if you know what you're doing. Code has some in-line comments.
A utility webapp has also been developed with RStudio's Shiny framework to perform gene-specific searches of expression profiles between cases and controls or stratified by FLG genotype.
To run the Shiny App do the following:
- Clone this repository into a new RStudio project
- Open the
app\DRSexpr\app.Rfile - Click the 'Run App' button