Differential abundance analysis (DAA) for Moonlight2R

This repository contains:

• A folder cptac_data_access/ which includes a script for the access to quantitative proteomic data of tumor and normal samples of different cancer types from CPTAC Data Portal and for data visualization.
• A folder DAA_limma/ which includes a script to perform (DAA) of protein using limma statistical method. More details are provided within each subfolder.
• Two environment configuartion files, namely environment-python.yml and environment-r.yml that should be used for the setup of two virtual environments to run the Python and R scripts included in this repository.

To reproduce the results follow these steps:

• Clone the github repository

git clone https://github.com/ELELAB/DAA_for_Moonlight2R.git

• Create two new environments for Python and R separately- using the environment configuration files already provided in the repository

module load conda
conda env create --prefix ./DAA_py_env -f environment-python.yml
conda env create --prefix ./DAA_r_env -f environment-r.yml

• Activate one environment at time to run the python script for proteomic data access cptac_data_access.py and the R script for DAA analysis limma.R. You can check if the two have environments have been created successfully as follows:

module load conda
conda activate DAA_py_env/

conda deactivate
conda activate DAA_r_env/

• Navigate to the directory cptac_data_access/ to perform proteomic data access

cd cptac_data_access/

• Navigate to the directory DAA_limma/ to perform DAA

cd DAA_limma/