DGE-ontology is a set of two Jupyter notebooks dedicated to:
- visualisation of differential gene expression (DGE) analysis results
- gene set enrichment analysis (ontology analysis) and visualisation
The software has been primarily designed for transcriptomics (for results obtained using Salmon1 and DESeq22), however it may be utilised for any data that express fold change of relative or absolute quantity measures of multiple entities (transcripts, proteins, metabolites, etc.).
Detailed step-by-step documentation and the methodology description are placed in both notebooks.
The exemplary input contains metadata on Staphylococcus aureus RN4220
transcriptome and output from rnaseq-pipeline-2
executed for two strains: wild type (wt51e) and ΔΔsaoBC mutant (mt51e).
Notebooks contain the test output. If rerun for the exemplary input, exactly
the same results should be obtained. All visualisations are saved to output
directory as high-resolution PNG files.
Miniconda installation is required. The notebooks have been tested on Ubuntu 22.04 using conda package manager 23.11.0 and the following packages:
- jupyterlab 4.0.11
- scikit-learn 1.4.0
- scipy 1.12.0
- pandas 2.2.0
- matplotlib 3.8.2
- pillow 10.2.0
- pyarrow 14.0.2
A ready-to-use conda environment might be created using envs/dge-ontology.yml:
conda env create --file envs/dge-ontology.ymlWhen successfully created, the environment may be activated as follwing:
conda activate dge-ontologyOnce the evironment is active, browse and run the notebooks in Jupyter Lab:
jupyter lab