TCP-Lab / transportome_profiler

Code to profile the expression of the transportome

Transportome Profiler analysis

This repository contains the code for the analysis on the expression profile of the transportome in Cancer based on the MTP-DB.

Note

Read the preprint here: Profiling the Expression of Transportome Genes in cancer: A systematic approach

It's potentially out of date.

The project follows the Kerblam! standard.

Running the analysis

You can run the analysis pipelines with Kerblam! and docker:

# Clone the repo
git clone [e-mail]:TCP-Lab/transportome_profiler.git
cd ./transportome_profiler

kerblam data fetch # Fetch the input data not present in the repository
kerblam run <pipeline>

Kerblam! will build docker containers and run the analysis locally. To run without docker, read below.

Pipelines

The project currently encompasses the following pipelines:

• heatmaps: Create large heatmaps from the expression matrices by using GSEA on computed gene rankings, testing all possible gene lists that can be made from the MTP-DB.
  • The test profile makes this pipeline much faster by running on smaller (i.e. sampled) input data (~75% reduction in sample number, only 5000 random genes).

Running locally without docker

You need some requirements to be installed before you can run the analysis locally:

• R version 4.3.0+.
  • Install R requirements with ./src/helper_scripts/install_r_pkgs.R.
• Python version 3.11+.
  • Install python requirements with pip install -r ./src/requirements.txt.
• The jq utility (that you can find here).
• The xsv program, required by metasplit (sudo pacman -Syu xsv on Arch, not packaged by Debian, but this guide might be useful. If you have cargo installed, you can simply run cargo install xsv).
• Follow the extra installation guide for generanker (namely installing fast-cohen)
• The xls2csv utility (on arch yay -Syu perl-xls2csv)
• A series of R packages that can be installed with Rscript ./src/helper_scripts/install_R_pkgs.R
• Quite a bit of RAM (some steps require > 50 Gb of RAM) and time. Override N_THREADS (with export N_THREADS=...) to run with less threads.

If you have all the requirements, you can simply:

kerblam run <pipeline> --local

Important

The manuscript for this project is also available online in this repository.