Code for "Microenvironment of metastatic site reveals key predictors of PD-1 blockade response in renal cell carcinoma"

This repository contains all the resources needed to reproduce the Tumor-Immunity Differential (TID) score, the discovery of genes associated with anti-tumor treatment outcomes and the assessment of all other existing signatures.

The analyses were developed in R.

To seamlessly reproduce the entire workflow without dependency issues, you can employed the following docker image compressed as "dockerImage_TID.tar".

To make it a docker image and to open a jupyter lab session, please follow this two-step process:

1. Import dockerImage_TID.tar (8Gb) as a docker image.

docker import dockerImage_TID.tar dockerImage_TID

2. Run with the CodeNotebook directory (uncompressed) mounted inside your image:

docker run -it -p 8888:8888 --mount type=bind,src=/[yourCustomedPath]/CodeNotebook,target=/home/jovyan/myData dockerImage_TID

Then, you will have a complete Jupyter Lab software with all the necessary dependencies to reproduce the analyses.

Please, note that due to dependencies between the notebook results, you should follow the numbering to run each notebook. So, run the "0.*", then the "1.*", and so on.

For any questions, you can find my contact information on my orcid page: https://orcid.org/0000-0002-9301-4019