- Visualise RNA-seq differential expression data.
- Perform your own DGE analysis, or use the inbuilt server to analyse from your own "counts" file.
Access a public web service running Degust
View a short video of the interface in use.
Read a summary on the Degust home page.
If you do not want to use the public Degust installation, you may install your own.
You first need to grab a copy of Degust.
git clone firstname.lastname@example.org:Victorian-Bioinformatics-Consortium/degust.git
Degust can be installed in two ways:
- Perform your own DGE analysis, and use only the web frontend from Degust
- Install the frontend and back-end software to perform analysis and visualise the results.
Frontend installation only
To use the frontend visualisation, you will need to have done your own DGE analysis with a tool like edgeR or voom. You will need CSV file contain a line per gene, and the following columns:
- ID - containing a unique identifier for each gene (required)
- Adjusted p-value - The adjusted p-value (FDR or similar) for that gene (required)
- Log intensity for each condition - Used to compute the log fold-change (required)
- Average intensity across the conditions - Used for the MA-plot (required)
- Gene info - Arbitrary information columns to display in the gene list table (optional)
- Read counts - Read counts for each replicate, only used for display purposes (optional)
The simplest approach is to download degust.py then run it with your csv file as a parameter. This will create a single HTML page that you view or share. Run ``degust.py --help` to find the parameters to specify the column names for your CSV.
Degust is released under the GPL v3 (or later) license, see COPYING.txt