alserglab/phantasus repository overview

Phantasus is a web tool designed for visual and interactive gene expression analysis. In particular, it was designed to allow to go from a typical dataset to differential expression and downstream analysis in an easy and streamlined manner. For that aim, Phantasus integrates an intuitive heatmap interface with gene expression analysis tools from Bioconductor.

Main features:

• Loading public datasets from Gene Expression Omnibus with both microarrays and RNA-seq datasets (via ARCHS4) being supported.
• Differential gene expression using limma or DESeq2.
• Publication ready plots with export to SVG: PCA plot, row profiles, box plots.
• Clustering: k-means and hierarchical.
• Gene set enrichment analysis via fgsea package.
• Sharing session links.

⁠Quick start

Phantasus can be accessed online via the official mirrors: https://alserglab.wustl.edu/phantasus⁠.

Alternatively, Phantasus can be set up locally using this docker image. To run Phantasus from a docker image you may use following commands:

sudo docker pull alserglab/phantasus
mkdir phantasus-cache
sudo docker run -t -p 8000:8000 \
    --env OCPU_USER=$UID \
    --mount type=bind,source=`pwd`/phantasus-cache,target=/var/phantasus/cache \
    alserglab/phantasus

Phantasus will be available at http://localhost:8000⁠ after the initial setup is finished. The setup populates /var/phantasus/cache directory within the container, thus it is helpful to mount it as a volume to preserve the files between runs.

Please refer to the documentation⁠ for more comprehensive installation and configuration instructions.

⁠Links:

• Official mirror: https://alserglab.wustl.edu/phantasus⁠.
• Documentation: https://alserglab.github.io/phantasus-doc⁠.
• Bioconductor package: https://bioconductor.org/packages/phantasus⁠.
• Docker image: https://hub.docker.com/r/alserglab/phantasus⁠.

⁠Citation:

• Kleverov, M., Zenkova, D., Kamenev, V., Sablina, M., Artyomov, M. N., & Sergushichev, A. A. (2024). Phantasus, a web application for visual and interactive gene expression analysis. Elife, 13, e85722. https://doi.org/10.7554/eLife.85722⁠.